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Browse files- .gitattributes +35 -35
- 1.0.0 +16 -0
- app.py +1165 -1165
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1.0.0
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Requirement already satisfied: openai in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (1.60.1)
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Requirement already satisfied: anyio<5,>=3.5.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (4.8.0)
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Requirement already satisfied: distro<2,>=1.7.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (1.9.0)
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Requirement already satisfied: httpx<1,>=0.23.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (0.27.2)
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Requirement already satisfied: jiter<1,>=0.4.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (0.8.2)
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Requirement already satisfied: pydantic<3,>=1.9.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (2.10.6)
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Requirement already satisfied: sniffio in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (1.3.1)
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Requirement already satisfied: tqdm>4 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (4.67.1)
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Requirement already satisfied: typing-extensions<5,>=4.11 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from openai) (4.12.2)
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Requirement already satisfied: certifi in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from httpx<1,>=0.23.0->openai) (2024.12.14)
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Requirement already satisfied: httpcore==1.* in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from httpx<1,>=0.23.0->openai) (1.0.7)
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Requirement already satisfied: h11<0.15,>=0.13 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)
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Requirement already satisfied: annotated-types>=0.6.0 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)
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Requirement already satisfied: pydantic-core==2.27.2 in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from pydantic<3,>=1.9.0->openai) (2.27.2)
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Requirement already satisfied: colorama in c:\users\adminidiakhoa\appdata\local\programs\python\python312\lib\site-packages (from tqdm>4->openai) (0.4.6)
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app.py
CHANGED
@@ -1,1165 +1,1165 @@
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import os
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import json
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import base64
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import io
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import ast
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import logging
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional, Any
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import streamlit as st
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import spacy
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from scipy.stats import ttest_ind, f_oneway
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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from statsmodels.tsa.seasonal import seasonal_decompose
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from statsmodels.tsa.stattools import adfuller
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from pydantic import BaseModel, Field
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from Bio import Entrez # Ensure BioPython is installed
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from dotenv import load_dotenv
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import requests
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import openai # Updated for OpenAI SDK v1.0.0+
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from openai.error import APIError, RateLimitError, InvalidRequestError
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# ---------------------- Load Environment Variables ---------------------------
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load_dotenv()
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# ---------------------- Logging Configuration ---------------------------
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logging.basicConfig(
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filename='app.log',
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filemode='a',
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format='%(asctime)s - %(levelname)s - %(message)s',
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level=logging.INFO
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)
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logger = logging.getLogger()
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# ---------------------- Streamlit Page Configuration ---------------------------
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st.set_page_config(page_title="AI Clinical Intelligence Hub", layout="wide")
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# ---------------------- Initialize OpenAI SDK ---------------------------
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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PUB_EMAIL = os.getenv("PUB_EMAIL", "")
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if not OPENAI_API_KEY:
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st.error("OpenAI API key must be set as an environment variable (OPENAI_API_KEY).")
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st.stop()
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# Set the OpenAI API key
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openai.api_key = OPENAI_API_KEY
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# ---------------------- Load spaCy Model ---------------------------
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try:
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nlp = spacy.load("en_core_web_sm")
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except OSError:
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# Avoid using Streamlit commands before set_page_config()
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import subprocess
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import sys
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subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
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nlp = spacy.load("en_core_web_sm")
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# ---------------------- Base Classes and Schemas ---------------------------
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class ResearchInput(BaseModel):
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"""Base schema for research tool inputs."""
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data_key: str = Field(..., description="Session state key containing DataFrame")
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columns: Optional[List[str]] = Field(None, description="List of columns to analyze")
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class TemporalAnalysisInput(ResearchInput):
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"""Schema for temporal analysis."""
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time_col: str = Field(..., description="Name of timestamp column")
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value_col: str = Field(..., description="Name of value column to analyze")
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class HypothesisInput(ResearchInput):
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"""Schema for hypothesis testing."""
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group_col: str = Field(..., description="Categorical column defining groups")
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value_col: str = Field(..., description="Numerical column to compare")
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class ModelTrainingInput(ResearchInput):
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"""Schema for model training."""
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target_col: str = Field(..., description="Name of target column")
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class DataAnalyzer(ABC):
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"""Abstract base class for data analysis modules."""
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@abstractmethod
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def invoke(self, data: pd.DataFrame, **kwargs) -> Dict[str, Any]:
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pass
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# ---------------------- Concrete Analyzer Implementations ---------------------------
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class AdvancedEDA(DataAnalyzer):
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"""Comprehensive Exploratory Data Analysis."""
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def invoke(self, data: pd.DataFrame, **kwargs) -> Dict[str, Any]:
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try:
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analysis = {
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"dimensionality": {
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"rows": len(data),
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"columns": list(data.columns),
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"memory_usage_MB": f"{data.memory_usage().sum() / 1e6:.2f} MB"
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},
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"statistical_profile": data.describe(percentiles=[.25, .5, .75]).to_dict(),
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"temporal_analysis": {
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"date_ranges": {
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col: {
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"min": data[col].min(),
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"max": data[col].max()
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} for col in data.select_dtypes(include='datetime').columns
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}
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},
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"data_quality": {
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"missing_values": data.isnull().sum().to_dict(),
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"duplicates": data.duplicated().sum(),
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"cardinality": {
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col: data[col].nunique() for col in data.columns
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}
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}
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}
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return analysis
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except Exception as e:
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logger.error(f"EDA Failed: {str(e)}")
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return {"error": f"EDA Failed: {str(e)}"}
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class DistributionVisualizer(DataAnalyzer):
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"""Distribution visualizations."""
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def invoke(self, data: pd.DataFrame, columns: List[str], **kwargs) -> str:
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try:
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plt.figure(figsize=(12, 6))
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for i, col in enumerate(columns, 1):
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plt.subplot(1, len(columns), i)
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sns.histplot(data[col], kde=True, stat="density")
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plt.title(f'Distribution of {col}', fontsize=10)
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plt.xticks(fontsize=8)
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plt.yticks(fontsize=8)
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plt.tight_layout()
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=300, bbox_inches='tight')
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plt.close()
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return base64.b64encode(buf.getvalue()).decode()
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except Exception as e:
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logger.error(f"Visualization Error: {str(e)}")
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return f"Visualization Error: {str(e)}"
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-
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class TemporalAnalyzer(DataAnalyzer):
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"""Time series analysis."""
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def invoke(self, data: pd.DataFrame, time_col: str, value_col: str, **kwargs) -> Dict[str, Any]:
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try:
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ts_data = data.set_index(pd.to_datetime(data[time_col]))[value_col]
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decomposition = seasonal_decompose(ts_data, period=365)
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plt.figure(figsize=(12, 8))
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decomposition.plot()
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plt.tight_layout()
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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plt.close()
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plot_data = base64.b64encode(buf.getvalue()).decode()
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-
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stationarity_p_value = adfuller(ts_data)[1]
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return {
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"trend_statistics": {
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"stationarity_p_value": stationarity_p_value,
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"seasonality_strength": float(max(decomposition.seasonal))
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},
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"visualization": plot_data
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}
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except Exception as e:
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logger.error(f"Temporal Analysis Failed: {str(e)}")
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return {"error": f"Temporal Analysis Failed: {str(e)}"}
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class HypothesisTester(DataAnalyzer):
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"""Statistical hypothesis testing."""
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def invoke(self, data: pd.DataFrame, group_col: str, value_col: str, **kwargs) -> Dict[str, Any]:
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try:
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groups = data[group_col].unique()
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if len(groups) < 2:
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return {"error": "Insufficient groups for comparison"}
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group_data = [data[data[group_col] == g][value_col] for g in groups]
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if len(groups) == 2:
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stat, p = ttest_ind(*group_data)
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test_type = "Independent t-test"
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effect_size = self.calculate_cohens_d(group_data[0], group_data[1])
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else:
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stat, p = f_oneway(*group_data)
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test_type = "ANOVA"
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effect_size = None
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return {
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"test_type": test_type,
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"test_statistic": stat,
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"p_value": p,
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"effect_size": effect_size,
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"interpretation": self.interpret_p_value(p)
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}
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except Exception as e:
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logger.error(f"Hypothesis Testing Failed: {str(e)}")
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return {"error": f"Hypothesis Testing Failed: {str(e)}"}
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@staticmethod
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def calculate_cohens_d(x: pd.Series, y: pd.Series) -> Optional[float]:
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"""Calculate Cohen's d for effect size."""
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try:
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mean_diff = abs(x.mean() - y.mean())
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pooled_std = np.sqrt((x.var() + y.var()) / 2)
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return mean_diff / pooled_std
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except Exception as e:
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logger.error(f"Error calculating Cohen's d: {str(e)}")
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return None
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@staticmethod
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def interpret_p_value(p: float) -> str:
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"""Interpret the p-value."""
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if p < 0.001:
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return "Very strong evidence against H0"
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elif p < 0.01:
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return "Strong evidence against H0"
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elif p < 0.05:
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return "Evidence against H0"
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elif p < 0.1:
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return "Weak evidence against H0"
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else:
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return "No significant evidence against H0"
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class LogisticRegressionTrainer(DataAnalyzer):
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"""Logistic Regression Model Trainer."""
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238 |
-
def invoke(self, data: pd.DataFrame, target_col: str, columns: List[str], **kwargs) -> Dict[str, Any]:
|
239 |
-
try:
|
240 |
-
X = data[columns]
|
241 |
-
y = data[target_col]
|
242 |
-
X_train, X_test, y_train, y_test = train_test_split(
|
243 |
-
X, y, test_size=0.2, random_state=42
|
244 |
-
)
|
245 |
-
model = LogisticRegression(max_iter=1000)
|
246 |
-
model.fit(X_train, y_train)
|
247 |
-
y_pred = model.predict(X_test)
|
248 |
-
accuracy = accuracy_score(y_test, y_pred)
|
249 |
-
return {
|
250 |
-
"model_type": "Logistic Regression",
|
251 |
-
"accuracy": accuracy,
|
252 |
-
"model_params": model.get_params()
|
253 |
-
}
|
254 |
-
except Exception as e:
|
255 |
-
logger.error(f"Logistic Regression Model Error: {str(e)}")
|
256 |
-
return {"error": f"Logistic Regression Model Error: {str(e)}"}
|
257 |
-
|
258 |
-
# ---------------------- Business Logic Layer ---------------------------
|
259 |
-
|
260 |
-
class ClinicalRule(BaseModel):
|
261 |
-
"""Defines a clinical rule."""
|
262 |
-
name: str
|
263 |
-
condition: str
|
264 |
-
action: str
|
265 |
-
severity: str # low, medium, or high
|
266 |
-
|
267 |
-
class ClinicalRulesEngine:
|
268 |
-
"""Executes rules against patient data."""
|
269 |
-
def __init__(self):
|
270 |
-
self.rules: Dict[str, ClinicalRule] = {}
|
271 |
-
|
272 |
-
def add_rule(self, rule: ClinicalRule):
|
273 |
-
self.rules[rule.name] = rule
|
274 |
-
|
275 |
-
def execute_rules(self, data: pd.DataFrame) -> Dict[str, Any]:
|
276 |
-
results = {}
|
277 |
-
for rule_name, rule in self.rules.items():
|
278 |
-
try:
|
279 |
-
# Using safe_eval instead of eval for security
|
280 |
-
rule_matched = self.safe_eval(rule.condition, {"df": data})
|
281 |
-
results[rule_name] = {
|
282 |
-
"rule_matched": rule_matched,
|
283 |
-
"action": rule.action if rule_matched else None,
|
284 |
-
"severity": rule.severity if rule_matched else None
|
285 |
-
}
|
286 |
-
except Exception as e:
|
287 |
-
logger.error(f"Error executing rule '{rule_name}': {str(e)}")
|
288 |
-
results[rule_name] = {
|
289 |
-
"rule_matched": False,
|
290 |
-
"error": str(e),
|
291 |
-
"severity": None
|
292 |
-
}
|
293 |
-
return results
|
294 |
-
|
295 |
-
@staticmethod
|
296 |
-
def safe_eval(expr, variables):
|
297 |
-
"""
|
298 |
-
Safely evaluate an expression using AST parsing.
|
299 |
-
Only allows certain node types to prevent execution of arbitrary code.
|
300 |
-
"""
|
301 |
-
allowed_nodes = (
|
302 |
-
ast.Expression, ast.BoolOp, ast.BinOp, ast.UnaryOp, ast.Compare,
|
303 |
-
ast.Call, ast.Name, ast.Load, ast.Constant, ast.Num, ast.Str,
|
304 |
-
ast.List, ast.Tuple, ast.Dict
|
305 |
-
)
|
306 |
-
try:
|
307 |
-
node = ast.parse(expr, mode='eval')
|
308 |
-
for subnode in ast.walk(node):
|
309 |
-
if not isinstance(subnode, allowed_nodes):
|
310 |
-
raise ValueError(f"Unsupported expression: {expr}")
|
311 |
-
return eval(compile(node, '<string>', mode='eval'), {"__builtins__": None}, variables)
|
312 |
-
except Exception as e:
|
313 |
-
logger.error(f"safe_eval error: {str(e)}")
|
314 |
-
raise ValueError(f"Invalid expression: {e}")
|
315 |
-
|
316 |
-
class ClinicalKPI(BaseModel):
|
317 |
-
"""Define a clinical KPI."""
|
318 |
-
name: str
|
319 |
-
calculation: str
|
320 |
-
threshold: Optional[float] = None
|
321 |
-
|
322 |
-
class ClinicalKPIMonitoring:
|
323 |
-
"""Calculates KPIs based on data."""
|
324 |
-
def __init__(self):
|
325 |
-
self.kpis: Dict[str, ClinicalKPI] = {}
|
326 |
-
|
327 |
-
def add_kpi(self, kpi: ClinicalKPI):
|
328 |
-
self.kpis[kpi.name] = kpi
|
329 |
-
|
330 |
-
def calculate_kpis(self, data: pd.DataFrame) -> Dict[str, Any]:
|
331 |
-
results = {}
|
332 |
-
for kpi_name, kpi in self.kpis.items():
|
333 |
-
try:
|
334 |
-
# Using safe_eval instead of eval for security
|
335 |
-
kpi_value = self.safe_eval(kpi.calculation, {"df": data})
|
336 |
-
status = self.evaluate_threshold(kpi_value, kpi.threshold)
|
337 |
-
results[kpi_name] = {
|
338 |
-
"value": kpi_value,
|
339 |
-
"threshold": kpi.threshold,
|
340 |
-
"status": status
|
341 |
-
}
|
342 |
-
except Exception as e:
|
343 |
-
logger.error(f"Error calculating KPI '{kpi_name}': {str(e)}")
|
344 |
-
results[kpi_name] = {"error": str(e)}
|
345 |
-
return results
|
346 |
-
|
347 |
-
@staticmethod
|
348 |
-
def evaluate_threshold(value: Any, threshold: Optional[float]) -> Optional[str]:
|
349 |
-
if threshold is None:
|
350 |
-
return None
|
351 |
-
try:
|
352 |
-
return "Above Threshold" if value > threshold else "Below Threshold"
|
353 |
-
except TypeError:
|
354 |
-
return "Threshold Evaluation Not Applicable"
|
355 |
-
|
356 |
-
@staticmethod
|
357 |
-
def safe_eval(expr, variables):
|
358 |
-
"""
|
359 |
-
Safely evaluate an expression using AST parsing.
|
360 |
-
Only allows certain node types to prevent execution of arbitrary code.
|
361 |
-
"""
|
362 |
-
allowed_nodes = (
|
363 |
-
ast.Expression, ast.BoolOp, ast.BinOp, ast.UnaryOp, ast.Compare,
|
364 |
-
ast.Call, ast.Name, ast.Load, ast.Constant, ast.Num, ast.Str,
|
365 |
-
ast.List, ast.Tuple, ast.Dict
|
366 |
-
)
|
367 |
-
try:
|
368 |
-
node = ast.parse(expr, mode='eval')
|
369 |
-
for subnode in ast.walk(node):
|
370 |
-
if not isinstance(subnode, allowed_nodes):
|
371 |
-
raise ValueError(f"Unsupported expression: {expr}")
|
372 |
-
return eval(compile(node, '<string>', mode='eval'), {"__builtins__": None}, variables)
|
373 |
-
except Exception as e:
|
374 |
-
logger.error(f"safe_eval error: {str(e)}")
|
375 |
-
raise ValueError(f"Invalid expression: {e}")
|
376 |
-
|
377 |
-
class DiagnosisSupport(ABC):
|
378 |
-
"""Abstract class for implementing clinical diagnoses."""
|
379 |
-
@abstractmethod
|
380 |
-
def diagnose(
|
381 |
-
self,
|
382 |
-
data: pd.DataFrame,
|
383 |
-
target_col: str,
|
384 |
-
columns: List[str],
|
385 |
-
diagnosis_key: str = "diagnosis",
|
386 |
-
**kwargs
|
387 |
-
) -> pd.DataFrame:
|
388 |
-
pass
|
389 |
-
|
390 |
-
class SimpleDiagnosis(DiagnosisSupport):
|
391 |
-
"""Provides a simple diagnosis example, based on the Logistic regression model."""
|
392 |
-
def __init__(self):
|
393 |
-
self.model_trainer: LogisticRegressionTrainer = LogisticRegressionTrainer()
|
394 |
-
|
395 |
-
def diagnose(
|
396 |
-
self,
|
397 |
-
data: pd.DataFrame,
|
398 |
-
target_col: str,
|
399 |
-
columns: List[str],
|
400 |
-
diagnosis_key: str = "diagnosis",
|
401 |
-
**kwargs
|
402 |
-
) -> pd.DataFrame:
|
403 |
-
try:
|
404 |
-
result = self.model_trainer.invoke(data, target_col=target_col, columns=columns)
|
405 |
-
if "accuracy" in result:
|
406 |
-
return pd.DataFrame({
|
407 |
-
diagnosis_key: [f"Model Accuracy: {result['accuracy']:.2%}"],
|
408 |
-
"model": [result["model_type"]]
|
409 |
-
})
|
410 |
-
else:
|
411 |
-
return pd.DataFrame({
|
412 |
-
diagnosis_key: [f"Diagnosis failed: {result.get('error', 'Unknown error')}"]
|
413 |
-
})
|
414 |
-
except Exception as e:
|
415 |
-
logger.error(f"Error during diagnosis: {str(e)}")
|
416 |
-
return pd.DataFrame({
|
417 |
-
diagnosis_key: [f"Error during diagnosis: {e}"]
|
418 |
-
})
|
419 |
-
|
420 |
-
class TreatmentRecommendation(ABC):
|
421 |
-
"""Abstract class for treatment recommendations."""
|
422 |
-
@abstractmethod
|
423 |
-
def recommend(
|
424 |
-
self,
|
425 |
-
data: pd.DataFrame,
|
426 |
-
condition_col: str,
|
427 |
-
treatment_col: str,
|
428 |
-
recommendation_key: str = "recommendation",
|
429 |
-
**kwargs
|
430 |
-
) -> pd.DataFrame:
|
431 |
-
pass
|
432 |
-
|
433 |
-
class BasicTreatmentRecommendation(TreatmentRecommendation):
|
434 |
-
"""A placeholder class for basic treatment recommendations."""
|
435 |
-
def recommend(
|
436 |
-
self,
|
437 |
-
data: pd.DataFrame,
|
438 |
-
condition_col: str,
|
439 |
-
treatment_col: str,
|
440 |
-
recommendation_key: str = "recommendation",
|
441 |
-
**kwargs
|
442 |
-
) -> pd.DataFrame:
|
443 |
-
if condition_col not in data.columns or treatment_col not in data.columns:
|
444 |
-
logger.warning(f"Condition or Treatment columns not found: {condition_col}, {treatment_col}")
|
445 |
-
return pd.DataFrame({
|
446 |
-
recommendation_key: ["Condition or Treatment columns not found!"]
|
447 |
-
})
|
448 |
-
|
449 |
-
treatment = data[data[condition_col] == "High"][treatment_col].to_list()
|
450 |
-
if treatment:
|
451 |
-
return pd.DataFrame({
|
452 |
-
recommendation_key: [f"Treatment recommended for High risk patients: {treatment}"]
|
453 |
-
})
|
454 |
-
else:
|
455 |
-
return pd.DataFrame({
|
456 |
-
recommendation_key: ["No treatment recommendation found!"]
|
457 |
-
})
|
458 |
-
|
459 |
-
# ---------------------- Medical Knowledge Base ---------------------------
|
460 |
-
|
461 |
-
class MedicalKnowledgeBase(ABC):
|
462 |
-
"""Abstract class for Medical Knowledge."""
|
463 |
-
@abstractmethod
|
464 |
-
def search_medical_info(self, query: str, pub_email: str = "") -> str:
|
465 |
-
pass
|
466 |
-
|
467 |
-
class SimpleMedicalKnowledge(MedicalKnowledgeBase):
|
468 |
-
"""Enhanced Medical Knowledge Class using OpenAI GPT-4."""
|
469 |
-
def __init__(self, nlp_model):
|
470 |
-
self.nlp = nlp_model # Using the loaded spaCy model
|
471 |
-
|
472 |
-
def search_medical_info(self, query: str, pub_email: str = "") -> str:
|
473 |
-
"""
|
474 |
-
Uses OpenAI's GPT-4 to fetch medical information based on the user's query.
|
475 |
-
"""
|
476 |
-
logger.info(f"Received medical query: {query}")
|
477 |
-
try:
|
478 |
-
# Preprocess the query (e.g., entity recognition)
|
479 |
-
doc = self.nlp(query.lower())
|
480 |
-
entities = [ent.text for ent in doc.ents]
|
481 |
-
processed_query = " ".join(entities) if entities else query.lower()
|
482 |
-
|
483 |
-
logger.info(f"Processed query: {processed_query}")
|
484 |
-
|
485 |
-
# Create a prompt for GPT-4
|
486 |
-
prompt = f"""
|
487 |
-
You are a medical assistant. Provide a comprehensive and accurate response to the following medical query:
|
488 |
-
|
489 |
-
Query: {processed_query}
|
490 |
-
|
491 |
-
Please ensure the information is clear, concise, and evidence-based.
|
492 |
-
"""
|
493 |
-
|
494 |
-
# Make the API request to OpenAI GPT-4
|
495 |
-
response = openai.ChatCompletion.create(
|
496 |
-
model="gpt-4",
|
497 |
-
messages=[
|
498 |
-
{"role": "system", "content": "You are a helpful medical assistant."},
|
499 |
-
{"role": "user", "content": prompt}
|
500 |
-
],
|
501 |
-
max_tokens=500,
|
502 |
-
temperature=0.7,
|
503 |
-
)
|
504 |
-
|
505 |
-
# Extract the answer from the response
|
506 |
-
answer = response.choices[0].message['content'].strip()
|
507 |
-
|
508 |
-
logger.info("Successfully retrieved data from OpenAI GPT-4.")
|
509 |
-
|
510 |
-
# Fetch PubMed abstract related to the query
|
511 |
-
pubmed_abstract = self.fetch_pubmed_abstract(processed_query, pub_email)
|
512 |
-
|
513 |
-
# Format the response
|
514 |
-
return f"**Based on your query:** {answer}\n\n**PubMed Abstract:**\n\n{pubmed_abstract}"
|
515 |
-
|
516 |
-
except RateLimitError as e:
|
517 |
-
logger.error(f"Rate Limit Exceeded: {str(e)}")
|
518 |
-
return "Rate limit exceeded. Please try again later."
|
519 |
-
except InvalidRequestError as e:
|
520 |
-
logger.error(f"Invalid Request: {str(e)}")
|
521 |
-
return f"Invalid request: {str(e)}"
|
522 |
-
except APIError as e:
|
523 |
-
logger.error(f"OpenAI API Error: {str(e)}")
|
524 |
-
return f"OpenAI API Error: {str(e)}"
|
525 |
-
except Exception as e:
|
526 |
-
logger.error(f"Medical Knowledge Search Failed: {str(e)}")
|
527 |
-
return f"Medical Knowledge Search Failed: {str(e)}"
|
528 |
-
|
529 |
-
def fetch_pubmed_abstract(self, query: str, email: str) -> str:
|
530 |
-
"""
|
531 |
-
Searches PubMed for abstracts related to the query.
|
532 |
-
"""
|
533 |
-
try:
|
534 |
-
if not email:
|
535 |
-
logger.warning("PubMed abstract retrieval skipped: Email not provided.")
|
536 |
-
return "No PubMed abstract available: Email not provided."
|
537 |
-
|
538 |
-
Entrez.email = email
|
539 |
-
handle = Entrez.esearch(db="pubmed", term=query, retmax=1, sort='relevance')
|
540 |
-
record = Entrez.read(handle)
|
541 |
-
handle.close()
|
542 |
-
logger.info(f"PubMed search for query '{query}' returned IDs: {record['IdList']}")
|
543 |
-
|
544 |
-
if record["IdList"]:
|
545 |
-
handle = Entrez.efetch(db="pubmed", id=record["IdList"][0], rettype="abstract", retmode="text")
|
546 |
-
abstract = handle.read()
|
547 |
-
handle.close()
|
548 |
-
logger.info(f"Fetched PubMed abstract for ID {record['IdList'][0]}")
|
549 |
-
return abstract
|
550 |
-
else:
|
551 |
-
logger.info(f"No PubMed abstracts found for query '{query}'.")
|
552 |
-
return "No abstracts found for this query on PubMed."
|
553 |
-
except Exception as e:
|
554 |
-
logger.error(f"Error searching PubMed: {e}")
|
555 |
-
return f"Error searching PubMed: {e}"
|
556 |
-
|
557 |
-
# ---------------------- Forecasting Engine ---------------------------
|
558 |
-
|
559 |
-
class ForecastingEngine(ABC):
|
560 |
-
"""Abstract class for forecasting."""
|
561 |
-
@abstractmethod
|
562 |
-
def predict(self, data: pd.DataFrame, **kwargs) -> pd.DataFrame:
|
563 |
-
pass
|
564 |
-
|
565 |
-
class SimpleForecasting(ForecastingEngine):
|
566 |
-
"""Simple forecasting engine."""
|
567 |
-
def predict(self, data: pd.DataFrame, period: int = 7, **kwargs) -> pd.DataFrame:
|
568 |
-
# Placeholder for actual forecasting logic
|
569 |
-
return pd.DataFrame({"forecast": [f"Forecast for the next {period} days"]})
|
570 |
-
|
571 |
-
# ---------------------- Insights and Reporting Layer ---------------------------
|
572 |
-
|
573 |
-
class AutomatedInsights:
|
574 |
-
"""Generates automated insights based on selected analyses."""
|
575 |
-
def __init__(self):
|
576 |
-
self.analyses: Dict[str, DataAnalyzer] = {
|
577 |
-
"EDA": AdvancedEDA(),
|
578 |
-
"temporal": TemporalAnalyzer(),
|
579 |
-
"distribution": DistributionVisualizer(),
|
580 |
-
"hypothesis": HypothesisTester(),
|
581 |
-
"model": LogisticRegressionTrainer()
|
582 |
-
}
|
583 |
-
|
584 |
-
def generate_insights(self, data: pd.DataFrame, analysis_names: List[str], **kwargs) -> Dict[str, Any]:
|
585 |
-
results = {}
|
586 |
-
for name in analysis_names:
|
587 |
-
analyzer = self.analyses.get(name)
|
588 |
-
if analyzer:
|
589 |
-
try:
|
590 |
-
results[name] = analyzer.invoke(data=data, **kwargs)
|
591 |
-
except Exception as e:
|
592 |
-
logger.error(f"Error in analysis '{name}': {str(e)}")
|
593 |
-
results[name] = {"error": str(e)}
|
594 |
-
else:
|
595 |
-
logger.warning(f"Analysis '{name}' not found.")
|
596 |
-
results[name] = {"error": "Analysis not found"}
|
597 |
-
return results
|
598 |
-
|
599 |
-
class Dashboard:
|
600 |
-
"""Handles the creation and display of the dashboard."""
|
601 |
-
def __init__(self):
|
602 |
-
self.layout: Dict[str, str] = {}
|
603 |
-
|
604 |
-
def add_visualisation(self, vis_name: str, vis_type: str):
|
605 |
-
self.layout[vis_name] = vis_type
|
606 |
-
|
607 |
-
def display_dashboard(self, data_dict: Dict[str, pd.DataFrame]):
|
608 |
-
st.header("Dashboard")
|
609 |
-
for vis_name, vis_type in self.layout.items():
|
610 |
-
st.subheader(vis_name)
|
611 |
-
df = data_dict.get(vis_name)
|
612 |
-
if df is not None:
|
613 |
-
if vis_type == "table":
|
614 |
-
st.table(df)
|
615 |
-
elif vis_type == "plot":
|
616 |
-
if len(df.columns) > 1:
|
617 |
-
fig = plt.figure()
|
618 |
-
sns.lineplot(data=df)
|
619 |
-
st.pyplot(fig)
|
620 |
-
else:
|
621 |
-
st.write("Please select a DataFrame with more than 1 column for plotting.")
|
622 |
-
else:
|
623 |
-
st.write("Data Not Found")
|
624 |
-
|
625 |
-
class AutomatedReports:
|
626 |
-
"""Manages automated report definitions and generation."""
|
627 |
-
def __init__(self):
|
628 |
-
self.report_definitions: Dict[str, str] = {}
|
629 |
-
|
630 |
-
def create_report_definition(self, report_name: str, definition: str):
|
631 |
-
self.report_definitions[report_name] = definition
|
632 |
-
|
633 |
-
def generate_report(self, report_name: str, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
|
634 |
-
if report_name not in self.report_definitions:
|
635 |
-
return {"error": "Report name not found"}
|
636 |
-
report_content = {
|
637 |
-
"Report Name": report_name,
|
638 |
-
"Report Definition": self.report_definitions[report_name],
|
639 |
-
"Data": {df_name: df.to_dict() for df_name, df in data.items()}
|
640 |
-
}
|
641 |
-
return report_content
|
642 |
-
|
643 |
-
# ---------------------- Data Acquisition Layer ---------------------------
|
644 |
-
|
645 |
-
class DataSource(ABC):
|
646 |
-
"""Base class for data sources."""
|
647 |
-
@abstractmethod
|
648 |
-
def connect(self) -> None:
|
649 |
-
"""Connect to the data source."""
|
650 |
-
pass
|
651 |
-
|
652 |
-
@abstractmethod
|
653 |
-
def fetch_data(self, query: str, **kwargs) -> pd.DataFrame:
|
654 |
-
"""Fetch the data based on a specific query."""
|
655 |
-
pass
|
656 |
-
|
657 |
-
class CSVDataSource(DataSource):
|
658 |
-
"""Data source for CSV files."""
|
659 |
-
def __init__(self, file_path: io.BytesIO):
|
660 |
-
self.file_path = file_path
|
661 |
-
self.data: Optional[pd.DataFrame] = None
|
662 |
-
|
663 |
-
def connect(self):
|
664 |
-
self.data = pd.read_csv(self.file_path)
|
665 |
-
|
666 |
-
def fetch_data(self, query: str = None, **kwargs) -> pd.DataFrame:
|
667 |
-
if self.data is None:
|
668 |
-
raise Exception("No connection is made, call connect()")
|
669 |
-
return self.data
|
670 |
-
|
671 |
-
class DatabaseSource(DataSource):
|
672 |
-
"""Data source for SQL Databases."""
|
673 |
-
def __init__(self, connection_string: str, database_type: str):
|
674 |
-
self.connection_string = connection_string
|
675 |
-
self.database_type = database_type.lower()
|
676 |
-
self.connection = None
|
677 |
-
|
678 |
-
def connect(self):
|
679 |
-
if self.database_type == "sql":
|
680 |
-
# Placeholder for actual SQL connection logic
|
681 |
-
self.connection = "Connected to SQL Database"
|
682 |
-
else:
|
683 |
-
raise Exception(f"Database type '{self.database_type}' is not supported.")
|
684 |
-
|
685 |
-
def fetch_data(self, query: str, **kwargs) -> pd.DataFrame:
|
686 |
-
if self.connection is None:
|
687 |
-
raise Exception("No connection is made, call connect()")
|
688 |
-
# Placeholder for data fetching logic
|
689 |
-
return pd.DataFrame({"result": [f"Fetched data based on query: {query}"]})
|
690 |
-
|
691 |
-
class DataIngestion:
|
692 |
-
"""Handles data ingestion from various sources."""
|
693 |
-
def __init__(self):
|
694 |
-
self.sources: Dict[str, DataSource] = {}
|
695 |
-
|
696 |
-
def add_source(self, source_name: str, source: DataSource):
|
697 |
-
self.sources[source_name] = source
|
698 |
-
|
699 |
-
def ingest_data(self, source_name: str, query: str = None, **kwargs) -> pd.DataFrame:
|
700 |
-
if source_name not in self.sources:
|
701 |
-
raise Exception(f"Source '{source_name}' not found.")
|
702 |
-
source = self.sources[source_name]
|
703 |
-
source.connect()
|
704 |
-
return source.fetch_data(query, **kwargs)
|
705 |
-
|
706 |
-
class DataModel(BaseModel):
|
707 |
-
"""Defines a data model."""
|
708 |
-
name: str
|
709 |
-
kpis: List[str] = Field(default_factory=list)
|
710 |
-
dimensions: List[str] = Field(default_factory=list)
|
711 |
-
custom_calculations: Optional[Dict[str, str]] = None
|
712 |
-
relations: Optional[Dict[str, str]] = None # Example: {"table1": "table2"}
|
713 |
-
|
714 |
-
def to_json(self) -> str:
|
715 |
-
return json.dumps(self.dict())
|
716 |
-
|
717 |
-
@staticmethod
|
718 |
-
def from_json(json_str: str) -> 'DataModel':
|
719 |
-
return DataModel(**json.loads(json_str))
|
720 |
-
|
721 |
-
class DataModelling:
|
722 |
-
"""Manages data models."""
|
723 |
-
def __init__(self):
|
724 |
-
self.models: Dict[str, DataModel] = {}
|
725 |
-
|
726 |
-
def add_model(self, model: DataModel):
|
727 |
-
self.models[model.name] = model
|
728 |
-
|
729 |
-
def get_model(self, model_name: str) -> DataModel:
|
730 |
-
if model_name not in self.models:
|
731 |
-
raise Exception(f"Model '{model_name}' not found.")
|
732 |
-
return self.models[model_name]
|
733 |
-
|
734 |
-
# ---------------------- Main Streamlit Application ---------------------------
|
735 |
-
|
736 |
-
def main():
|
737 |
-
"""Main function to run the Streamlit app."""
|
738 |
-
st.title("🏥 AI-Powered Clinical Intelligence Hub")
|
739 |
-
|
740 |
-
# Initialize Session State
|
741 |
-
initialize_session_state()
|
742 |
-
|
743 |
-
# Sidebar for Data Management
|
744 |
-
with st.sidebar:
|
745 |
-
data_management_section()
|
746 |
-
|
747 |
-
# Main Content
|
748 |
-
if st.session_state.data:
|
749 |
-
col1, col2 = st.columns([1, 3])
|
750 |
-
|
751 |
-
with col1:
|
752 |
-
dataset_metadata_section()
|
753 |
-
|
754 |
-
with col2:
|
755 |
-
main_tabs_section()
|
756 |
-
|
757 |
-
def initialize_session_state():
|
758 |
-
"""Initialize necessary components in Streamlit's session state."""
|
759 |
-
if 'data' not in st.session_state:
|
760 |
-
st.session_state.data = {} # Store pd.DataFrame under a name
|
761 |
-
if 'data_ingestion' not in st.session_state:
|
762 |
-
st.session_state.data_ingestion = DataIngestion()
|
763 |
-
if 'data_modelling' not in st.session_state:
|
764 |
-
st.session_state.data_modelling = DataModelling()
|
765 |
-
if 'clinical_rules' not in st.session_state:
|
766 |
-
st.session_state.clinical_rules = ClinicalRulesEngine()
|
767 |
-
if 'kpi_monitoring' not in st.session_state:
|
768 |
-
st.session_state.kpi_monitoring = ClinicalKPIMonitoring()
|
769 |
-
if 'forecasting_engine' not in st.session_state:
|
770 |
-
st.session_state.forecasting_engine = SimpleForecasting()
|
771 |
-
if 'automated_insights' not in st.session_state:
|
772 |
-
st.session_state.automated_insights = AutomatedInsights()
|
773 |
-
if 'dashboard' not in st.session_state:
|
774 |
-
st.session_state.dashboard = Dashboard()
|
775 |
-
if 'automated_reports' not in st.session_state:
|
776 |
-
st.session_state.automated_reports = AutomatedReports()
|
777 |
-
if 'diagnosis_support' not in st.session_state:
|
778 |
-
st.session_state.diagnosis_support = SimpleDiagnosis()
|
779 |
-
if 'treatment_recommendation' not in st.session_state:
|
780 |
-
st.session_state.treatment_recommendation = BasicTreatmentRecommendation()
|
781 |
-
if 'knowledge_base' not in st.session_state:
|
782 |
-
st.session_state.knowledge_base = SimpleMedicalKnowledge(nlp_model=nlp)
|
783 |
-
if 'pub_email' not in st.session_state:
|
784 |
-
st.session_state.pub_email = PUB_EMAIL # Load PUB_EMAIL from environment variables
|
785 |
-
|
786 |
-
def data_management_section():
|
787 |
-
"""Handles the data management section in the sidebar."""
|
788 |
-
st.header("⚙️ Data Management")
|
789 |
-
data_source_selection = st.selectbox("Select Data Source Type", ["CSV", "SQL Database"])
|
790 |
-
|
791 |
-
if data_source_selection == "CSV":
|
792 |
-
handle_csv_upload()
|
793 |
-
elif data_source_selection == "SQL Database":
|
794 |
-
handle_sql_database()
|
795 |
-
|
796 |
-
if st.button("Ingest Data"):
|
797 |
-
ingest_data_action()
|
798 |
-
|
799 |
-
def handle_csv_upload():
|
800 |
-
"""Handles CSV file uploads."""
|
801 |
-
uploaded_file = st.file_uploader("Upload research dataset (CSV)", type=["csv"])
|
802 |
-
if uploaded_file:
|
803 |
-
source_name = st.text_input("Data Source Name")
|
804 |
-
if source_name:
|
805 |
-
try:
|
806 |
-
csv_source = CSVDataSource(file_path=uploaded_file)
|
807 |
-
st.session_state.data_ingestion.add_source(source_name, csv_source)
|
808 |
-
st.success(f"Uploaded {uploaded_file.name} as '{source_name}'.")
|
809 |
-
except Exception as e:
|
810 |
-
st.error(f"Error loading dataset: {e}")
|
811 |
-
|
812 |
-
def handle_sql_database():
|
813 |
-
"""Handles SQL database connections."""
|
814 |
-
conn_str = st.text_input("Enter connection string for SQL DB")
|
815 |
-
if conn_str:
|
816 |
-
source_name = st.text_input("Data Source Name")
|
817 |
-
if source_name:
|
818 |
-
try:
|
819 |
-
sql_source = DatabaseSource(connection_string=conn_str, database_type="sql")
|
820 |
-
st.session_state.data_ingestion.add_source(source_name, sql_source)
|
821 |
-
st.success(f"Added SQL DB Source '{source_name}'.")
|
822 |
-
except Exception as e:
|
823 |
-
st.error(f"Error loading database source: {e}")
|
824 |
-
|
825 |
-
def ingest_data_action():
|
826 |
-
"""Performs data ingestion from the selected source."""
|
827 |
-
if st.session_state.data_ingestion.sources:
|
828 |
-
source_name_to_fetch = st.selectbox("Select Data Source to Ingest", list(st.session_state.data_ingestion.sources.keys()))
|
829 |
-
query = st.text_area("Optional Query to Fetch data")
|
830 |
-
if source_name_to_fetch:
|
831 |
-
with st.spinner("Ingesting data..."):
|
832 |
-
try:
|
833 |
-
data = st.session_state.data_ingestion.ingest_data(source_name_to_fetch, query)
|
834 |
-
st.session_state.data[source_name_to_fetch] = data
|
835 |
-
st.success(f"Ingested data from '{source_name_to_fetch}'.")
|
836 |
-
except Exception as e:
|
837 |
-
st.error(f"Ingestion failed: {e}")
|
838 |
-
else:
|
839 |
-
st.error("No data source added. Please add a data source.")
|
840 |
-
|
841 |
-
def dataset_metadata_section():
|
842 |
-
"""Displays metadata for the selected dataset."""
|
843 |
-
st.subheader("Dataset Metadata")
|
844 |
-
data_source_keys = list(st.session_state.data.keys())
|
845 |
-
selected_data_key = st.selectbox("Select Dataset", data_source_keys)
|
846 |
-
|
847 |
-
if selected_data_key:
|
848 |
-
data = st.session_state.data[selected_data_key]
|
849 |
-
metadata = {
|
850 |
-
"Variables": list(data.columns),
|
851 |
-
"Time Range": {
|
852 |
-
col: {
|
853 |
-
"min": data[col].min(),
|
854 |
-
"max": data[col].max()
|
855 |
-
} for col in data.select_dtypes(include='datetime').columns
|
856 |
-
},
|
857 |
-
"Size": f"{data.memory_usage().sum() / 1e6:.2f} MB"
|
858 |
-
}
|
859 |
-
st.json(metadata)
|
860 |
-
# Store the selected dataset key in session state for use in analysis
|
861 |
-
st.session_state.selected_data_key = selected_data_key
|
862 |
-
|
863 |
-
def main_tabs_section():
|
864 |
-
"""Creates and manages the main tabs in the application."""
|
865 |
-
analysis_tab, clinical_logic_tab, insights_tab, reports_tab, knowledge_tab = st.tabs([
|
866 |
-
"Data Analysis",
|
867 |
-
"Clinical Logic",
|
868 |
-
"Insights",
|
869 |
-
"Reports",
|
870 |
-
"Medical Knowledge"
|
871 |
-
])
|
872 |
-
|
873 |
-
with analysis_tab:
|
874 |
-
data_analysis_section()
|
875 |
-
|
876 |
-
with clinical_logic_tab:
|
877 |
-
clinical_logic_section()
|
878 |
-
|
879 |
-
with insights_tab:
|
880 |
-
insights_section()
|
881 |
-
|
882 |
-
with reports_tab:
|
883 |
-
reports_section()
|
884 |
-
|
885 |
-
with knowledge_tab:
|
886 |
-
medical_knowledge_section()
|
887 |
-
|
888 |
-
def data_analysis_section():
|
889 |
-
"""Handles the Data Analysis tab."""
|
890 |
-
selected_data_key = st.session_state.get('selected_data_key', None)
|
891 |
-
if not selected_data_key:
|
892 |
-
st.warning("Please select a dataset from the metadata section.")
|
893 |
-
return
|
894 |
-
|
895 |
-
data = st.session_state.data[selected_data_key]
|
896 |
-
analysis_type = st.selectbox("Select Analysis Mode", [
|
897 |
-
"Exploratory Data Analysis",
|
898 |
-
"Temporal Pattern Analysis",
|
899 |
-
"Comparative Statistics",
|
900 |
-
"Distribution Analysis",
|
901 |
-
"Train Logistic Regression Model"
|
902 |
-
])
|
903 |
-
|
904 |
-
if analysis_type == "Exploratory Data Analysis":
|
905 |
-
perform_eda(data)
|
906 |
-
elif analysis_type == "Temporal Pattern Analysis":
|
907 |
-
perform_temporal_analysis(data)
|
908 |
-
elif analysis_type == "Comparative Statistics":
|
909 |
-
perform_comparative_statistics(data)
|
910 |
-
elif analysis_type == "Distribution Analysis":
|
911 |
-
perform_distribution_analysis(data)
|
912 |
-
elif analysis_type == "Train Logistic Regression Model":
|
913 |
-
perform_logistic_regression_training(data)
|
914 |
-
|
915 |
-
def perform_eda(data: pd.DataFrame):
|
916 |
-
"""Performs Exploratory Data Analysis."""
|
917 |
-
analyzer = AdvancedEDA()
|
918 |
-
eda_result = analyzer.invoke(data=data)
|
919 |
-
st.subheader("Data Quality Report")
|
920 |
-
st.json(eda_result)
|
921 |
-
|
922 |
-
def perform_temporal_analysis(data: pd.DataFrame):
|
923 |
-
"""Performs Temporal Pattern Analysis."""
|
924 |
-
time_cols = data.select_dtypes(include='datetime').columns
|
925 |
-
num_cols = data.select_dtypes(include=np.number).columns
|
926 |
-
|
927 |
-
if len(time_cols) == 0:
|
928 |
-
st.warning("No datetime columns available for temporal analysis.")
|
929 |
-
return
|
930 |
-
|
931 |
-
time_col = st.selectbox("Select Temporal Variable", time_cols)
|
932 |
-
value_col = st.selectbox("Select Analysis Variable", num_cols)
|
933 |
-
|
934 |
-
if time_col and value_col:
|
935 |
-
analyzer = TemporalAnalyzer()
|
936 |
-
result = analyzer.invoke(data=data, time_col=time_col, value_col=value_col)
|
937 |
-
if "visualization" in result and result["visualization"]:
|
938 |
-
st.image(f"data:image/png;base64,{result['visualization']}", use_column_width=True)
|
939 |
-
st.json(result)
|
940 |
-
|
941 |
-
def perform_comparative_statistics(data: pd.DataFrame):
|
942 |
-
"""Performs Comparative Statistics."""
|
943 |
-
categorical_cols = data.select_dtypes(include=['category', 'object']).columns
|
944 |
-
numeric_cols = data.select_dtypes(include=np.number).columns
|
945 |
-
|
946 |
-
if len(categorical_cols) == 0:
|
947 |
-
st.warning("No categorical columns available for hypothesis testing.")
|
948 |
-
return
|
949 |
-
|
950 |
-
if len(numeric_cols) == 0:
|
951 |
-
st.warning("No numerical columns available for hypothesis testing.")
|
952 |
-
return
|
953 |
-
|
954 |
-
group_col = st.selectbox("Select Grouping Variable", categorical_cols)
|
955 |
-
value_col = st.selectbox("Select Metric Variable", numeric_cols)
|
956 |
-
|
957 |
-
if group_col and value_col:
|
958 |
-
analyzer = HypothesisTester()
|
959 |
-
result = analyzer.invoke(data=data, group_col=group_col, value_col=value_col)
|
960 |
-
st.subheader("Statistical Test Results")
|
961 |
-
st.json(result)
|
962 |
-
|
963 |
-
def perform_distribution_analysis(data: pd.DataFrame):
|
964 |
-
"""Performs Distribution Analysis."""
|
965 |
-
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
966 |
-
selected_cols = st.multiselect("Select Variables for Distribution Analysis", numeric_cols)
|
967 |
-
|
968 |
-
if selected_cols:
|
969 |
-
analyzer = DistributionVisualizer()
|
970 |
-
img_data = analyzer.invoke(data=data, columns=selected_cols)
|
971 |
-
if not img_data.startswith("Visualization Error"):
|
972 |
-
st.image(f"data:image/png;base64,{img_data}", use_column_width=True)
|
973 |
-
else:
|
974 |
-
st.error(img_data)
|
975 |
-
else:
|
976 |
-
st.info("Please select at least one numerical column to visualize.")
|
977 |
-
|
978 |
-
def perform_logistic_regression_training(data: pd.DataFrame):
|
979 |
-
"""Trains a Logistic Regression model."""
|
980 |
-
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
981 |
-
target_col = st.selectbox("Select Target Variable", data.columns.tolist())
|
982 |
-
selected_cols = st.multiselect("Select Feature Variables", numeric_cols)
|
983 |
-
|
984 |
-
if selected_cols and target_col:
|
985 |
-
analyzer = LogisticRegressionTrainer()
|
986 |
-
result = analyzer.invoke(data=data, target_col=target_col, columns=selected_cols)
|
987 |
-
st.subheader("Logistic Regression Model Results")
|
988 |
-
st.json(result)
|
989 |
-
else:
|
990 |
-
st.warning("Please select both target and feature variables for model training.")
|
991 |
-
|
992 |
-
def clinical_logic_section():
|
993 |
-
"""Handles the Clinical Logic tab."""
|
994 |
-
st.header("Clinical Logic")
|
995 |
-
|
996 |
-
# Clinical Rules Management
|
997 |
-
st.subheader("Clinical Rules")
|
998 |
-
rule_name = st.text_input("Enter Rule Name")
|
999 |
-
condition = st.text_area("Enter Rule Condition (use 'df' for DataFrame)",
|
1000 |
-
help="Example: df['blood_pressure'] > 140")
|
1001 |
-
action = st.text_area("Enter Action to be Taken on Rule Match")
|
1002 |
-
severity = st.selectbox("Enter Severity for the Rule", ["low", "medium", "high"])
|
1003 |
-
|
1004 |
-
if st.button("Add Clinical Rule"):
|
1005 |
-
if rule_name and condition and action and severity:
|
1006 |
-
try:
|
1007 |
-
rule = ClinicalRule(
|
1008 |
-
name=rule_name,
|
1009 |
-
condition=condition,
|
1010 |
-
action=action,
|
1011 |
-
severity=severity
|
1012 |
-
)
|
1013 |
-
st.session_state.clinical_rules.add_rule(rule)
|
1014 |
-
st.success("Added Clinical Rule successfully.")
|
1015 |
-
except Exception as e:
|
1016 |
-
st.error(f"Error in rule definition: {e}")
|
1017 |
-
else:
|
1018 |
-
st.error("Please fill in all fields to add a clinical rule.")
|
1019 |
-
|
1020 |
-
# Clinical KPI Management
|
1021 |
-
st.subheader("Clinical KPI Definition")
|
1022 |
-
kpi_name = st.text_input("Enter KPI Name")
|
1023 |
-
kpi_calculation = st.text_area("Enter KPI Calculation (use 'df' for DataFrame)",
|
1024 |
-
help="Example: df['patient_count'].sum()")
|
1025 |
-
threshold = st.text_input("Enter Threshold for KPI (Optional)", help="Leave blank if not applicable")
|
1026 |
-
|
1027 |
-
if st.button("Add Clinical KPI"):
|
1028 |
-
if kpi_name and kpi_calculation:
|
1029 |
-
try:
|
1030 |
-
threshold_value = float(threshold) if threshold else None
|
1031 |
-
kpi = ClinicalKPI(
|
1032 |
-
name=kpi_name,
|
1033 |
-
calculation=kpi_calculation,
|
1034 |
-
threshold=threshold_value
|
1035 |
-
)
|
1036 |
-
st.session_state.kpi_monitoring.add_kpi(kpi)
|
1037 |
-
st.success(f"Added KPI '{kpi_name}' successfully.")
|
1038 |
-
except ValueError:
|
1039 |
-
st.error("Threshold must be a numeric value.")
|
1040 |
-
except Exception as e:
|
1041 |
-
st.error(f"Error creating KPI: {e}")
|
1042 |
-
else:
|
1043 |
-
st.error("Please provide both KPI name and calculation.")
|
1044 |
-
|
1045 |
-
# Execute Clinical Rules and Calculate KPIs
|
1046 |
-
selected_data_key = st.selectbox("Select Dataset for Clinical Logic", list(st.session_state.data.keys()))
|
1047 |
-
if selected_data_key:
|
1048 |
-
data = st.session_state.data[selected_data_key]
|
1049 |
-
if st.button("Execute Clinical Rules"):
|
1050 |
-
with st.spinner("Executing Clinical Rules..."):
|
1051 |
-
result = st.session_state.clinical_rules.execute_rules(data)
|
1052 |
-
st.json(result)
|
1053 |
-
if st.button("Calculate Clinical KPIs"):
|
1054 |
-
with st.spinner("Calculating Clinical KPIs..."):
|
1055 |
-
result = st.session_state.kpi_monitoring.calculate_kpis(data)
|
1056 |
-
st.json(result)
|
1057 |
-
else:
|
1058 |
-
st.warning("Please ingest data to execute clinical rules and calculate KPIs.")
|
1059 |
-
|
1060 |
-
def insights_section():
|
1061 |
-
"""Handles the Insights tab."""
|
1062 |
-
st.header("Automated Insights")
|
1063 |
-
|
1064 |
-
selected_data_key = st.selectbox("Select Dataset for Insights", list(st.session_state.data.keys()))
|
1065 |
-
if not selected_data_key:
|
1066 |
-
st.warning("Please select a dataset to generate insights.")
|
1067 |
-
return
|
1068 |
-
|
1069 |
-
data = st.session_state.data[selected_data_key]
|
1070 |
-
available_analyses = ["EDA", "temporal", "distribution", "hypothesis", "model"]
|
1071 |
-
selected_analyses = st.multiselect("Select Analyses for Insights", available_analyses)
|
1072 |
-
|
1073 |
-
if st.button("Generate Automated Insights"):
|
1074 |
-
if selected_analyses:
|
1075 |
-
with st.spinner("Generating Insights..."):
|
1076 |
-
results = st.session_state.automated_insights.generate_insights(
|
1077 |
-
data, analysis_names=selected_analyses
|
1078 |
-
)
|
1079 |
-
st.json(results)
|
1080 |
-
else:
|
1081 |
-
st.warning("Please select at least one analysis to generate insights.")
|
1082 |
-
|
1083 |
-
# Diagnosis Support
|
1084 |
-
st.subheader("Diagnosis Support")
|
1085 |
-
target_col = st.selectbox("Select Target Variable for Diagnosis", data.columns.tolist())
|
1086 |
-
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
1087 |
-
selected_feature_cols = st.multiselect("Select Feature Variables for Diagnosis", numeric_cols)
|
1088 |
-
|
1089 |
-
if st.button("Generate Diagnosis"):
|
1090 |
-
if target_col and selected_feature_cols:
|
1091 |
-
with st.spinner("Generating Diagnosis..."):
|
1092 |
-
result = st.session_state.diagnosis_support.diagnose(
|
1093 |
-
data, target_col=target_col, columns=selected_feature_cols, diagnosis_key="diagnosis_result"
|
1094 |
-
)
|
1095 |
-
st.json(result)
|
1096 |
-
else:
|
1097 |
-
st.error("Please select both target and feature variables for diagnosis.")
|
1098 |
-
|
1099 |
-
# Treatment Recommendation
|
1100 |
-
st.subheader("Treatment Recommendation")
|
1101 |
-
condition_col = st.selectbox("Select Condition Column for Treatment Recommendation", data.columns.tolist())
|
1102 |
-
treatment_col = st.selectbox("Select Treatment Column for Treatment Recommendation", data.columns.tolist())
|
1103 |
-
|
1104 |
-
if st.button("Generate Treatment Recommendation"):
|
1105 |
-
if condition_col and treatment_col:
|
1106 |
-
with st.spinner("Generating Treatment Recommendation..."):
|
1107 |
-
result = st.session_state.treatment_recommendation.recommend(
|
1108 |
-
data, condition_col=condition_col, treatment_col=treatment_col, recommendation_key="treatment_recommendation"
|
1109 |
-
)
|
1110 |
-
st.json(result)
|
1111 |
-
else:
|
1112 |
-
st.error("Please select both condition and treatment columns.")
|
1113 |
-
|
1114 |
-
def reports_section():
|
1115 |
-
"""Handles the Reports tab."""
|
1116 |
-
st.header("Automated Reports")
|
1117 |
-
|
1118 |
-
# Create Report Definition
|
1119 |
-
st.subheader("Create Report Definition")
|
1120 |
-
report_name = st.text_input("Report Name")
|
1121 |
-
report_def = st.text_area("Report Definition", help="Describe the structure and content of the report.")
|
1122 |
-
|
1123 |
-
if st.button("Create Report Definition"):
|
1124 |
-
if report_name and report_def:
|
1125 |
-
st.session_state.automated_reports.create_report_definition(report_name, report_def)
|
1126 |
-
st.success("Report definition created successfully.")
|
1127 |
-
else:
|
1128 |
-
st.error("Please provide both report name and definition.")
|
1129 |
-
|
1130 |
-
# Generate Report
|
1131 |
-
st.subheader("Generate Report")
|
1132 |
-
report_names = list(st.session_state.automated_reports.report_definitions.keys())
|
1133 |
-
if report_names:
|
1134 |
-
report_name_to_generate = st.selectbox("Select Report to Generate", report_names)
|
1135 |
-
if st.button("Generate Report"):
|
1136 |
-
with st.spinner("Generating Report..."):
|
1137 |
-
report = st.session_state.automated_reports.generate_report(report_name_to_generate, st.session_state.data)
|
1138 |
-
if "error" not in report:
|
1139 |
-
st.header(f"Report: {report['Report Name']}")
|
1140 |
-
st.markdown(f"**Definition:** {report['Report Definition']}")
|
1141 |
-
for df_name, df_content in report["Data"].items():
|
1142 |
-
st.subheader(f"Data: {df_name}")
|
1143 |
-
st.dataframe(pd.DataFrame(df_content))
|
1144 |
-
else:
|
1145 |
-
st.error(report["error"])
|
1146 |
-
else:
|
1147 |
-
st.info("No report definitions found. Please create a report definition first.")
|
1148 |
-
|
1149 |
-
def medical_knowledge_section():
|
1150 |
-
"""Handles the Medical Knowledge tab."""
|
1151 |
-
st.header("Medical Knowledge")
|
1152 |
-
query = st.text_input("Enter your medical question here:")
|
1153 |
-
|
1154 |
-
if st.button("Search"):
|
1155 |
-
if query.strip():
|
1156 |
-
with st.spinner("Searching..."):
|
1157 |
-
result = st.session_state.knowledge_base.search_medical_info(
|
1158 |
-
query, pub_email=st.session_state.pub_email
|
1159 |
-
)
|
1160 |
-
st.markdown(result)
|
1161 |
-
else:
|
1162 |
-
st.error("Please enter a medical question to search.")
|
1163 |
-
|
1164 |
-
if __name__ == "__main__":
|
1165 |
-
main()
|
|
|
1 |
+
import os
|
2 |
+
import json
|
3 |
+
import base64
|
4 |
+
import io
|
5 |
+
import ast
|
6 |
+
import logging
|
7 |
+
from abc import ABC, abstractmethod
|
8 |
+
from typing import Dict, List, Optional, Any
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import pandas as pd
|
12 |
+
import matplotlib.pyplot as plt
|
13 |
+
import seaborn as sns
|
14 |
+
import streamlit as st
|
15 |
+
import spacy
|
16 |
+
|
17 |
+
from scipy.stats import ttest_ind, f_oneway
|
18 |
+
from sklearn.model_selection import train_test_split
|
19 |
+
from sklearn.linear_model import LogisticRegression
|
20 |
+
from sklearn.metrics import accuracy_score
|
21 |
+
|
22 |
+
from statsmodels.tsa.seasonal import seasonal_decompose
|
23 |
+
from statsmodels.tsa.stattools import adfuller
|
24 |
+
|
25 |
+
from pydantic import BaseModel, Field
|
26 |
+
from Bio import Entrez # Ensure BioPython is installed
|
27 |
+
|
28 |
+
from dotenv import load_dotenv
|
29 |
+
import requests
|
30 |
+
import openai # Updated for OpenAI SDK v1.0.0+
|
31 |
+
from openai.error import APIError, RateLimitError, InvalidRequestError
|
32 |
+
|
33 |
+
# ---------------------- Load Environment Variables ---------------------------
|
34 |
+
load_dotenv()
|
35 |
+
|
36 |
+
# ---------------------- Logging Configuration ---------------------------
|
37 |
+
logging.basicConfig(
|
38 |
+
filename='app.log',
|
39 |
+
filemode='a',
|
40 |
+
format='%(asctime)s - %(levelname)s - %(message)s',
|
41 |
+
level=logging.INFO
|
42 |
+
)
|
43 |
+
logger = logging.getLogger()
|
44 |
+
|
45 |
+
# ---------------------- Streamlit Page Configuration ---------------------------
|
46 |
+
st.set_page_config(page_title="AI Clinical Intelligence Hub", layout="wide")
|
47 |
+
|
48 |
+
# ---------------------- Initialize OpenAI SDK ---------------------------
|
49 |
+
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
|
50 |
+
PUB_EMAIL = os.getenv("PUB_EMAIL", "")
|
51 |
+
|
52 |
+
if not OPENAI_API_KEY:
|
53 |
+
st.error("OpenAI API key must be set as an environment variable (OPENAI_API_KEY).")
|
54 |
+
st.stop()
|
55 |
+
|
56 |
+
# Set the OpenAI API key
|
57 |
+
openai.api_key = OPENAI_API_KEY
|
58 |
+
|
59 |
+
# ---------------------- Load spaCy Model ---------------------------
|
60 |
+
try:
|
61 |
+
nlp = spacy.load("en_core_web_sm")
|
62 |
+
except OSError:
|
63 |
+
# Avoid using Streamlit commands before set_page_config()
|
64 |
+
import subprocess
|
65 |
+
import sys
|
66 |
+
subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
|
67 |
+
nlp = spacy.load("en_core_web_sm")
|
68 |
+
|
69 |
+
# ---------------------- Base Classes and Schemas ---------------------------
|
70 |
+
|
71 |
+
class ResearchInput(BaseModel):
|
72 |
+
"""Base schema for research tool inputs."""
|
73 |
+
data_key: str = Field(..., description="Session state key containing DataFrame")
|
74 |
+
columns: Optional[List[str]] = Field(None, description="List of columns to analyze")
|
75 |
+
|
76 |
+
class TemporalAnalysisInput(ResearchInput):
|
77 |
+
"""Schema for temporal analysis."""
|
78 |
+
time_col: str = Field(..., description="Name of timestamp column")
|
79 |
+
value_col: str = Field(..., description="Name of value column to analyze")
|
80 |
+
|
81 |
+
class HypothesisInput(ResearchInput):
|
82 |
+
"""Schema for hypothesis testing."""
|
83 |
+
group_col: str = Field(..., description="Categorical column defining groups")
|
84 |
+
value_col: str = Field(..., description="Numerical column to compare")
|
85 |
+
|
86 |
+
class ModelTrainingInput(ResearchInput):
|
87 |
+
"""Schema for model training."""
|
88 |
+
target_col: str = Field(..., description="Name of target column")
|
89 |
+
|
90 |
+
class DataAnalyzer(ABC):
|
91 |
+
"""Abstract base class for data analysis modules."""
|
92 |
+
@abstractmethod
|
93 |
+
def invoke(self, data: pd.DataFrame, **kwargs) -> Dict[str, Any]:
|
94 |
+
pass
|
95 |
+
|
96 |
+
# ---------------------- Concrete Analyzer Implementations ---------------------------
|
97 |
+
|
98 |
+
class AdvancedEDA(DataAnalyzer):
|
99 |
+
"""Comprehensive Exploratory Data Analysis."""
|
100 |
+
def invoke(self, data: pd.DataFrame, **kwargs) -> Dict[str, Any]:
|
101 |
+
try:
|
102 |
+
analysis = {
|
103 |
+
"dimensionality": {
|
104 |
+
"rows": len(data),
|
105 |
+
"columns": list(data.columns),
|
106 |
+
"memory_usage_MB": f"{data.memory_usage().sum() / 1e6:.2f} MB"
|
107 |
+
},
|
108 |
+
"statistical_profile": data.describe(percentiles=[.25, .5, .75]).to_dict(),
|
109 |
+
"temporal_analysis": {
|
110 |
+
"date_ranges": {
|
111 |
+
col: {
|
112 |
+
"min": data[col].min(),
|
113 |
+
"max": data[col].max()
|
114 |
+
} for col in data.select_dtypes(include='datetime').columns
|
115 |
+
}
|
116 |
+
},
|
117 |
+
"data_quality": {
|
118 |
+
"missing_values": data.isnull().sum().to_dict(),
|
119 |
+
"duplicates": data.duplicated().sum(),
|
120 |
+
"cardinality": {
|
121 |
+
col: data[col].nunique() for col in data.columns
|
122 |
+
}
|
123 |
+
}
|
124 |
+
}
|
125 |
+
return analysis
|
126 |
+
except Exception as e:
|
127 |
+
logger.error(f"EDA Failed: {str(e)}")
|
128 |
+
return {"error": f"EDA Failed: {str(e)}"}
|
129 |
+
|
130 |
+
class DistributionVisualizer(DataAnalyzer):
|
131 |
+
"""Distribution visualizations."""
|
132 |
+
def invoke(self, data: pd.DataFrame, columns: List[str], **kwargs) -> str:
|
133 |
+
try:
|
134 |
+
plt.figure(figsize=(12, 6))
|
135 |
+
for i, col in enumerate(columns, 1):
|
136 |
+
plt.subplot(1, len(columns), i)
|
137 |
+
sns.histplot(data[col], kde=True, stat="density")
|
138 |
+
plt.title(f'Distribution of {col}', fontsize=10)
|
139 |
+
plt.xticks(fontsize=8)
|
140 |
+
plt.yticks(fontsize=8)
|
141 |
+
plt.tight_layout()
|
142 |
+
|
143 |
+
buf = io.BytesIO()
|
144 |
+
plt.savefig(buf, format='png', dpi=300, bbox_inches='tight')
|
145 |
+
plt.close()
|
146 |
+
return base64.b64encode(buf.getvalue()).decode()
|
147 |
+
except Exception as e:
|
148 |
+
logger.error(f"Visualization Error: {str(e)}")
|
149 |
+
return f"Visualization Error: {str(e)}"
|
150 |
+
|
151 |
+
class TemporalAnalyzer(DataAnalyzer):
|
152 |
+
"""Time series analysis."""
|
153 |
+
def invoke(self, data: pd.DataFrame, time_col: str, value_col: str, **kwargs) -> Dict[str, Any]:
|
154 |
+
try:
|
155 |
+
ts_data = data.set_index(pd.to_datetime(data[time_col]))[value_col]
|
156 |
+
decomposition = seasonal_decompose(ts_data, period=365)
|
157 |
+
|
158 |
+
plt.figure(figsize=(12, 8))
|
159 |
+
decomposition.plot()
|
160 |
+
plt.tight_layout()
|
161 |
+
|
162 |
+
buf = io.BytesIO()
|
163 |
+
plt.savefig(buf, format='png')
|
164 |
+
plt.close()
|
165 |
+
plot_data = base64.b64encode(buf.getvalue()).decode()
|
166 |
+
|
167 |
+
stationarity_p_value = adfuller(ts_data)[1]
|
168 |
+
|
169 |
+
return {
|
170 |
+
"trend_statistics": {
|
171 |
+
"stationarity_p_value": stationarity_p_value,
|
172 |
+
"seasonality_strength": float(max(decomposition.seasonal))
|
173 |
+
},
|
174 |
+
"visualization": plot_data
|
175 |
+
}
|
176 |
+
except Exception as e:
|
177 |
+
logger.error(f"Temporal Analysis Failed: {str(e)}")
|
178 |
+
return {"error": f"Temporal Analysis Failed: {str(e)}"}
|
179 |
+
|
180 |
+
class HypothesisTester(DataAnalyzer):
|
181 |
+
"""Statistical hypothesis testing."""
|
182 |
+
def invoke(self, data: pd.DataFrame, group_col: str, value_col: str, **kwargs) -> Dict[str, Any]:
|
183 |
+
try:
|
184 |
+
groups = data[group_col].unique()
|
185 |
+
|
186 |
+
if len(groups) < 2:
|
187 |
+
return {"error": "Insufficient groups for comparison"}
|
188 |
+
|
189 |
+
group_data = [data[data[group_col] == g][value_col] for g in groups]
|
190 |
+
|
191 |
+
if len(groups) == 2:
|
192 |
+
stat, p = ttest_ind(*group_data)
|
193 |
+
test_type = "Independent t-test"
|
194 |
+
effect_size = self.calculate_cohens_d(group_data[0], group_data[1])
|
195 |
+
else:
|
196 |
+
stat, p = f_oneway(*group_data)
|
197 |
+
test_type = "ANOVA"
|
198 |
+
effect_size = None
|
199 |
+
|
200 |
+
return {
|
201 |
+
"test_type": test_type,
|
202 |
+
"test_statistic": stat,
|
203 |
+
"p_value": p,
|
204 |
+
"effect_size": effect_size,
|
205 |
+
"interpretation": self.interpret_p_value(p)
|
206 |
+
}
|
207 |
+
except Exception as e:
|
208 |
+
logger.error(f"Hypothesis Testing Failed: {str(e)}")
|
209 |
+
return {"error": f"Hypothesis Testing Failed: {str(e)}"}
|
210 |
+
|
211 |
+
@staticmethod
|
212 |
+
def calculate_cohens_d(x: pd.Series, y: pd.Series) -> Optional[float]:
|
213 |
+
"""Calculate Cohen's d for effect size."""
|
214 |
+
try:
|
215 |
+
mean_diff = abs(x.mean() - y.mean())
|
216 |
+
pooled_std = np.sqrt((x.var() + y.var()) / 2)
|
217 |
+
return mean_diff / pooled_std
|
218 |
+
except Exception as e:
|
219 |
+
logger.error(f"Error calculating Cohen's d: {str(e)}")
|
220 |
+
return None
|
221 |
+
|
222 |
+
@staticmethod
|
223 |
+
def interpret_p_value(p: float) -> str:
|
224 |
+
"""Interpret the p-value."""
|
225 |
+
if p < 0.001:
|
226 |
+
return "Very strong evidence against H0"
|
227 |
+
elif p < 0.01:
|
228 |
+
return "Strong evidence against H0"
|
229 |
+
elif p < 0.05:
|
230 |
+
return "Evidence against H0"
|
231 |
+
elif p < 0.1:
|
232 |
+
return "Weak evidence against H0"
|
233 |
+
else:
|
234 |
+
return "No significant evidence against H0"
|
235 |
+
|
236 |
+
class LogisticRegressionTrainer(DataAnalyzer):
|
237 |
+
"""Logistic Regression Model Trainer."""
|
238 |
+
def invoke(self, data: pd.DataFrame, target_col: str, columns: List[str], **kwargs) -> Dict[str, Any]:
|
239 |
+
try:
|
240 |
+
X = data[columns]
|
241 |
+
y = data[target_col]
|
242 |
+
X_train, X_test, y_train, y_test = train_test_split(
|
243 |
+
X, y, test_size=0.2, random_state=42
|
244 |
+
)
|
245 |
+
model = LogisticRegression(max_iter=1000)
|
246 |
+
model.fit(X_train, y_train)
|
247 |
+
y_pred = model.predict(X_test)
|
248 |
+
accuracy = accuracy_score(y_test, y_pred)
|
249 |
+
return {
|
250 |
+
"model_type": "Logistic Regression",
|
251 |
+
"accuracy": accuracy,
|
252 |
+
"model_params": model.get_params()
|
253 |
+
}
|
254 |
+
except Exception as e:
|
255 |
+
logger.error(f"Logistic Regression Model Error: {str(e)}")
|
256 |
+
return {"error": f"Logistic Regression Model Error: {str(e)}"}
|
257 |
+
|
258 |
+
# ---------------------- Business Logic Layer ---------------------------
|
259 |
+
|
260 |
+
class ClinicalRule(BaseModel):
|
261 |
+
"""Defines a clinical rule."""
|
262 |
+
name: str
|
263 |
+
condition: str
|
264 |
+
action: str
|
265 |
+
severity: str # low, medium, or high
|
266 |
+
|
267 |
+
class ClinicalRulesEngine:
|
268 |
+
"""Executes rules against patient data."""
|
269 |
+
def __init__(self):
|
270 |
+
self.rules: Dict[str, ClinicalRule] = {}
|
271 |
+
|
272 |
+
def add_rule(self, rule: ClinicalRule):
|
273 |
+
self.rules[rule.name] = rule
|
274 |
+
|
275 |
+
def execute_rules(self, data: pd.DataFrame) -> Dict[str, Any]:
|
276 |
+
results = {}
|
277 |
+
for rule_name, rule in self.rules.items():
|
278 |
+
try:
|
279 |
+
# Using safe_eval instead of eval for security
|
280 |
+
rule_matched = self.safe_eval(rule.condition, {"df": data})
|
281 |
+
results[rule_name] = {
|
282 |
+
"rule_matched": rule_matched,
|
283 |
+
"action": rule.action if rule_matched else None,
|
284 |
+
"severity": rule.severity if rule_matched else None
|
285 |
+
}
|
286 |
+
except Exception as e:
|
287 |
+
logger.error(f"Error executing rule '{rule_name}': {str(e)}")
|
288 |
+
results[rule_name] = {
|
289 |
+
"rule_matched": False,
|
290 |
+
"error": str(e),
|
291 |
+
"severity": None
|
292 |
+
}
|
293 |
+
return results
|
294 |
+
|
295 |
+
@staticmethod
|
296 |
+
def safe_eval(expr, variables):
|
297 |
+
"""
|
298 |
+
Safely evaluate an expression using AST parsing.
|
299 |
+
Only allows certain node types to prevent execution of arbitrary code.
|
300 |
+
"""
|
301 |
+
allowed_nodes = (
|
302 |
+
ast.Expression, ast.BoolOp, ast.BinOp, ast.UnaryOp, ast.Compare,
|
303 |
+
ast.Call, ast.Name, ast.Load, ast.Constant, ast.Num, ast.Str,
|
304 |
+
ast.List, ast.Tuple, ast.Dict
|
305 |
+
)
|
306 |
+
try:
|
307 |
+
node = ast.parse(expr, mode='eval')
|
308 |
+
for subnode in ast.walk(node):
|
309 |
+
if not isinstance(subnode, allowed_nodes):
|
310 |
+
raise ValueError(f"Unsupported expression: {expr}")
|
311 |
+
return eval(compile(node, '<string>', mode='eval'), {"__builtins__": None}, variables)
|
312 |
+
except Exception as e:
|
313 |
+
logger.error(f"safe_eval error: {str(e)}")
|
314 |
+
raise ValueError(f"Invalid expression: {e}")
|
315 |
+
|
316 |
+
class ClinicalKPI(BaseModel):
|
317 |
+
"""Define a clinical KPI."""
|
318 |
+
name: str
|
319 |
+
calculation: str
|
320 |
+
threshold: Optional[float] = None
|
321 |
+
|
322 |
+
class ClinicalKPIMonitoring:
|
323 |
+
"""Calculates KPIs based on data."""
|
324 |
+
def __init__(self):
|
325 |
+
self.kpis: Dict[str, ClinicalKPI] = {}
|
326 |
+
|
327 |
+
def add_kpi(self, kpi: ClinicalKPI):
|
328 |
+
self.kpis[kpi.name] = kpi
|
329 |
+
|
330 |
+
def calculate_kpis(self, data: pd.DataFrame) -> Dict[str, Any]:
|
331 |
+
results = {}
|
332 |
+
for kpi_name, kpi in self.kpis.items():
|
333 |
+
try:
|
334 |
+
# Using safe_eval instead of eval for security
|
335 |
+
kpi_value = self.safe_eval(kpi.calculation, {"df": data})
|
336 |
+
status = self.evaluate_threshold(kpi_value, kpi.threshold)
|
337 |
+
results[kpi_name] = {
|
338 |
+
"value": kpi_value,
|
339 |
+
"threshold": kpi.threshold,
|
340 |
+
"status": status
|
341 |
+
}
|
342 |
+
except Exception as e:
|
343 |
+
logger.error(f"Error calculating KPI '{kpi_name}': {str(e)}")
|
344 |
+
results[kpi_name] = {"error": str(e)}
|
345 |
+
return results
|
346 |
+
|
347 |
+
@staticmethod
|
348 |
+
def evaluate_threshold(value: Any, threshold: Optional[float]) -> Optional[str]:
|
349 |
+
if threshold is None:
|
350 |
+
return None
|
351 |
+
try:
|
352 |
+
return "Above Threshold" if value > threshold else "Below Threshold"
|
353 |
+
except TypeError:
|
354 |
+
return "Threshold Evaluation Not Applicable"
|
355 |
+
|
356 |
+
@staticmethod
|
357 |
+
def safe_eval(expr, variables):
|
358 |
+
"""
|
359 |
+
Safely evaluate an expression using AST parsing.
|
360 |
+
Only allows certain node types to prevent execution of arbitrary code.
|
361 |
+
"""
|
362 |
+
allowed_nodes = (
|
363 |
+
ast.Expression, ast.BoolOp, ast.BinOp, ast.UnaryOp, ast.Compare,
|
364 |
+
ast.Call, ast.Name, ast.Load, ast.Constant, ast.Num, ast.Str,
|
365 |
+
ast.List, ast.Tuple, ast.Dict
|
366 |
+
)
|
367 |
+
try:
|
368 |
+
node = ast.parse(expr, mode='eval')
|
369 |
+
for subnode in ast.walk(node):
|
370 |
+
if not isinstance(subnode, allowed_nodes):
|
371 |
+
raise ValueError(f"Unsupported expression: {expr}")
|
372 |
+
return eval(compile(node, '<string>', mode='eval'), {"__builtins__": None}, variables)
|
373 |
+
except Exception as e:
|
374 |
+
logger.error(f"safe_eval error: {str(e)}")
|
375 |
+
raise ValueError(f"Invalid expression: {e}")
|
376 |
+
|
377 |
+
class DiagnosisSupport(ABC):
|
378 |
+
"""Abstract class for implementing clinical diagnoses."""
|
379 |
+
@abstractmethod
|
380 |
+
def diagnose(
|
381 |
+
self,
|
382 |
+
data: pd.DataFrame,
|
383 |
+
target_col: str,
|
384 |
+
columns: List[str],
|
385 |
+
diagnosis_key: str = "diagnosis",
|
386 |
+
**kwargs
|
387 |
+
) -> pd.DataFrame:
|
388 |
+
pass
|
389 |
+
|
390 |
+
class SimpleDiagnosis(DiagnosisSupport):
|
391 |
+
"""Provides a simple diagnosis example, based on the Logistic regression model."""
|
392 |
+
def __init__(self):
|
393 |
+
self.model_trainer: LogisticRegressionTrainer = LogisticRegressionTrainer()
|
394 |
+
|
395 |
+
def diagnose(
|
396 |
+
self,
|
397 |
+
data: pd.DataFrame,
|
398 |
+
target_col: str,
|
399 |
+
columns: List[str],
|
400 |
+
diagnosis_key: str = "diagnosis",
|
401 |
+
**kwargs
|
402 |
+
) -> pd.DataFrame:
|
403 |
+
try:
|
404 |
+
result = self.model_trainer.invoke(data, target_col=target_col, columns=columns)
|
405 |
+
if "accuracy" in result:
|
406 |
+
return pd.DataFrame({
|
407 |
+
diagnosis_key: [f"Model Accuracy: {result['accuracy']:.2%}"],
|
408 |
+
"model": [result["model_type"]]
|
409 |
+
})
|
410 |
+
else:
|
411 |
+
return pd.DataFrame({
|
412 |
+
diagnosis_key: [f"Diagnosis failed: {result.get('error', 'Unknown error')}"]
|
413 |
+
})
|
414 |
+
except Exception as e:
|
415 |
+
logger.error(f"Error during diagnosis: {str(e)}")
|
416 |
+
return pd.DataFrame({
|
417 |
+
diagnosis_key: [f"Error during diagnosis: {e}"]
|
418 |
+
})
|
419 |
+
|
420 |
+
class TreatmentRecommendation(ABC):
|
421 |
+
"""Abstract class for treatment recommendations."""
|
422 |
+
@abstractmethod
|
423 |
+
def recommend(
|
424 |
+
self,
|
425 |
+
data: pd.DataFrame,
|
426 |
+
condition_col: str,
|
427 |
+
treatment_col: str,
|
428 |
+
recommendation_key: str = "recommendation",
|
429 |
+
**kwargs
|
430 |
+
) -> pd.DataFrame:
|
431 |
+
pass
|
432 |
+
|
433 |
+
class BasicTreatmentRecommendation(TreatmentRecommendation):
|
434 |
+
"""A placeholder class for basic treatment recommendations."""
|
435 |
+
def recommend(
|
436 |
+
self,
|
437 |
+
data: pd.DataFrame,
|
438 |
+
condition_col: str,
|
439 |
+
treatment_col: str,
|
440 |
+
recommendation_key: str = "recommendation",
|
441 |
+
**kwargs
|
442 |
+
) -> pd.DataFrame:
|
443 |
+
if condition_col not in data.columns or treatment_col not in data.columns:
|
444 |
+
logger.warning(f"Condition or Treatment columns not found: {condition_col}, {treatment_col}")
|
445 |
+
return pd.DataFrame({
|
446 |
+
recommendation_key: ["Condition or Treatment columns not found!"]
|
447 |
+
})
|
448 |
+
|
449 |
+
treatment = data[data[condition_col] == "High"][treatment_col].to_list()
|
450 |
+
if treatment:
|
451 |
+
return pd.DataFrame({
|
452 |
+
recommendation_key: [f"Treatment recommended for High risk patients: {treatment}"]
|
453 |
+
})
|
454 |
+
else:
|
455 |
+
return pd.DataFrame({
|
456 |
+
recommendation_key: ["No treatment recommendation found!"]
|
457 |
+
})
|
458 |
+
|
459 |
+
# ---------------------- Medical Knowledge Base ---------------------------
|
460 |
+
|
461 |
+
class MedicalKnowledgeBase(ABC):
|
462 |
+
"""Abstract class for Medical Knowledge."""
|
463 |
+
@abstractmethod
|
464 |
+
def search_medical_info(self, query: str, pub_email: str = "") -> str:
|
465 |
+
pass
|
466 |
+
|
467 |
+
class SimpleMedicalKnowledge(MedicalKnowledgeBase):
|
468 |
+
"""Enhanced Medical Knowledge Class using OpenAI GPT-4."""
|
469 |
+
def __init__(self, nlp_model):
|
470 |
+
self.nlp = nlp_model # Using the loaded spaCy model
|
471 |
+
|
472 |
+
def search_medical_info(self, query: str, pub_email: str = "") -> str:
|
473 |
+
"""
|
474 |
+
Uses OpenAI's GPT-4 to fetch medical information based on the user's query.
|
475 |
+
"""
|
476 |
+
logger.info(f"Received medical query: {query}")
|
477 |
+
try:
|
478 |
+
# Preprocess the query (e.g., entity recognition)
|
479 |
+
doc = self.nlp(query.lower())
|
480 |
+
entities = [ent.text for ent in doc.ents]
|
481 |
+
processed_query = " ".join(entities) if entities else query.lower()
|
482 |
+
|
483 |
+
logger.info(f"Processed query: {processed_query}")
|
484 |
+
|
485 |
+
# Create a prompt for GPT-4
|
486 |
+
prompt = f"""
|
487 |
+
You are a medical assistant. Provide a comprehensive and accurate response to the following medical query:
|
488 |
+
|
489 |
+
Query: {processed_query}
|
490 |
+
|
491 |
+
Please ensure the information is clear, concise, and evidence-based.
|
492 |
+
"""
|
493 |
+
|
494 |
+
# Make the API request to OpenAI GPT-4
|
495 |
+
response = openai.ChatCompletion.create(
|
496 |
+
model="gpt-4",
|
497 |
+
messages=[
|
498 |
+
{"role": "system", "content": "You are a helpful medical assistant."},
|
499 |
+
{"role": "user", "content": prompt}
|
500 |
+
],
|
501 |
+
max_tokens=500,
|
502 |
+
temperature=0.7,
|
503 |
+
)
|
504 |
+
|
505 |
+
# Extract the answer from the response
|
506 |
+
answer = response.choices[0].message['content'].strip()
|
507 |
+
|
508 |
+
logger.info("Successfully retrieved data from OpenAI GPT-4.")
|
509 |
+
|
510 |
+
# Fetch PubMed abstract related to the query
|
511 |
+
pubmed_abstract = self.fetch_pubmed_abstract(processed_query, pub_email)
|
512 |
+
|
513 |
+
# Format the response
|
514 |
+
return f"**Based on your query:** {answer}\n\n**PubMed Abstract:**\n\n{pubmed_abstract}"
|
515 |
+
|
516 |
+
except RateLimitError as e:
|
517 |
+
logger.error(f"Rate Limit Exceeded: {str(e)}")
|
518 |
+
return "Rate limit exceeded. Please try again later."
|
519 |
+
except InvalidRequestError as e:
|
520 |
+
logger.error(f"Invalid Request: {str(e)}")
|
521 |
+
return f"Invalid request: {str(e)}"
|
522 |
+
except APIError as e:
|
523 |
+
logger.error(f"OpenAI API Error: {str(e)}")
|
524 |
+
return f"OpenAI API Error: {str(e)}"
|
525 |
+
except Exception as e:
|
526 |
+
logger.error(f"Medical Knowledge Search Failed: {str(e)}")
|
527 |
+
return f"Medical Knowledge Search Failed: {str(e)}"
|
528 |
+
|
529 |
+
def fetch_pubmed_abstract(self, query: str, email: str) -> str:
|
530 |
+
"""
|
531 |
+
Searches PubMed for abstracts related to the query.
|
532 |
+
"""
|
533 |
+
try:
|
534 |
+
if not email:
|
535 |
+
logger.warning("PubMed abstract retrieval skipped: Email not provided.")
|
536 |
+
return "No PubMed abstract available: Email not provided."
|
537 |
+
|
538 |
+
Entrez.email = email
|
539 |
+
handle = Entrez.esearch(db="pubmed", term=query, retmax=1, sort='relevance')
|
540 |
+
record = Entrez.read(handle)
|
541 |
+
handle.close()
|
542 |
+
logger.info(f"PubMed search for query '{query}' returned IDs: {record['IdList']}")
|
543 |
+
|
544 |
+
if record["IdList"]:
|
545 |
+
handle = Entrez.efetch(db="pubmed", id=record["IdList"][0], rettype="abstract", retmode="text")
|
546 |
+
abstract = handle.read()
|
547 |
+
handle.close()
|
548 |
+
logger.info(f"Fetched PubMed abstract for ID {record['IdList'][0]}")
|
549 |
+
return abstract
|
550 |
+
else:
|
551 |
+
logger.info(f"No PubMed abstracts found for query '{query}'.")
|
552 |
+
return "No abstracts found for this query on PubMed."
|
553 |
+
except Exception as e:
|
554 |
+
logger.error(f"Error searching PubMed: {e}")
|
555 |
+
return f"Error searching PubMed: {e}"
|
556 |
+
|
557 |
+
# ---------------------- Forecasting Engine ---------------------------
|
558 |
+
|
559 |
+
class ForecastingEngine(ABC):
|
560 |
+
"""Abstract class for forecasting."""
|
561 |
+
@abstractmethod
|
562 |
+
def predict(self, data: pd.DataFrame, **kwargs) -> pd.DataFrame:
|
563 |
+
pass
|
564 |
+
|
565 |
+
class SimpleForecasting(ForecastingEngine):
|
566 |
+
"""Simple forecasting engine."""
|
567 |
+
def predict(self, data: pd.DataFrame, period: int = 7, **kwargs) -> pd.DataFrame:
|
568 |
+
# Placeholder for actual forecasting logic
|
569 |
+
return pd.DataFrame({"forecast": [f"Forecast for the next {period} days"]})
|
570 |
+
|
571 |
+
# ---------------------- Insights and Reporting Layer ---------------------------
|
572 |
+
|
573 |
+
class AutomatedInsights:
|
574 |
+
"""Generates automated insights based on selected analyses."""
|
575 |
+
def __init__(self):
|
576 |
+
self.analyses: Dict[str, DataAnalyzer] = {
|
577 |
+
"EDA": AdvancedEDA(),
|
578 |
+
"temporal": TemporalAnalyzer(),
|
579 |
+
"distribution": DistributionVisualizer(),
|
580 |
+
"hypothesis": HypothesisTester(),
|
581 |
+
"model": LogisticRegressionTrainer()
|
582 |
+
}
|
583 |
+
|
584 |
+
def generate_insights(self, data: pd.DataFrame, analysis_names: List[str], **kwargs) -> Dict[str, Any]:
|
585 |
+
results = {}
|
586 |
+
for name in analysis_names:
|
587 |
+
analyzer = self.analyses.get(name)
|
588 |
+
if analyzer:
|
589 |
+
try:
|
590 |
+
results[name] = analyzer.invoke(data=data, **kwargs)
|
591 |
+
except Exception as e:
|
592 |
+
logger.error(f"Error in analysis '{name}': {str(e)}")
|
593 |
+
results[name] = {"error": str(e)}
|
594 |
+
else:
|
595 |
+
logger.warning(f"Analysis '{name}' not found.")
|
596 |
+
results[name] = {"error": "Analysis not found"}
|
597 |
+
return results
|
598 |
+
|
599 |
+
class Dashboard:
|
600 |
+
"""Handles the creation and display of the dashboard."""
|
601 |
+
def __init__(self):
|
602 |
+
self.layout: Dict[str, str] = {}
|
603 |
+
|
604 |
+
def add_visualisation(self, vis_name: str, vis_type: str):
|
605 |
+
self.layout[vis_name] = vis_type
|
606 |
+
|
607 |
+
def display_dashboard(self, data_dict: Dict[str, pd.DataFrame]):
|
608 |
+
st.header("Dashboard")
|
609 |
+
for vis_name, vis_type in self.layout.items():
|
610 |
+
st.subheader(vis_name)
|
611 |
+
df = data_dict.get(vis_name)
|
612 |
+
if df is not None:
|
613 |
+
if vis_type == "table":
|
614 |
+
st.table(df)
|
615 |
+
elif vis_type == "plot":
|
616 |
+
if len(df.columns) > 1:
|
617 |
+
fig = plt.figure()
|
618 |
+
sns.lineplot(data=df)
|
619 |
+
st.pyplot(fig)
|
620 |
+
else:
|
621 |
+
st.write("Please select a DataFrame with more than 1 column for plotting.")
|
622 |
+
else:
|
623 |
+
st.write("Data Not Found")
|
624 |
+
|
625 |
+
class AutomatedReports:
|
626 |
+
"""Manages automated report definitions and generation."""
|
627 |
+
def __init__(self):
|
628 |
+
self.report_definitions: Dict[str, str] = {}
|
629 |
+
|
630 |
+
def create_report_definition(self, report_name: str, definition: str):
|
631 |
+
self.report_definitions[report_name] = definition
|
632 |
+
|
633 |
+
def generate_report(self, report_name: str, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
|
634 |
+
if report_name not in self.report_definitions:
|
635 |
+
return {"error": "Report name not found"}
|
636 |
+
report_content = {
|
637 |
+
"Report Name": report_name,
|
638 |
+
"Report Definition": self.report_definitions[report_name],
|
639 |
+
"Data": {df_name: df.to_dict() for df_name, df in data.items()}
|
640 |
+
}
|
641 |
+
return report_content
|
642 |
+
|
643 |
+
# ---------------------- Data Acquisition Layer ---------------------------
|
644 |
+
|
645 |
+
class DataSource(ABC):
|
646 |
+
"""Base class for data sources."""
|
647 |
+
@abstractmethod
|
648 |
+
def connect(self) -> None:
|
649 |
+
"""Connect to the data source."""
|
650 |
+
pass
|
651 |
+
|
652 |
+
@abstractmethod
|
653 |
+
def fetch_data(self, query: str, **kwargs) -> pd.DataFrame:
|
654 |
+
"""Fetch the data based on a specific query."""
|
655 |
+
pass
|
656 |
+
|
657 |
+
class CSVDataSource(DataSource):
|
658 |
+
"""Data source for CSV files."""
|
659 |
+
def __init__(self, file_path: io.BytesIO):
|
660 |
+
self.file_path = file_path
|
661 |
+
self.data: Optional[pd.DataFrame] = None
|
662 |
+
|
663 |
+
def connect(self):
|
664 |
+
self.data = pd.read_csv(self.file_path)
|
665 |
+
|
666 |
+
def fetch_data(self, query: str = None, **kwargs) -> pd.DataFrame:
|
667 |
+
if self.data is None:
|
668 |
+
raise Exception("No connection is made, call connect()")
|
669 |
+
return self.data
|
670 |
+
|
671 |
+
class DatabaseSource(DataSource):
|
672 |
+
"""Data source for SQL Databases."""
|
673 |
+
def __init__(self, connection_string: str, database_type: str):
|
674 |
+
self.connection_string = connection_string
|
675 |
+
self.database_type = database_type.lower()
|
676 |
+
self.connection = None
|
677 |
+
|
678 |
+
def connect(self):
|
679 |
+
if self.database_type == "sql":
|
680 |
+
# Placeholder for actual SQL connection logic
|
681 |
+
self.connection = "Connected to SQL Database"
|
682 |
+
else:
|
683 |
+
raise Exception(f"Database type '{self.database_type}' is not supported.")
|
684 |
+
|
685 |
+
def fetch_data(self, query: str, **kwargs) -> pd.DataFrame:
|
686 |
+
if self.connection is None:
|
687 |
+
raise Exception("No connection is made, call connect()")
|
688 |
+
# Placeholder for data fetching logic
|
689 |
+
return pd.DataFrame({"result": [f"Fetched data based on query: {query}"]})
|
690 |
+
|
691 |
+
class DataIngestion:
|
692 |
+
"""Handles data ingestion from various sources."""
|
693 |
+
def __init__(self):
|
694 |
+
self.sources: Dict[str, DataSource] = {}
|
695 |
+
|
696 |
+
def add_source(self, source_name: str, source: DataSource):
|
697 |
+
self.sources[source_name] = source
|
698 |
+
|
699 |
+
def ingest_data(self, source_name: str, query: str = None, **kwargs) -> pd.DataFrame:
|
700 |
+
if source_name not in self.sources:
|
701 |
+
raise Exception(f"Source '{source_name}' not found.")
|
702 |
+
source = self.sources[source_name]
|
703 |
+
source.connect()
|
704 |
+
return source.fetch_data(query, **kwargs)
|
705 |
+
|
706 |
+
class DataModel(BaseModel):
|
707 |
+
"""Defines a data model."""
|
708 |
+
name: str
|
709 |
+
kpis: List[str] = Field(default_factory=list)
|
710 |
+
dimensions: List[str] = Field(default_factory=list)
|
711 |
+
custom_calculations: Optional[Dict[str, str]] = None
|
712 |
+
relations: Optional[Dict[str, str]] = None # Example: {"table1": "table2"}
|
713 |
+
|
714 |
+
def to_json(self) -> str:
|
715 |
+
return json.dumps(self.dict())
|
716 |
+
|
717 |
+
@staticmethod
|
718 |
+
def from_json(json_str: str) -> 'DataModel':
|
719 |
+
return DataModel(**json.loads(json_str))
|
720 |
+
|
721 |
+
class DataModelling:
|
722 |
+
"""Manages data models."""
|
723 |
+
def __init__(self):
|
724 |
+
self.models: Dict[str, DataModel] = {}
|
725 |
+
|
726 |
+
def add_model(self, model: DataModel):
|
727 |
+
self.models[model.name] = model
|
728 |
+
|
729 |
+
def get_model(self, model_name: str) -> DataModel:
|
730 |
+
if model_name not in self.models:
|
731 |
+
raise Exception(f"Model '{model_name}' not found.")
|
732 |
+
return self.models[model_name]
|
733 |
+
|
734 |
+
# ---------------------- Main Streamlit Application ---------------------------
|
735 |
+
|
736 |
+
def main():
|
737 |
+
"""Main function to run the Streamlit app."""
|
738 |
+
st.title("🏥 AI-Powered Clinical Intelligence Hub")
|
739 |
+
|
740 |
+
# Initialize Session State
|
741 |
+
initialize_session_state()
|
742 |
+
|
743 |
+
# Sidebar for Data Management
|
744 |
+
with st.sidebar:
|
745 |
+
data_management_section()
|
746 |
+
|
747 |
+
# Main Content
|
748 |
+
if st.session_state.data:
|
749 |
+
col1, col2 = st.columns([1, 3])
|
750 |
+
|
751 |
+
with col1:
|
752 |
+
dataset_metadata_section()
|
753 |
+
|
754 |
+
with col2:
|
755 |
+
main_tabs_section()
|
756 |
+
|
757 |
+
def initialize_session_state():
|
758 |
+
"""Initialize necessary components in Streamlit's session state."""
|
759 |
+
if 'data' not in st.session_state:
|
760 |
+
st.session_state.data = {} # Store pd.DataFrame under a name
|
761 |
+
if 'data_ingestion' not in st.session_state:
|
762 |
+
st.session_state.data_ingestion = DataIngestion()
|
763 |
+
if 'data_modelling' not in st.session_state:
|
764 |
+
st.session_state.data_modelling = DataModelling()
|
765 |
+
if 'clinical_rules' not in st.session_state:
|
766 |
+
st.session_state.clinical_rules = ClinicalRulesEngine()
|
767 |
+
if 'kpi_monitoring' not in st.session_state:
|
768 |
+
st.session_state.kpi_monitoring = ClinicalKPIMonitoring()
|
769 |
+
if 'forecasting_engine' not in st.session_state:
|
770 |
+
st.session_state.forecasting_engine = SimpleForecasting()
|
771 |
+
if 'automated_insights' not in st.session_state:
|
772 |
+
st.session_state.automated_insights = AutomatedInsights()
|
773 |
+
if 'dashboard' not in st.session_state:
|
774 |
+
st.session_state.dashboard = Dashboard()
|
775 |
+
if 'automated_reports' not in st.session_state:
|
776 |
+
st.session_state.automated_reports = AutomatedReports()
|
777 |
+
if 'diagnosis_support' not in st.session_state:
|
778 |
+
st.session_state.diagnosis_support = SimpleDiagnosis()
|
779 |
+
if 'treatment_recommendation' not in st.session_state:
|
780 |
+
st.session_state.treatment_recommendation = BasicTreatmentRecommendation()
|
781 |
+
if 'knowledge_base' not in st.session_state:
|
782 |
+
st.session_state.knowledge_base = SimpleMedicalKnowledge(nlp_model=nlp)
|
783 |
+
if 'pub_email' not in st.session_state:
|
784 |
+
st.session_state.pub_email = PUB_EMAIL # Load PUB_EMAIL from environment variables
|
785 |
+
|
786 |
+
def data_management_section():
|
787 |
+
"""Handles the data management section in the sidebar."""
|
788 |
+
st.header("⚙️ Data Management")
|
789 |
+
data_source_selection = st.selectbox("Select Data Source Type", ["CSV", "SQL Database"])
|
790 |
+
|
791 |
+
if data_source_selection == "CSV":
|
792 |
+
handle_csv_upload()
|
793 |
+
elif data_source_selection == "SQL Database":
|
794 |
+
handle_sql_database()
|
795 |
+
|
796 |
+
if st.button("Ingest Data"):
|
797 |
+
ingest_data_action()
|
798 |
+
|
799 |
+
def handle_csv_upload():
|
800 |
+
"""Handles CSV file uploads."""
|
801 |
+
uploaded_file = st.file_uploader("Upload research dataset (CSV)", type=["csv"])
|
802 |
+
if uploaded_file:
|
803 |
+
source_name = st.text_input("Data Source Name")
|
804 |
+
if source_name:
|
805 |
+
try:
|
806 |
+
csv_source = CSVDataSource(file_path=uploaded_file)
|
807 |
+
st.session_state.data_ingestion.add_source(source_name, csv_source)
|
808 |
+
st.success(f"Uploaded {uploaded_file.name} as '{source_name}'.")
|
809 |
+
except Exception as e:
|
810 |
+
st.error(f"Error loading dataset: {e}")
|
811 |
+
|
812 |
+
def handle_sql_database():
|
813 |
+
"""Handles SQL database connections."""
|
814 |
+
conn_str = st.text_input("Enter connection string for SQL DB")
|
815 |
+
if conn_str:
|
816 |
+
source_name = st.text_input("Data Source Name")
|
817 |
+
if source_name:
|
818 |
+
try:
|
819 |
+
sql_source = DatabaseSource(connection_string=conn_str, database_type="sql")
|
820 |
+
st.session_state.data_ingestion.add_source(source_name, sql_source)
|
821 |
+
st.success(f"Added SQL DB Source '{source_name}'.")
|
822 |
+
except Exception as e:
|
823 |
+
st.error(f"Error loading database source: {e}")
|
824 |
+
|
825 |
+
def ingest_data_action():
|
826 |
+
"""Performs data ingestion from the selected source."""
|
827 |
+
if st.session_state.data_ingestion.sources:
|
828 |
+
source_name_to_fetch = st.selectbox("Select Data Source to Ingest", list(st.session_state.data_ingestion.sources.keys()))
|
829 |
+
query = st.text_area("Optional Query to Fetch data")
|
830 |
+
if source_name_to_fetch:
|
831 |
+
with st.spinner("Ingesting data..."):
|
832 |
+
try:
|
833 |
+
data = st.session_state.data_ingestion.ingest_data(source_name_to_fetch, query)
|
834 |
+
st.session_state.data[source_name_to_fetch] = data
|
835 |
+
st.success(f"Ingested data from '{source_name_to_fetch}'.")
|
836 |
+
except Exception as e:
|
837 |
+
st.error(f"Ingestion failed: {e}")
|
838 |
+
else:
|
839 |
+
st.error("No data source added. Please add a data source.")
|
840 |
+
|
841 |
+
def dataset_metadata_section():
|
842 |
+
"""Displays metadata for the selected dataset."""
|
843 |
+
st.subheader("Dataset Metadata")
|
844 |
+
data_source_keys = list(st.session_state.data.keys())
|
845 |
+
selected_data_key = st.selectbox("Select Dataset", data_source_keys)
|
846 |
+
|
847 |
+
if selected_data_key:
|
848 |
+
data = st.session_state.data[selected_data_key]
|
849 |
+
metadata = {
|
850 |
+
"Variables": list(data.columns),
|
851 |
+
"Time Range": {
|
852 |
+
col: {
|
853 |
+
"min": data[col].min(),
|
854 |
+
"max": data[col].max()
|
855 |
+
} for col in data.select_dtypes(include='datetime').columns
|
856 |
+
},
|
857 |
+
"Size": f"{data.memory_usage().sum() / 1e6:.2f} MB"
|
858 |
+
}
|
859 |
+
st.json(metadata)
|
860 |
+
# Store the selected dataset key in session state for use in analysis
|
861 |
+
st.session_state.selected_data_key = selected_data_key
|
862 |
+
|
863 |
+
def main_tabs_section():
|
864 |
+
"""Creates and manages the main tabs in the application."""
|
865 |
+
analysis_tab, clinical_logic_tab, insights_tab, reports_tab, knowledge_tab = st.tabs([
|
866 |
+
"Data Analysis",
|
867 |
+
"Clinical Logic",
|
868 |
+
"Insights",
|
869 |
+
"Reports",
|
870 |
+
"Medical Knowledge"
|
871 |
+
])
|
872 |
+
|
873 |
+
with analysis_tab:
|
874 |
+
data_analysis_section()
|
875 |
+
|
876 |
+
with clinical_logic_tab:
|
877 |
+
clinical_logic_section()
|
878 |
+
|
879 |
+
with insights_tab:
|
880 |
+
insights_section()
|
881 |
+
|
882 |
+
with reports_tab:
|
883 |
+
reports_section()
|
884 |
+
|
885 |
+
with knowledge_tab:
|
886 |
+
medical_knowledge_section()
|
887 |
+
|
888 |
+
def data_analysis_section():
|
889 |
+
"""Handles the Data Analysis tab."""
|
890 |
+
selected_data_key = st.session_state.get('selected_data_key', None)
|
891 |
+
if not selected_data_key:
|
892 |
+
st.warning("Please select a dataset from the metadata section.")
|
893 |
+
return
|
894 |
+
|
895 |
+
data = st.session_state.data[selected_data_key]
|
896 |
+
analysis_type = st.selectbox("Select Analysis Mode", [
|
897 |
+
"Exploratory Data Analysis",
|
898 |
+
"Temporal Pattern Analysis",
|
899 |
+
"Comparative Statistics",
|
900 |
+
"Distribution Analysis",
|
901 |
+
"Train Logistic Regression Model"
|
902 |
+
])
|
903 |
+
|
904 |
+
if analysis_type == "Exploratory Data Analysis":
|
905 |
+
perform_eda(data)
|
906 |
+
elif analysis_type == "Temporal Pattern Analysis":
|
907 |
+
perform_temporal_analysis(data)
|
908 |
+
elif analysis_type == "Comparative Statistics":
|
909 |
+
perform_comparative_statistics(data)
|
910 |
+
elif analysis_type == "Distribution Analysis":
|
911 |
+
perform_distribution_analysis(data)
|
912 |
+
elif analysis_type == "Train Logistic Regression Model":
|
913 |
+
perform_logistic_regression_training(data)
|
914 |
+
|
915 |
+
def perform_eda(data: pd.DataFrame):
|
916 |
+
"""Performs Exploratory Data Analysis."""
|
917 |
+
analyzer = AdvancedEDA()
|
918 |
+
eda_result = analyzer.invoke(data=data)
|
919 |
+
st.subheader("Data Quality Report")
|
920 |
+
st.json(eda_result)
|
921 |
+
|
922 |
+
def perform_temporal_analysis(data: pd.DataFrame):
|
923 |
+
"""Performs Temporal Pattern Analysis."""
|
924 |
+
time_cols = data.select_dtypes(include='datetime').columns
|
925 |
+
num_cols = data.select_dtypes(include=np.number).columns
|
926 |
+
|
927 |
+
if len(time_cols) == 0:
|
928 |
+
st.warning("No datetime columns available for temporal analysis.")
|
929 |
+
return
|
930 |
+
|
931 |
+
time_col = st.selectbox("Select Temporal Variable", time_cols)
|
932 |
+
value_col = st.selectbox("Select Analysis Variable", num_cols)
|
933 |
+
|
934 |
+
if time_col and value_col:
|
935 |
+
analyzer = TemporalAnalyzer()
|
936 |
+
result = analyzer.invoke(data=data, time_col=time_col, value_col=value_col)
|
937 |
+
if "visualization" in result and result["visualization"]:
|
938 |
+
st.image(f"data:image/png;base64,{result['visualization']}", use_column_width=True)
|
939 |
+
st.json(result)
|
940 |
+
|
941 |
+
def perform_comparative_statistics(data: pd.DataFrame):
|
942 |
+
"""Performs Comparative Statistics."""
|
943 |
+
categorical_cols = data.select_dtypes(include=['category', 'object']).columns
|
944 |
+
numeric_cols = data.select_dtypes(include=np.number).columns
|
945 |
+
|
946 |
+
if len(categorical_cols) == 0:
|
947 |
+
st.warning("No categorical columns available for hypothesis testing.")
|
948 |
+
return
|
949 |
+
|
950 |
+
if len(numeric_cols) == 0:
|
951 |
+
st.warning("No numerical columns available for hypothesis testing.")
|
952 |
+
return
|
953 |
+
|
954 |
+
group_col = st.selectbox("Select Grouping Variable", categorical_cols)
|
955 |
+
value_col = st.selectbox("Select Metric Variable", numeric_cols)
|
956 |
+
|
957 |
+
if group_col and value_col:
|
958 |
+
analyzer = HypothesisTester()
|
959 |
+
result = analyzer.invoke(data=data, group_col=group_col, value_col=value_col)
|
960 |
+
st.subheader("Statistical Test Results")
|
961 |
+
st.json(result)
|
962 |
+
|
963 |
+
def perform_distribution_analysis(data: pd.DataFrame):
|
964 |
+
"""Performs Distribution Analysis."""
|
965 |
+
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
966 |
+
selected_cols = st.multiselect("Select Variables for Distribution Analysis", numeric_cols)
|
967 |
+
|
968 |
+
if selected_cols:
|
969 |
+
analyzer = DistributionVisualizer()
|
970 |
+
img_data = analyzer.invoke(data=data, columns=selected_cols)
|
971 |
+
if not img_data.startswith("Visualization Error"):
|
972 |
+
st.image(f"data:image/png;base64,{img_data}", use_column_width=True)
|
973 |
+
else:
|
974 |
+
st.error(img_data)
|
975 |
+
else:
|
976 |
+
st.info("Please select at least one numerical column to visualize.")
|
977 |
+
|
978 |
+
def perform_logistic_regression_training(data: pd.DataFrame):
|
979 |
+
"""Trains a Logistic Regression model."""
|
980 |
+
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
981 |
+
target_col = st.selectbox("Select Target Variable", data.columns.tolist())
|
982 |
+
selected_cols = st.multiselect("Select Feature Variables", numeric_cols)
|
983 |
+
|
984 |
+
if selected_cols and target_col:
|
985 |
+
analyzer = LogisticRegressionTrainer()
|
986 |
+
result = analyzer.invoke(data=data, target_col=target_col, columns=selected_cols)
|
987 |
+
st.subheader("Logistic Regression Model Results")
|
988 |
+
st.json(result)
|
989 |
+
else:
|
990 |
+
st.warning("Please select both target and feature variables for model training.")
|
991 |
+
|
992 |
+
def clinical_logic_section():
|
993 |
+
"""Handles the Clinical Logic tab."""
|
994 |
+
st.header("Clinical Logic")
|
995 |
+
|
996 |
+
# Clinical Rules Management
|
997 |
+
st.subheader("Clinical Rules")
|
998 |
+
rule_name = st.text_input("Enter Rule Name")
|
999 |
+
condition = st.text_area("Enter Rule Condition (use 'df' for DataFrame)",
|
1000 |
+
help="Example: df['blood_pressure'] > 140")
|
1001 |
+
action = st.text_area("Enter Action to be Taken on Rule Match")
|
1002 |
+
severity = st.selectbox("Enter Severity for the Rule", ["low", "medium", "high"])
|
1003 |
+
|
1004 |
+
if st.button("Add Clinical Rule"):
|
1005 |
+
if rule_name and condition and action and severity:
|
1006 |
+
try:
|
1007 |
+
rule = ClinicalRule(
|
1008 |
+
name=rule_name,
|
1009 |
+
condition=condition,
|
1010 |
+
action=action,
|
1011 |
+
severity=severity
|
1012 |
+
)
|
1013 |
+
st.session_state.clinical_rules.add_rule(rule)
|
1014 |
+
st.success("Added Clinical Rule successfully.")
|
1015 |
+
except Exception as e:
|
1016 |
+
st.error(f"Error in rule definition: {e}")
|
1017 |
+
else:
|
1018 |
+
st.error("Please fill in all fields to add a clinical rule.")
|
1019 |
+
|
1020 |
+
# Clinical KPI Management
|
1021 |
+
st.subheader("Clinical KPI Definition")
|
1022 |
+
kpi_name = st.text_input("Enter KPI Name")
|
1023 |
+
kpi_calculation = st.text_area("Enter KPI Calculation (use 'df' for DataFrame)",
|
1024 |
+
help="Example: df['patient_count'].sum()")
|
1025 |
+
threshold = st.text_input("Enter Threshold for KPI (Optional)", help="Leave blank if not applicable")
|
1026 |
+
|
1027 |
+
if st.button("Add Clinical KPI"):
|
1028 |
+
if kpi_name and kpi_calculation:
|
1029 |
+
try:
|
1030 |
+
threshold_value = float(threshold) if threshold else None
|
1031 |
+
kpi = ClinicalKPI(
|
1032 |
+
name=kpi_name,
|
1033 |
+
calculation=kpi_calculation,
|
1034 |
+
threshold=threshold_value
|
1035 |
+
)
|
1036 |
+
st.session_state.kpi_monitoring.add_kpi(kpi)
|
1037 |
+
st.success(f"Added KPI '{kpi_name}' successfully.")
|
1038 |
+
except ValueError:
|
1039 |
+
st.error("Threshold must be a numeric value.")
|
1040 |
+
except Exception as e:
|
1041 |
+
st.error(f"Error creating KPI: {e}")
|
1042 |
+
else:
|
1043 |
+
st.error("Please provide both KPI name and calculation.")
|
1044 |
+
|
1045 |
+
# Execute Clinical Rules and Calculate KPIs
|
1046 |
+
selected_data_key = st.selectbox("Select Dataset for Clinical Logic", list(st.session_state.data.keys()))
|
1047 |
+
if selected_data_key:
|
1048 |
+
data = st.session_state.data[selected_data_key]
|
1049 |
+
if st.button("Execute Clinical Rules"):
|
1050 |
+
with st.spinner("Executing Clinical Rules..."):
|
1051 |
+
result = st.session_state.clinical_rules.execute_rules(data)
|
1052 |
+
st.json(result)
|
1053 |
+
if st.button("Calculate Clinical KPIs"):
|
1054 |
+
with st.spinner("Calculating Clinical KPIs..."):
|
1055 |
+
result = st.session_state.kpi_monitoring.calculate_kpis(data)
|
1056 |
+
st.json(result)
|
1057 |
+
else:
|
1058 |
+
st.warning("Please ingest data to execute clinical rules and calculate KPIs.")
|
1059 |
+
|
1060 |
+
def insights_section():
|
1061 |
+
"""Handles the Insights tab."""
|
1062 |
+
st.header("Automated Insights")
|
1063 |
+
|
1064 |
+
selected_data_key = st.selectbox("Select Dataset for Insights", list(st.session_state.data.keys()))
|
1065 |
+
if not selected_data_key:
|
1066 |
+
st.warning("Please select a dataset to generate insights.")
|
1067 |
+
return
|
1068 |
+
|
1069 |
+
data = st.session_state.data[selected_data_key]
|
1070 |
+
available_analyses = ["EDA", "temporal", "distribution", "hypothesis", "model"]
|
1071 |
+
selected_analyses = st.multiselect("Select Analyses for Insights", available_analyses)
|
1072 |
+
|
1073 |
+
if st.button("Generate Automated Insights"):
|
1074 |
+
if selected_analyses:
|
1075 |
+
with st.spinner("Generating Insights..."):
|
1076 |
+
results = st.session_state.automated_insights.generate_insights(
|
1077 |
+
data, analysis_names=selected_analyses
|
1078 |
+
)
|
1079 |
+
st.json(results)
|
1080 |
+
else:
|
1081 |
+
st.warning("Please select at least one analysis to generate insights.")
|
1082 |
+
|
1083 |
+
# Diagnosis Support
|
1084 |
+
st.subheader("Diagnosis Support")
|
1085 |
+
target_col = st.selectbox("Select Target Variable for Diagnosis", data.columns.tolist())
|
1086 |
+
numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
|
1087 |
+
selected_feature_cols = st.multiselect("Select Feature Variables for Diagnosis", numeric_cols)
|
1088 |
+
|
1089 |
+
if st.button("Generate Diagnosis"):
|
1090 |
+
if target_col and selected_feature_cols:
|
1091 |
+
with st.spinner("Generating Diagnosis..."):
|
1092 |
+
result = st.session_state.diagnosis_support.diagnose(
|
1093 |
+
data, target_col=target_col, columns=selected_feature_cols, diagnosis_key="diagnosis_result"
|
1094 |
+
)
|
1095 |
+
st.json(result)
|
1096 |
+
else:
|
1097 |
+
st.error("Please select both target and feature variables for diagnosis.")
|
1098 |
+
|
1099 |
+
# Treatment Recommendation
|
1100 |
+
st.subheader("Treatment Recommendation")
|
1101 |
+
condition_col = st.selectbox("Select Condition Column for Treatment Recommendation", data.columns.tolist())
|
1102 |
+
treatment_col = st.selectbox("Select Treatment Column for Treatment Recommendation", data.columns.tolist())
|
1103 |
+
|
1104 |
+
if st.button("Generate Treatment Recommendation"):
|
1105 |
+
if condition_col and treatment_col:
|
1106 |
+
with st.spinner("Generating Treatment Recommendation..."):
|
1107 |
+
result = st.session_state.treatment_recommendation.recommend(
|
1108 |
+
data, condition_col=condition_col, treatment_col=treatment_col, recommendation_key="treatment_recommendation"
|
1109 |
+
)
|
1110 |
+
st.json(result)
|
1111 |
+
else:
|
1112 |
+
st.error("Please select both condition and treatment columns.")
|
1113 |
+
|
1114 |
+
def reports_section():
|
1115 |
+
"""Handles the Reports tab."""
|
1116 |
+
st.header("Automated Reports")
|
1117 |
+
|
1118 |
+
# Create Report Definition
|
1119 |
+
st.subheader("Create Report Definition")
|
1120 |
+
report_name = st.text_input("Report Name")
|
1121 |
+
report_def = st.text_area("Report Definition", help="Describe the structure and content of the report.")
|
1122 |
+
|
1123 |
+
if st.button("Create Report Definition"):
|
1124 |
+
if report_name and report_def:
|
1125 |
+
st.session_state.automated_reports.create_report_definition(report_name, report_def)
|
1126 |
+
st.success("Report definition created successfully.")
|
1127 |
+
else:
|
1128 |
+
st.error("Please provide both report name and definition.")
|
1129 |
+
|
1130 |
+
# Generate Report
|
1131 |
+
st.subheader("Generate Report")
|
1132 |
+
report_names = list(st.session_state.automated_reports.report_definitions.keys())
|
1133 |
+
if report_names:
|
1134 |
+
report_name_to_generate = st.selectbox("Select Report to Generate", report_names)
|
1135 |
+
if st.button("Generate Report"):
|
1136 |
+
with st.spinner("Generating Report..."):
|
1137 |
+
report = st.session_state.automated_reports.generate_report(report_name_to_generate, st.session_state.data)
|
1138 |
+
if "error" not in report:
|
1139 |
+
st.header(f"Report: {report['Report Name']}")
|
1140 |
+
st.markdown(f"**Definition:** {report['Report Definition']}")
|
1141 |
+
for df_name, df_content in report["Data"].items():
|
1142 |
+
st.subheader(f"Data: {df_name}")
|
1143 |
+
st.dataframe(pd.DataFrame(df_content))
|
1144 |
+
else:
|
1145 |
+
st.error(report["error"])
|
1146 |
+
else:
|
1147 |
+
st.info("No report definitions found. Please create a report definition first.")
|
1148 |
+
|
1149 |
+
def medical_knowledge_section():
|
1150 |
+
"""Handles the Medical Knowledge tab."""
|
1151 |
+
st.header("Medical Knowledge")
|
1152 |
+
query = st.text_input("Enter your medical question here:")
|
1153 |
+
|
1154 |
+
if st.button("Search"):
|
1155 |
+
if query.strip():
|
1156 |
+
with st.spinner("Searching..."):
|
1157 |
+
result = st.session_state.knowledge_base.search_medical_info(
|
1158 |
+
query, pub_email=st.session_state.pub_email
|
1159 |
+
)
|
1160 |
+
st.markdown(result)
|
1161 |
+
else:
|
1162 |
+
st.error("Please enter a medical question to search.")
|
1163 |
+
|
1164 |
+
if __name__ == "__main__":
|
1165 |
+
main()
|