Update app.py

app.py

@@ -6,447 +6,153 @@ 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)
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 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
+from scipy.optimize import 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')
+# Initialize NLTK and spaCy
+nltk.download('punkt', quiet=True)
+nltk.download('vader_lexicon', quiet=True)
+try:
+    nlp = spacy.load("en_core_web_sm")
+except:
+    spacy.cli.download("en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
 
-# 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
+# Configure Gemini
 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,
+    "temperature": 0.4,
+    "top_p": 0.8,
+    "top_k": 50,
+    "max_output_tokens": 2048,
 }
 
 model = genai.GenerativeModel(
-    model_name="gemini-1.5-pro",
+    model_name="gemini-1.5-pro-latest",
     generation_config=generation_config,
-    system_instruction=SYSTEM_INSTRUCTIONS
+    system_instruction="You are Ath, an advanced AI coding assistant. Provide secure, efficient code with clear explanations."
 )
-chat_session = model.start_chat(history=[])
 
-@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
-def generate_response(user_input):
+# Streamlit UI Configuration
+st.set_page_config(page_title="AI Code Assistant", page_icon="💻", layout="wide")
+st.markdown("""
+<style>
+    .main-container {padding: 2rem; border-radius: 10px; background: #f8f9fa;}
+    .code-block {background: #1e1e1e; color: #d4d4d4; padding: 1rem; border-radius: 5px;}
+</style>
+""", unsafe_allow_html=True)
+
+# Core Functions
+@retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
+def generate_response(prompt):
     try:
-        response = chat_session.send_message(user_input)
+        response = model.generate_content(prompt)
         return response.text
     except Exception as e:
-        logging.error(f"Generation error: {str(e)}")
-        return f"Error: {e}"
+        return f"Error: {str(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
+def process_user_input(text):
     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()
+        doc = nlp(text)
+        return {
+            'tokens': [token.text for token in doc],
+            'entities': [(ent.text, ent.label_) for ent in doc.ents],
+            'sentiment': SentimentIntensityAnalyzer().polarity_scores(text)
         }
-    
-    return results
+    except Exception as e:
+        st.error(f"NLP Error: {str(e)}")
+        return text
 
-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)
+def optimize_code(code):
     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)}"
-            )
+        with open("temp.py", "w") as f:
+            f.write(code)
+        result = subprocess.run(["pylint", "temp.py"], capture_output=True, text=True)
+        os.remove("temp.py")
+        return code, result.stdout
     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)
+        return code, f"Optimization Error: {str(e)}"
+
+# Streamlit UI Components
+st.title("💻 AI Code Assistant")
+st.markdown("### Generate, Optimize, and Deploy Code")
+
+task_type = st.selectbox("Select Task Type", [
+    "Code Generation",
+    "Data Analysis",
+    "NLP Processing",
+    "Math Solving"
+])
+
+prompt = st.text_area("Enter your request:", height=150)
+
+if st.button("Generate Solution"):
+    if not prompt.strip():
+        st.error("Please enter a valid prompt")
+    else:
+        with st.spinner("Processing..."):
+            try:
+                # Process input
+                processed = process_user_input(prompt)
+                
+                # Generate response
+                response = generate_response(prompt)
+                
+                # Display results
+                with st.expander("Generated Solution", expanded=True):
+                    if task_type == "Code Generation":
+                        optimized, lint = optimize_code(response)
+                        st.code(optimized, language='python')
+                        st.write("Code Analysis:")
+                        st.text(lint[:1000])  # Show first 1000 chars
                         
-                        # Display results in tabs
-                        tab1, tab2, tab3 = st.tabs(["Solution", "Analysis", "Deployment"])
+                    elif task_type == "Data Analysis":
+                        df = pd.DataFrame(np.random.randn(50, 4), columns=['A','B','C','D'])
+                        st.write("Sample Analysis:")
+                        st.dataframe(df.describe())
+                        fig, ax = plt.subplots()
+                        df.plot.kde(ax=ax)
+                        st.pyplot(fig)
                         
-                        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)
+                    elif task_type == "NLP Processing":
+                        st.json(processed)
                         
-                        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
+                    elif task_type == "Math Solving":
+                        solution = sp.solve(prompt)
+                        st.latex(f"Solution: {solution}")
                         
-                        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
+                st.success("Processing complete!")
+                
+            except Exception as e:
+                st.error(f"Error: {str(e)}")
+
+# Sidebar Utilities
+st.sidebar.header("Tools")
+if st.sidebar.button("Clear Cache"):
+    st.cache_data.clear()
+    st.success("Cache cleared!")
+
+st.sidebar.markdown("""
+---
+**About**  
+AI Code Assistant v2.0  
+Powered by Gemini Pro  
+[GitHub Repo](https://github.com/your-repo)
+""")