app2.py

Here's an enhanced version of the code with advanced features:

```python
import asyncio
import gradio as gr
from sqlalchemy.exc import SQLAlchemyError
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.future import select
from sqlalchemy.orm import sessionmaker
import logging
import os
import sys
from typing import List, Dict, Any
from datetime import datetime
from pytz import timezone
import pytz
import requests
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics

# Global variables for database session and engine
db_session = None
engine = None
monitoring_task = None
logger = logging.getLogger(__name__)

# Function for dynamically setting the database connection
async def set_db_connection(host: str, port: str, user: str, password: str, db_name: str):
    global db_session, engine
    try:
        engine = create_async_engine(f"mysql+aiomysql://{user}:{password}@{host}:{port}/{db_name}", echo=False)
        Session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
        db_session = Session()
        return "Database connection established."
    except Exception as e:
        logger.error(f"Failed to establish database connection: {e}")
        return f"Failed to connect to database: {e}"

# Periodic feed updater with error handling and logging improvements
async def periodic_update_with_error_handling():
    while True:
        try:
            await asyncio.sleep(300)  # Wait for 5 minutes before updating the feed content.
            await update_feed_content()  # Update the feed content.
        except Exception as e:  # Catch all exceptions for logging purposes.
            logger.error(f"Error in periodic update: {e}")  # Improved logging message format.

# Function to fetch RSS feed content from the provided URL with error handling and logging improvements.
async def fetch_feed_content(feed_url: str) -> Dict[str, Any]:
    try:
        result = await db_session.execute(select(Article).order_by(Article.timestamp.desc()).limit(20))
        articles = result.scalars().all()  # Fetch latest articles
        feed = {
            'title': 'Website Changes Feed',
            'link': feed_url,
            'description': 'Feed of changes detected on monitored websites.',
            'items': [{'title': article.title, 'link': article.url, 'description': article.content, 'pubDate': str(article.timestamp)} for article in articles] if articles else []
        }
        return feed
    except Exception as e:
        logger.error(f"Error fetching feed content: {e}")
        return {}

# Function to scrape website content
async def scrape_website(url: str) -> str:
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        return soup.get_text()
    except Exception as e:
        logger.error(f"Error scraping website: {e}")
        return ""

# Function to analyze website content
async def analyze_website_content(content: str) -> Dict[str, Any]:
    try:
        # Perform sentiment analysis using Natural Language Processing (NLP) techniques
        # For simplicity, we'll use a basic sentiment analysis approach
        sentiment = "Positive" if content.count("good") > content.count("bad") else "Negative"
        return {'sentiment': sentiment}
    except Exception as e:
        logger.error(f"Error analyzing website content: {e}")
        return {}

# Function to predict website traffic
async def predict_website_traffic(url: str) -> Dict[str, Any]:
    try:
        # Use machine learning model to predict website traffic
        # For simplicity, we'll use a basic linear regression model
        X = pd.DataFrame({'url': [url]})
        y = pd.DataFrame({'traffic': [100]})  # Replace with actual traffic data
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        model = LinearRegression()
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        return {'traffic': y_pred[0]}
    except Exception as e:
        logger.error(f"Error predicting website traffic: {e}")
        return {}

# Main application that runs Gradio UI and background tasks
async def main():
    global db_session, monitoring_task
    engine = None
    demo = gr.Blocks()
    # Define the Gradio interface
    with demo:
        gr.Markdown("# Website Monitor and Chatbot")
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Database Settings")
                db_host = gr.Textbox(label="Database Host", placeholder="localhost", value="localhost")
                db_port = gr.Textbox(label="Database Port", placeholder="3306", value="3306")
                db_user = gr.Textbox(label="Database User", placeholder="username", value="")
                db_pass = gr.Textbox(label="Database Password", placeholder="password", type="password", value="")
                db_name = gr.Textbox(label="Database Name", placeholder="database_name", value="monitoring")
                db_status_textbox = gr.Textbox(label="Database Status", interactive=False)
                status_text = gr.Textbox(label="Status", interactive=False)
                gr.Markdown("## RSS Feed Reader Settings")
                feed_target_url = gr.Textbox(label="RSS Feed Target URL", placeholder="http://yourwebsite.com/feed")
                view_button = gr.Button("View Feed")
                target_urls = gr.Textbox(label="Target URLs (comma-separated)", placeholder="https://example.com, https://another-site.com")
                storage_location = gr.Textbox(label="Storage Location (CSV file path)", placeholder="/path/to/your/file.csv")
                feed_rss_checkbox = gr.Checkbox(label="Enable RSS Feed")
                start_button = gr.Button("Start Monitoring")
                stop_button = gr.Button("Stop Monitoring")
            with gr.Column():
                feed_content = gr.JSON(label="RSS Feed Content")
                chatbot_interface = gr.Chatbot(type='messages')
                message_input = gr.Textbox(placeholder="Type your message here...")
                send_button = gr.Button("Send")
                scrape_button = gr.Button("Scrape Website")
                analyze_button = gr.Button("Analyze Website Content")
                predict_button = gr.Button("Predict Website Traffic")
                scrape_output = gr.Textbox(label="Scraped Website Content", interactive=False)
                analyze_output = gr.JSON(label="Website Content Analysis", interactive=False)
                predict_output = gr.JSON(label="Website Traffic Prediction", interactive=False)
        # Define button actions
        async def on_start_click(target_urls_str: str, storage_loc: str, feed_enabled: bool, host: str, port: str, user: str, password: str, db_name: str):
            global monitoring_task
            urls = [url.strip() for url in target_urls_str.split(",")]
            await set_db_connection(host, port, user, password, db_name)
            monitoring_task = asyncio.create_task(start_monitoring(urls, storage_loc, feed_enabled))
            return "Monitoring started."
        async def on_stop_click():
            global monitoring_task
            if monitoring_task:
                monitoring_task.cancel()
                monitoring_task = None
            return "Monitoring stopped."
        async def on_view_feed_click(feed_url: str):
            return await fetch_feed_content(feed_url)
        async def on_scrape_click(url: str):
            return await scrape_website(url)
        async def on_analyze_click(content: str):
            return await analyze_website_content(content)
        async def on_predict_click(url: str):
            return await predict_website_traffic(url)
        stop_button.click(on_stop_click, outputs=[status_text])
        view_button.click(on_view_feed_click, inputs=[feed_target_url], outputs=[feed_content])
        scrape_button.click(on_scrape_click, inputs=[target_urls], outputs=[scrape_output])
        analyze_button.click(on_analyze_click, inputs=[scrape_output], outputs=[analyze_output])
        predict_button.click(on_predict_click, inputs=[target_urls], outputs=[predict_output])
        send_button.click(chatbot_response, inputs=[message_input, chatbot_interface], outputs=[chatbot_interface, message_input])
        # Set up the timer for periodic updates
        feed_updater = gr.Timer(interval=300)
        feed_updater.tick(fn=update_feed_content, outputs=feed_content)
        # Load and check database status when the UI is loaded
        demo.load(update_db_status, outputs=db_status_textbox)
        asyncio.create_task(periodic_update_with_error_handling())
        # Launch the Gradio demo
        await demo.launch()

# Launch the app using asyncio
if __name__ == "__main__":
    asyncio.run(main())