Create app.py

app.py

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+import streamlit as st
+import google.generativeai as genai
+import requests
+import subprocess
+import os
+import pylint
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.metrics import (accuracy_score, precision_score, 
+                            recall_score, f1_score, confusion_matrix)
+import git
+import spacy
+from spacy.lang.en import English
+import boto3
+import unittest
+import docker
+import sympy as sp
+from scipy.optimize import minimize, differential_evolution
+import matplotlib.pyplot as plt
+import seaborn as sns
+from IPython.display import display
+from tenacity import retry, stop_after_attempt, wait_fixed
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from transformers import (AutoTokenizer, AutoModel,
+                          pipeline, set_seed)
+import networkx as nx
+from sklearn.cluster import KMeans
+from scipy.stats import ttest_ind
+from statsmodels.tsa.arima.model import ARIMA
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+import cv2
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+import logging
+from logging.handlers import RotatingFileHandler
+import platform
+import psutil
+import yaml
+import json
+import black
+import flake8.main.application
+
+# Initialize NLTK resources
+nltk.download('punkt')
+nltk.download('vader_lexicon')
+
+# Configure logging
+log_handler = RotatingFileHandler('app.log', maxBytes=1e6, backupCount=5)
+logging.basicConfig(
+    handlers=[log_handler],
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+# Configure the Gemini API
+genai.configure(api_key=st.secrets["GOOGLE_API_KEY"])
+
+# Enhanced system instructions with security and best practices
+SYSTEM_INSTRUCTIONS = """
+You are Ath, an ultra-advanced AI code assistant with expertise across multiple domains. Follow these guidelines:
+1. Generate secure, efficient, and maintainable code
+2. Implement industry best practices and design patterns
+3. Include proper error handling and logging
+4. Optimize for performance and scalability
+5. Add detailed documentation and type hints
+6. Suggest relevant libraries and frameworks
+7. Consider security implications and vulnerabilities
+8. Provide test cases and benchmarking
+9. Support multiple programming languages when applicable
+10. Follow PEP8 and other relevant style guides
+"""
+
+# Create the model with enhanced configuration
+generation_config = {
+    "temperature": 0.35,
+    "top_p": 0.85,
+    "top_k": 40,
+    "max_output_tokens": 8192,
+}
+
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-pro",
+    generation_config=generation_config,
+    system_instruction=SYSTEM_INSTRUCTIONS
+)
+chat_session = model.start_chat(history=[])
+
+@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
+def generate_response(user_input):
+    try:
+        response = chat_session.send_message(user_input)
+        return response.text
+    except Exception as e:
+        logging.error(f"Generation error: {str(e)}")
+        return f"Error: {e}"
+
+def optimize_code(code):
+    """Perform comprehensive code optimization and linting"""
+    with open("temp_code.py", "w") as file:
+        file.write(code)
+    
+    # Run multiple code quality tools
+    tools = {
+        'pylint': ["pylint", "temp_code.py"],
+        'flake8': ["flake8", "temp_code.py"],
+        'black': ["black", "--check", "temp_code.py"]
+    }
+    
+    results = {}
+    for tool, cmd in tools.items():
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        results[tool] = {
+            'output': result.stdout + result.stderr,
+            'status': result.returncode
+        }
+    
+    # Format code with black
+    try:
+        formatted_code = black.format_file_contents(
+            code, mode=black.FileMode()
+        )
+        code = formatted_code
+    except Exception as e:
+        logging.warning(f"Black formatting failed: {str(e)}")
+    
+    os.remove("temp_code.py")
+    return code, results
+
+def train_advanced_ml_model(X, y):
+    """Enhanced ML training with hyperparameter tuning"""
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, stratify=y
+    )
+    
+    param_grid = {
+        'RandomForest': {
+            'n_estimators': [100, 200],
+            'max_depth': [None, 10, 20],
+            'min_samples_split': [2, 5]
+        },
+        'GradientBoosting': {
+            'n_estimators': [100, 200],
+            'learning_rate': [0.1, 0.05],
+            'max_depth': [3, 5]
+        }
+    }
+    
+    models = {
+        'RandomForest': RandomForestClassifier(random_state=42),
+        'GradientBoosting': GradientBoostingClassifier(random_state=42)
+    }
+    
+    results = {}
+    for name, model in models.items():
+        grid_search = GridSearchCV(
+            model,
+            param_grid[name],
+            cv=5,
+            n_jobs=-1,
+            scoring='f1_weighted'
+        )
+        grid_search.fit(X_train, y_train)
+        
+        best_model = grid_search.best_estimator_
+        y_pred = best_model.predict(X_test)
+        
+        results[name] = {
+            'best_params': grid_search.best_params_,
+            'accuracy': accuracy_score(y_test, y_pred),
+            'precision': precision_score(y_test, y_pred, average='weighted'),
+            'recall': recall_score(y_test, y_pred, average='weighted'),
+            'f1': f1_score(y_test, y_pred, average='weighted'),
+            'confusion_matrix': confusion_matrix(y_test, y_pred).tolist()
+        }
+    
+    return results
+
+def handle_error(error):
+    """Enhanced error handling with logging and notifications"""
+    st.error(f"An error occurred: {error}")
+    logging.error(f"User-facing error: {str(error)}")
+    
+    # Send notification to admin (example with AWS SNS)
+    try:
+        if st.secrets.get("AWS_CREDENTIALS"):
+            client = boto3.client(
+                'sns',
+                aws_access_key_id=st.secrets["AWS_CREDENTIALS"]["access_key"],
+                aws_secret_access_key=st.secrets["AWS_CREDENTIALS"]["secret_key"],
+                region_name='us-east-1'
+            )
+            client.publish(
+                TopicArn=st.secrets["AWS_CREDENTIALS"]["sns_topic"],
+                Message=f"Code Assistant Error: {str(error)}"
+            )
+    except Exception as e:
+        logging.error(f"Error notification failed: {str(e)}")
+
+def visualize_complex_data(data):
+    """Enhanced visualization with interactive elements"""
+    df = pd.DataFrame(data)
+    
+    # Create interactive Plotly figures
+    fig = px.scatter_matrix(df)
+    fig.update_layout(
+        title='Interactive Scatter Matrix',
+        width=1200,
+        height=800
+    )
+    
+    # Add 3D visualization
+    if df.shape[1] >= 3:
+        fig_3d = px.scatter_3d(
+            df,
+            x=df.columns[0],
+            y=df.columns[1],
+            z=df.columns[2],
+            title='3D Data Visualization'
+        )
+        return [fig, fig_3d]
+    
+    return [fig]
+
+def perform_nlp_analysis(text):
+    """Enhanced NLP analysis with transformer models"""
+    # Basic spaCy analysis
+    nlp = spacy.load("en_core_web_trf")
+    doc = nlp(text)
+    
+    # Transformer-based sentiment analysis
+    sentiment_analyzer = pipeline(
+        "sentiment-analysis",
+        model="distilbert-base-uncased-finetuned-sst-2-english"
+    )
+    
+    # Text summarization
+    summarizer = pipeline("summarization", model="t5-small")
+    
+    return {
+        'entities': [(ent.text, ent.label_) for ent in doc.ents],
+        'syntax': [(token.text, token.dep_) for token in doc],
+        'sentiment': sentiment_analyzer(text),
+        'summary': summarizer(text, max_length=50, min_length=25),
+        'transformer_embeddings': doc._.trf_data.tensors[-1].tolist()
+    }
+
+# Enhanced Streamlit UI Components
+st.set_page_config(
+    page_title="Ultra AI Code Assistant Pro",
+    page_icon="🚀",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for improved styling
+st.markdown("""
+<style>
+    .main-container {
+        background-color: #f8f9fa;
+        padding: 2rem;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    }
+    .code-block {
+        background-color: #1e1e1e;
+        color: #d4d4d4;
+        padding: 1rem;
+        border-radius: 5px;
+        margin: 1rem 0;
+        font-family: 'Fira Code', monospace;
+    }
+    .stButton>button {
+        background: linear-gradient(45deg, #4CAF50, #45a049);
+        color: white;
+        border: none;
+        padding: 0.8rem 1.5rem;
+        border-radius: 25px;
+        font-weight: bold;
+        transition: transform 0.2s;
+    }
+    .stButton>button:hover {
+        transform: scale(1.05);
+    }
+    .feature-card {
+        background: white;
+        padding: 1.5rem;
+        border-radius: 10px;
+        margin: 1rem 0;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Main UI Layout
+st.title("🚀 Ultra AI Code Assistant Pro")
+st.markdown("""
+<div class="main-container">
+    <p class="subtitle">Next-Generation AI-Powered Development Environment</p>
+</div>
+""", unsafe_allow_html=True)
+
+# Split layout into main content and sidebar
+main_col, sidebar_col = st.columns([3, 1])
+
+with main_col:
+    task_type = st.selectbox("Select Task Type", [
+        "Code Generation", 
+        "ML Pipeline Development",
+        "Data Science Analysis",
+        "NLP Processing",
+        "Computer Vision",
+        "Cloud Deployment",
+        "Performance Optimization"
+    ], key='task_type')
+    
+    prompt = st.text_area("Describe your task in detail:", height=150,
+                         placeholder="Enter your requirements here...")
+    
+    if st.button("Generate Solution", key="main_generate"):
+        if not prompt.strip():
+            st.error("Please provide detailed requirements")
+        else:
+            with st.spinner("Analyzing requirements and generating solution..."):
+                try:
+                    # Enhanced processing pipeline
+                    processed_input = process_user_input(prompt)
+                    response = generate_response(f"""
+                        Generate comprehensive solution for: {processed_input.text}
+                        Include:
+                        - Architecture design
+                        - Implementation code
+                        - Testing strategy
+                        - Deployment plan
+                        - Monitoring setup
+                    """)
+                    
+                    if "Error" in response:
+                        handle_error(response)
+                    else:
+                        optimized_code, lint_results = optimize_code(response)
+                        
+                        # Display results in tabs
+                        tab1, tab2, tab3 = st.tabs(["Solution", "Analysis", "Deployment"])
+                        
+                        with tab1:
+                            st.subheader("Optimized Solution")
+                            st.code(optimized_code, language='python')
+                            
+                            col1, col2 = st.columns(2)
+                            with col1:
+                                st.download_button(
+                                    label="Download Code",
+                                    data=optimized_code,
+                                    file_name="solution.py",
+                                    mime="text/python"
+                                )
+                            with col2:
+                                if st.button("Generate Documentation"):
+                                    docs = generate_documentation(optimized_code)
+                                    st.markdown(docs)
+                        
+                        with tab2:
+                            st.subheader("Code Quality Report")
+                            for tool, result in lint_results.items():
+                                with st.expander(f"{tool.upper()} Results"):
+                                    st.code(result['output'])
+                            
+                            st.subheader("Performance Metrics")
+                            # Add performance benchmarking here
+                        
+                        with tab3:
+                            st.subheader("Cloud Deployment Options")
+                            # Add cloud deployment widgets here
+                            
+                except Exception as e:
+                    handle_error(e)
+
+with sidebar_col:
+    st.markdown("## Quick Tools")
+    
+    if st.button("Code Review"):
+        # Implement real-time code review
+        pass
+    
+    if st.button("Security Scan"):
+        # Implement security scanning
+        pass
+    
+    st.markdown("## Project Stats")
+    # Add system monitoring
+    st.write(f"CPU Usage: {psutil.cpu_percent()}%")
+    st.write(f"Memory Usage: {psutil.virtual_memory().percent}%")
+    
+    st.markdown("## Recent Activity")
+    # Add activity log display
+    st.write("No recent activity")
+
+# Additional Features
+st.markdown("## Advanced Features")
+features = st.columns(3)
+
+with features[0]:
+    with st.expander("Live Collaboration"):
+        st.write("Real-time collaborative coding features")
+        # Add collaborative editing components
+
+with features[1]:
+    with st.expander("API Generator"):
+        st.write("Generate REST API endpoints from code")
+        # Add OpenAPI/Swagger generation
+
+with features[2]:
+    with st.expander("ML Ops"):
+        st.write("Machine Learning Operations Dashboard")
+        # Add model monitoring components
+
+# System Monitoring Dashboard
+st.markdown("## System Health Monitor")
+sys_cols = st.columns(4)
+sys_cols[0].metric("CPU Load", f"{psutil.cpu_percent()}%")
+sys_cols[1].metric("Memory", f"{psutil.virtual_memory().percent}%")
+sys_cols[2].metric("Disk", f"{psutil.disk_usage('/').percent}%")
+sys_cols[3].metric("Network", f"{psutil.net_io_counters().bytes_sent/1e6:.2f}MB")
+
+# Footer
+st.markdown("""
+<hr>
+<div style="text-align: center; padding: 1rem">
+    <p>Ultra AI Code Assistant Pro v2.0</p>
+    <small>Powered by Gemini 1.5 Pro | Secure and Compliant</small>
+</div>
+""", unsafe_allow_html=True)
+
+# Additional enhancements not shown here would include:
+# - Real-time collaboration features
+# - Jupyter notebook integration
+# - CI/CD pipeline generation
+# - Infrastructure-as-Code templates
+# - Advanced profiling and benchmarking
+# - Multi-language support
+# - Vulnerability scanning integration
+# - Automated documentation generation
+# - Cloud deployment wizards
+# - Team management features