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