Update main.py

main.py

@@ -1,305 +1,307 @@
-import gradio as gr
-import os
-import sys
-from typing import List, Tuple
-import time
-from datetime import datetime
-
-# Add the src directory to Python path
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-from chatbot import RAGChatbot
-
-class ChatbotUI:
-    def __init__(self):
-        """Initialize the Gradio UI for RAG Chatbot"""
-        print("🚀 Initializing Chatbot UI...")
-        self.chatbot = RAGChatbot()
-        self.chat_history = []
-        
-    def add_url(self, url: str) -> Tuple[str, str]:
-        """
-        Add URL to knowledge base
-        Args:
-            url: URL to ingest
-        Returns:
-            Tuple of (status_message, updated_stats)
-        """
-        if not url or not url.strip():
-            return "❌ Please enter a valid URL", self.get_stats_display()
-        
-        url = url.strip()
-        if not (url.startswith('http://') or url.startswith('https://')):
-            url = 'https://' + url
-        
-        # Show processing message
-        status_msg = f"📥 Processing {url}..."
-        
-        try:
-            result = self.chatbot.ingest_url(url)
-            
-            if result['success']:
-                success_msg = f"""✅ Successfully added: {result['title']}
-📊 Added {result['chunks_added']} chunks ({result['word_count']} words)
-🔗 Source: {url}"""
-                return success_msg, self.get_stats_display()
-            else:
-                error_msg = f"❌ Failed to add URL: {result['message']}"
-                return error_msg, self.get_stats_display()
-                
-        except Exception as e:
-            error_msg = f"❌ Error processing URL: {str(e)}"
-            return error_msg, self.get_stats_display()
-    
-    def chat_response(self, message: str, history: List[List[str]]) -> Tuple[str, List[List[str]]]:
-        """
-        Generate chat response
-        Args:
-            message: User message
-            history: Chat history
-        Returns:
-            Tuple of (empty_string, updated_history)
-        """
-        if not message or not message.strip():
-            return "", history
-        
-        # Get response from chatbot
-        response_data = self.chatbot.chat(message.strip(), include_sources=True)
-        
-        # Format response with sources
-        formatted_response = self.format_response(response_data)
-        
-        # Update history
-        history.append([message, formatted_response])
-        
-        return "", history
-    
-    def format_response(self, response_data: dict) -> str:
-        """Format the chatbot response with sources and timing info"""
-        response = response_data['response']
-        
-        # Add timing information
-        timing_info = f"\n\n⏱️ *Response time: {response_data['total_time']}s*"
-        
-        # Add sources if available
-        if response_data.get('sources'):
-            sources_text = "\n\n📚 **Sources:**\n"
-            for i, source in enumerate(response_data['sources'][:3], 1):  # Limit to top 3 sources
-                score = f"({source['similarity_score']:.3f})" if source['similarity_score'] else ""
-                sources_text += f"{i}. **{source['title']}** {score}\n"
-                sources_text += f"   {source['snippet']}\n"
-                sources_text += f"   🔗 {source['url']}\n\n"
-            
-            response += sources_text
-        
-        response += timing_info
-        return response
-    
-    def get_stats_display(self) -> str:
-        """Get formatted knowledge base statistics"""
-        try:
-            stats = self.chatbot.get_knowledge_base_stats()
-            
-            stats_text = f"""📊 **Knowledge Base Statistics**
-            
-🗄️ **Total Documents:** {stats.get('total_documents', 0)}
-🧠 **AI Model:** {stats.get('model_used', 'Unknown')}
-🔤 **Embedding Model:** {stats.get('embedding_model', 'Unknown')}
-📐 **Vector Dimension:** {stats.get('index_dimension', 0)}
-📈 **Index Fullness:** {stats.get('index_fullness', 0):.1%}
-
-*Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*"""
-            
-            return stats_text
-            
-        except Exception as e:
-            return f"❌ Error getting stats: {str(e)}"
-    
-    def clear_knowledge_base(self) -> Tuple[str, str]:
-        """Clear all documents from knowledge base"""
-        try:
-            success = self.chatbot.clear_knowledge_base()
-            if success:
-                return "✅ Knowledge base cleared successfully!", self.get_stats_display()
-            else:
-                return "❌ Failed to clear knowledge base", self.get_stats_display()
-        except Exception as e:
-            return f"❌ Error clearing knowledge base: {str(e)}", self.get_stats_display()
-    
-    def create_interface(self):
-        """Create and return the Gradio interface"""
-        
-        # Custom CSS for better styling
-        custom_css = """
-        .gradio-container {
-            max-width: 1200px !important;
-        }
-        .chat-container {
-            height: 500px !important;
-        }
-        .input-container {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-            padding: 20px !important;
-            border-radius: 10px !important;
-        }
-        """
-        
-        with gr.Blocks(
-            title="🤖 RAG Chatbot",
-            theme=gr.themes.Soft(),
-            css=custom_css
-        ) as interface:
-            
-            # Header
-            gr.Markdown("""
-            # 🤖 RAG-Powered AI Chatbot
-            ### Intelligent Q&A with Web Content Integration
-            
-            **How to use:**
-            1. 📥 Add URLs containing articles or content you want the bot to learn from
-            2. 💬 Ask questions about the content - the bot will provide accurate answers with sources
-            3. 📊 Monitor your knowledge base statistics in the sidebar
-            """)
-            
-            with gr.Row():
-                # Main chat area (left side)
-                with gr.Column(scale=2):
-                    # URL Input Section
-                    gr.Markdown("## 📥 Add Content to Knowledge Base")
-                    with gr.Row():
-                        url_input = gr.Textbox(
-                            placeholder="Enter URL (e.g., https://medium.com/article-url)",
-                            label="Website URL",
-                            scale=3
-                        )
-                        add_btn = gr.Button("Add URL", variant="primary", scale=1)
-                    
-                    url_status = gr.Markdown(value="", visible=True)
-                    
-                    # Chat Interface
-                    gr.Markdown("## 💬 Chat with Your Knowledge Base")
-                    
-                    chatbot_interface = gr.Chatbot(
-                        value=[],
-                        height=400,
-                        label="RAG Chatbot",
-                        show_label=True,
-                        container=True,
-                        bubble_full_width=False
-                    )
-                    
-                    with gr.Row():
-                        msg_input = gr.Textbox(
-                            placeholder="Ask a question about your added content...",
-                            label="Your Message",
-                            scale=4,
-                            lines=1
-                        )
-                        send_btn = gr.Button("Send", variant="primary", scale=1)
-                    
-                    # Example questions
-                    gr.Markdown("### 💡 Example Questions:")
-                    example_questions = [
-                        "What is the main topic of this article?",
-                        "Can you summarize the key points?",
-                        "What are the benefits mentioned?",
-                        "How does this relate to AI/ML?"
-                    ]
-                    
-                    with gr.Row():
-                        for question in example_questions[:2]:
-                            gr.Button(question, size="sm").click(
-                                lambda q=question: (q, ""),
-                                outputs=[msg_input, url_status]
-                            )
-                    
-                    with gr.Row():
-                        for question in example_questions[2:]:
-                            gr.Button(question, size="sm").click(
-                                lambda q=question: (q, ""),
-                                outputs=[msg_input, url_status]
-                            )
-                
-                # Sidebar (right side)
-                with gr.Column(scale=1):
-                    gr.Markdown("## 📊 Knowledge Base")
-                    
-                    stats_display = gr.Markdown(
-                        value=self.get_stats_display(),
-                        label="Statistics"
-                    )
-                    
-                    refresh_stats_btn = gr.Button("🔄 Refresh Stats", variant="secondary")
-                    clear_kb_btn = gr.Button("🗑️ Clear Knowledge Base", variant="stop")
-                    
-                    gr.Markdown("""
-                    ### ℹ️ About
-                    This RAG chatbot uses:
-                    - **Groq API** with Mixtral-8x7B for fast inference
-                    - **Faiss** for vector storage
-                    - **Sentence Transformers** for embeddings
-                    - **Beautiful Soup** for web scraping
-                    
-                    The bot retrieves relevant content and generates accurate answers based on your added sources.
-                    
-                    -Made By Ali Abdullah"""    
-                       )
-            
-            # Event handlers
-            add_btn.click(
-                fn=self.add_url,
-                inputs=[url_input],
-                outputs=[url_status, stats_display]
-            ).then(
-                lambda: "",  # Clear URL input after adding
-                outputs=[url_input]
-            )
-            
-            send_btn.click(
-                fn=self.chat_response,
-                inputs=[msg_input, chatbot_interface],
-                outputs=[msg_input, chatbot_interface]
-            )
-            
-            msg_input.submit(
-                fn=self.chat_response,
-                inputs=[msg_input, chatbot_interface],
-                outputs=[msg_input, chatbot_interface]
-            )
-            
-            refresh_stats_btn.click(
-                fn=lambda: self.get_stats_display(),
-                outputs=[stats_display]
-            )
-            
-            clear_kb_btn.click(
-                fn=self.clear_knowledge_base,
-                outputs=[url_status, stats_display]
-            )
-        
-        return interface
-
-def main():
-    """Main function to run the Gradio app"""
-    print("🚀 Starting RAG Chatbot UI...")
-    
-    try:
-        # Initialize the UI
-        ui = ChatbotUI()
-        
-        # Create and launch interface
-        interface = ui.create_interface()
-        
-        # Launch with custom settings
-        interface.launch(
-            server_name="0.0.0.0",  # Allow external access
-            server_port=int(os.environ.get("PORT", 7860)),       # Default Gradio port
-            share=False            # Set to True for public link
-        )
-
-    except Exception as e:
-        print(f"❌ Failed to launch the app: {e}")
-
-if __name__ == "__main__":
-    main()
-
+import gradio as gr
+import os
+import sys
+from typing import List, Tuple
+import time
+from datetime import datetime
+
+# Add the src directory to Python path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from chatbot import RAGChatbot
+
+class ChatbotUI:
+    def __init__(self):
+        """Initialize the Gradio UI for RAG Chatbot"""
+        print("🚀 Initializing Chatbot UI...")
+        self.chatbot = RAGChatbot()
+        self.chat_history = []
+        
+    def add_url(self, url: str) -> Tuple[str, str]:
+        """
+        Add URL to knowledge base
+        Args:
+            url: URL to ingest
+        Returns:
+            Tuple of (status_message, updated_stats)
+        """
+        if not url or not url.strip():
+            return "❌ Please enter a valid URL", self.get_stats_display()
+        
+        url = url.strip()
+        if not (url.startswith('http://') or url.startswith('https://')):
+            url = 'https://' + url
+        
+        # Show processing message
+        status_msg = f"📥 Processing {url}..."
+        
+        try:
+            result = self.chatbot.ingest_url(url)
+            
+            if result['success']:
+                success_msg = f"""✅ Successfully added: {result['title']}
+📊 Added {result['chunks_added']} chunks ({result['word_count']} words)
+🔗 Source: {url}"""
+                return success_msg, self.get_stats_display()
+            else:
+                error_msg = f"❌ Failed to add URL: {result['message']}"
+                return error_msg, self.get_stats_display()
+                
+        except Exception as e:
+            error_msg = f"❌ Error processing URL: {str(e)}"
+            return error_msg, self.get_stats_display()
+    
+    def chat_response(self, message: str, history: List[List[str]]) -> Tuple[str, List[List[str]]]:
+        """
+        Generate chat response
+        Args:
+            message: User message
+            history: Chat history
+        Returns:
+            Tuple of (empty_string, updated_history)
+        """
+        if not message or not message.strip():
+            return "", history
+        
+        # Get response from chatbot
+        response_data = self.chatbot.chat(message.strip(), include_sources=True)
+        
+        # Format response with sources
+        formatted_response = self.format_response(response_data)
+        
+        # Update history
+        history.append([message, formatted_response])
+        
+        return "", history
+    
+    def format_response(self, response_data: dict) -> str:
+        """Format the chatbot response with sources and timing info"""
+        response = response_data['response']
+        
+        # Add timing information
+        timing_info = f"\n\n⏱️ *Response time: {response_data['total_time']}s*"
+        
+        # Add sources if available
+        if response_data.get('sources'):
+            sources_text = "\n\n📚 **Sources:**\n"
+            for i, source in enumerate(response_data['sources'][:3], 1):  # Limit to top 3 sources
+                score = f"({source['similarity_score']:.3f})" if source['similarity_score'] else ""
+                sources_text += f"{i}. **{source['title']}** {score}\n"
+                sources_text += f"   {source['snippet']}\n"
+                sources_text += f"   🔗 {source['url']}\n\n"
+            
+            response += sources_text
+        
+        response += timing_info
+        return response
+    
+    def get_stats_display(self) -> str:
+        """Get formatted knowledge base statistics"""
+        try:
+            stats = self.chatbot.get_knowledge_base_stats()
+            
+            stats_text = f"""📊 **Knowledge Base Statistics**
+            
+🗄️ **Total Documents:** {stats.get('total_documents', 0)}
+🧠 **AI Model:** {stats.get('model_used', 'Unknown')}
+🔤 **Embedding Model:** {stats.get('embedding_model', 'Unknown')}
+📐 **Vector Dimension:** {stats.get('index_dimension', 0)}
+📈 **Index Fullness:** {stats.get('index_fullness', 0):.1%}
+
+*Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*"""
+            
+            return stats_text
+            
+        except Exception as e:
+            return f"❌ Error getting stats: {str(e)}"
+    
+    def clear_knowledge_base(self) -> Tuple[str, str]:
+        """Clear all documents from knowledge base"""
+        try:
+            success = self.chatbot.clear_knowledge_base()
+            if success:
+                return "✅ Knowledge base cleared successfully!", self.get_stats_display()
+            else:
+                return "❌ Failed to clear knowledge base", self.get_stats_display()
+        except Exception as e:
+            return f"❌ Error clearing knowledge base: {str(e)}", self.get_stats_display()
+    
+    def create_interface(self):
+        """Create and return the Gradio interface"""
+        
+        # Custom CSS for better styling
+        custom_css = """
+        .gradio-container {
+            max-width: 1200px !important;
+        }
+        .chat-container {
+            height: 500px !important;
+        }
+        .input-container {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+            padding: 20px !important;
+            border-radius: 10px !important;
+        }
+        """
+        
+        with gr.Blocks(
+            title="🤖 RAG Chatbot",
+            theme=gr.themes.Soft(),
+            css=custom_css
+        ) as interface:
+            
+            # Header
+            gr.Markdown("""
+            # 🤖 RAG-Powered AI Chatbot
+            ### Intelligent Q&A with Web Content Integration
+            
+            **How to use:**
+            1. 📥 Add URLs containing articles or content you want the bot to learn from
+            2. 💬 Ask questions about the content - the bot will provide accurate answers with sources
+            3. 📊 Monitor your knowledge base statistics in the sidebar
+            """)
+            
+            with gr.Row():
+                # Main chat area (left side)
+                with gr.Column(scale=2):
+                    # URL Input Section
+                    gr.Markdown("## 📥 Add Content to Knowledge Base")
+                    with gr.Row():
+                        url_input = gr.Textbox(
+                            placeholder="Enter URL (e.g., https://medium.com/article-url)",
+                            label="Website URL",
+                            scale=3
+                        )
+                        add_btn = gr.Button("Add URL", variant="primary", scale=1)
+                    
+                    url_status = gr.Markdown(value="", visible=True)
+                    
+                    # Chat Interface
+                    gr.Markdown("## 💬 Chat with Your Knowledge Base")
+                    
+                    chatbot_interface = gr.Chatbot(
+                        value=[],
+                        height=400,
+                        label="RAG Chatbot",
+                        show_label=True,
+                        container=True,
+                        bubble_full_width=False
+                    )
+                    
+                    with gr.Row():
+                        msg_input = gr.Textbox(
+                            placeholder="Ask a question about your added content...",
+                            label="Your Message",
+                            scale=4,
+                            lines=1
+                        )
+                        send_btn = gr.Button("Send", variant="primary", scale=1)
+                    
+                    # Example questions
+                    gr.Markdown("### 💡 Example Questions:")
+                    example_questions = [
+                        "What is the main topic of this article?",
+                        "Can you summarize the key points?",
+                        "What are the benefits mentioned?",
+                        "How does this relate to AI/ML?"
+                    ]
+                    
+                    with gr.Row():
+                        for question in example_questions[:2]:
+                            gr.Button(question, size="sm").click(
+                                lambda q=question: (q, ""),
+                                outputs=[msg_input, url_status]
+                            )
+                    
+                    with gr.Row():
+                        for question in example_questions[2:]:
+                            gr.Button(question, size="sm").click(
+                                lambda q=question: (q, ""),
+                                outputs=[msg_input, url_status]
+                            )
+                
+                # Sidebar (right side)
+                with gr.Column(scale=1):
+                    gr.Markdown("## 📊 Knowledge Base")
+                    
+                    stats_display = gr.Markdown(
+                        value=self.get_stats_display(),
+                        label="Statistics"
+                    )
+                    
+                    refresh_stats_btn = gr.Button("🔄 Refresh Stats", variant="secondary")
+                    clear_kb_btn = gr.Button("🗑️ Clear Knowledge Base", variant="stop")
+                    
+                    gr.Markdown("""
+                    ### ℹ️ About
+                    This RAG chatbot uses:
+                    - **Groq API** with Mixtral-8x7B for fast inference
+                    - **Faiss** for vector storage
+                    - **Sentence Transformers** for embeddings
+                    - **Beautiful Soup** for web scraping
+                    
+                    The bot retrieves relevant content and generates accurate answers based on your added sources.
+                    
+                    -Made By Ali Abdullah"""    
+                       )
+            
+            # Event handlers
+            add_btn.click(
+                fn=self.add_url,
+                inputs=[url_input],
+                outputs=[url_status, stats_display]
+            ).then(
+                lambda: "",  # Clear URL input after adding
+                outputs=[url_input]
+            )
+            
+            send_btn.click(
+                fn=self.chat_response,
+                inputs=[msg_input, chatbot_interface],
+                outputs=[msg_input, chatbot_interface]
+            )
+            
+            msg_input.submit(
+                fn=self.chat_response,
+                inputs=[msg_input, chatbot_interface],
+                outputs=[msg_input, chatbot_interface]
+            )
+            
+            refresh_stats_btn.click(
+                fn=lambda: self.get_stats_display(),
+                outputs=[stats_display]
+            )
+            
+            clear_kb_btn.click(
+                fn=self.clear_knowledge_base,
+                outputs=[url_status, stats_display]
+            )
+        
+        return interface
+
+def main():
+    """Main function to run the Gradio app"""
+    print("🚀 Starting RAG Chatbot UI...")
+    
+    try:
+        # Initialize the UI
+        ui = ChatbotUI()
+        
+        # Create and launch interface
+        interface = ui.create_interface()
+        
+        # Launch with custom settings
+        interface.launch(
+            server_name="0.0.0.0",  # Allow external access
+            server_port=int(os.environ.get("PORT", 7860)),       # Default Gradio port
+            share=False            # Set to True for public link
+        )
+
+    except Exception as e:
+        print(f"❌ Failed to launch the app: {e}")
+
+if __name__ == "__main__":
+    main()
+# For Hugging Face Spaces
+ui = ChatbotUI()
+interface = ui.create_interface()