Update app2.py

app2.py

@@ -1,3 +1,6 @@
+Here's an enhanced version of the code with advanced features:
+
+```python
 import asyncio
 import gradio as gr
 from sqlalchemy.exc import SQLAlchemyError
@@ -8,6 +11,17 @@ 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
@@ -52,6 +66,43 @@ async def fetch_feed_content(feed_url: str) -> Dict[str, Any]:
         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
@@ -83,6 +134,12 @@ async def main():
                 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
@@ -98,8 +155,17 @@ async def main():
             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)