Update app.py

app.py

@@ -1,248 +1,248 @@
-import gradio as gr
-from agents.tutor_agent import TutorAgent
-import time
-import logging
-import traceback
-import os
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler("tutoring_bot.log"),
-        logging.StreamHandler()
-    ]
-)
-
-class TutoringBotApp:
-    """Main application class for the Multi-Agent Tutoring Bot."""
-
-    def __init__(self):
-        self.tutor_agent = TutorAgent()
-        self.conversation_history = []
-
-    def chat_response(self, message, history):
-        """Handle chat responses with conversation history."""
-        if not message.strip():
-            return history, ""
-
-        # Process the query
-        try:
-            logging.info(f"Processing query: {message}")
-            response = self.tutor_agent.process_query(message)
-            logging.info(f"Query processed successfully. Response: {response[:100]}...")
-
-            # Ensure response is a string
-            if response is None:
-                response = "I apologize, but I couldn't generate a response. Please try again."
-                logging.warning("Response was None, using default message")
-
-            # Add to history using the new messages format
-            new_history = list(history)  # Create a copy to avoid modifying the original
-            new_history.append({"role": "user", "content": message})
-            new_history.append({"role": "assistant", "content": response})
-
-            # Log the history for debugging
-            logging.info(f"Updated history length: {len(new_history)}")
-            if len(new_history) > 0:
-                logging.info(f"Last history item: {new_history[-1]}")
-
-            # Store in conversation history
-            self.conversation_history.append({
-                "user": message,
-                "bot": response,
-                "timestamp": time.time()
-            })
-
-            return new_history, ""
-
-        except Exception as e:
-            error_details = traceback.format_exc()
-            logging.error(f"Error processing query: {str(e)}")
-            logging.error(f"Traceback: {error_details}")
-
-            error_response = f"I apologize, but I encountered an error. Please make sure Ollama is running and try again. Error: {str(e)}"
-
-            new_history = list(history)  # Create a copy to avoid modifying the original
-            new_history.append({"role": "user", "content": message})
-            new_history.append({"role": "assistant", "content": error_response})
-
-            return new_history, ""
-
-    def show_capabilities(self):
-        """Display bot capabilities."""
-        return self.tutor_agent.get_capabilities()
-
-    def clear_conversation(self):
-        """Clear the conversation history."""
-        self.conversation_history = []
-        return []
-
-    def create_interface(self):
-        """Create and configure the Gradio interface."""
-        with gr.Blocks(
-            title="Multi-Agent Tutoring Bot",
-            theme=gr.themes.Soft(),
-            css="""
-            .main-header {
-                text-align: center;
-                color: #2E8B57;
-                margin-bottom: 20px;
-            }
-            .info-box {
-                background-color: #f0f8ff;
-                padding: 15px;
-                border-radius: 10px;
-                border: 1px solid #add8e6;
-                margin: 10px 0;
-            }
-            """
-        ) as demo:
-
-            gr.Markdown(
-                """
-                # 🎓 Multi-Agent Tutoring Bot
-                ### Powered by LangChain, Ollama, and Gradio
-
-                Get help with **Mathematics** and **Physics** from specialized AI agents!
-                """,
-                elem_classes=["main-header"]
-            )
-
-            with gr.Row():
-                with gr.Column(scale=2):
-                    chatbot = gr.Chatbot(
-                        height=500,
-                        show_label=False,
-                        avatar_images=[
-                            "https://cdn-icons-png.flaticon.com/512/3135/3135810.png",  # Student icon
-                            "https://cdn-icons-png.flaticon.com/512/4712/4712027.png"   # Robot icon
-                        ],
-                        type="messages",
-                        render_markdown=True
-                    )
-
-                    msg = gr.Textbox(
-                        placeholder="Ask me about mathematics or physics...",
-                        label="Your Question",
-                        lines=2
-                    )
-
-                    with gr.Row():
-                        submit_btn = gr.Button("Send", variant="primary")
-                        clear_btn = gr.Button("Clear Chat", variant="secondary")
-
-                with gr.Column(scale=1):
-                    gr.Markdown(
-                        """
-                        ### 📚 Quick Examples
-                        **Mathematics:**
-                        - "Solve the equation 2x + 5 = 11"
-                        - "What is the derivative of x²?"
-                        - "Calculate 15 × 23"
-
-                        **Physics:**
-                        - "What is Newton's second law?"
-                        - "Explain kinetic energy"
-                        - "What is the speed of light?"
-
-                        ### ⚙️ System Info
-                        - **Model:** Qwen3 0.6b via Ollama
-                        - **Framework:** LangChain
-                        - **Agents:** Math & Physics specialists
-                        """,
-                        elem_classes=["info-box"]
-                    )
-
-                    capabilities_btn = gr.Button("Show Full Capabilities")
-                    capabilities_output = gr.Markdown(visible=False)
-
-            # Event handlers
-            def submit_message(message, history):
-                logging.info(f"Submit message called with message: '{message}'")
-                logging.info(f"Current history length: {len(history) if history else 0}")
-
-                # Ensure message is not empty
-                if not message or not message.strip():
-                    logging.warning("Empty message submitted, ignoring")
-                    return history, ""
-
-                # Process the message and get updated history
-                updated_history, _ = self.chat_response(message, history)
-                logging.info(f"Updated history returned with length: {len(updated_history)}")
-
-                return updated_history, ""
-
-            def clear_chat():
-                return self.clear_conversation()
-
-            def toggle_capabilities():
-                capabilities_text = self.show_capabilities()
-                return gr.Markdown(capabilities_text, visible=True)
-
-            # Wire up the events
-            msg.submit(
-                submit_message,
-                inputs=[msg, chatbot],
-                outputs=[chatbot, msg]
-            )
-
-            submit_btn.click(
-                submit_message,
-                inputs=[msg, chatbot],
-                outputs=[chatbot, msg]
-            )
-
-            clear_btn.click(
-                clear_chat,
-                outputs=[chatbot]
-            )
-
-            capabilities_btn.click(
-                toggle_capabilities,
-                outputs=[capabilities_output]
-            )
-
-            # Welcome message - updated for new format
-            demo.load(
-                lambda: [{"role": "assistant", "content": "Hello! I'm your AI tutoring assistant. I can help you with mathematics and physics questions. What would you like to learn about today?"}],
-                outputs=[chatbot]
-            )
-
-        return demo
-
-def main():
-    """Main function to run the application."""
-    print("Starting Multi-Agent Tutoring Bot...")
-    print("Make sure Ollama is running with qwen3:0.6b model")
-
-    logging.info("Initializing Tutoring Bot application")
-
-    try:
-        app = TutoringBotApp()
-        demo = app.create_interface()
-
-        logging.info("Application initialized successfully")
-
-        # Launch the app
-        server_name = os.environ.get("SERVER_NAME", "127.0.0.1")
-        server_port = int(os.environ.get("SERVER_PORT", "7860"))
-        logging.info(f"Launching web interface on http://{server_name}:{server_port}")
-        demo.launch(
-            server_name=server_name,
-            server_port=server_port,
-            share=False,
-            show_error=True,
-            quiet=False,
-            debug=True
-        )
-    except Exception as e:
-        error_details = traceback.format_exc()
-        logging.error(f"Failed to start application: {str(e)}")
-        logging.error(f"Traceback: {error_details}")
-        print(f"Error starting application: {str(e)}")
-        print("Check tutoring_bot.log for details")
-
-if __name__ == "__main__":
-    main()
+import gradio as gr
+from agents.tutor_agent import TutorAgent
+import time
+import logging
+import traceback
+import os
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler("tutoring_bot.log"),
+        logging.StreamHandler()
+    ]
+)
+
+class TutoringBotApp:
+    """Main application class for the Multi-Agent Tutoring Bot."""
+
+    def __init__(self):
+        self.tutor_agent = TutorAgent()
+        self.conversation_history = []
+
+    def chat_response(self, message, history):
+        """Handle chat responses with conversation history."""
+        if not message.strip():
+            return history, ""
+
+        # Process the query
+        try:
+            logging.info(f"Processing query: {message}")
+            response = self.tutor_agent.process_query(message)
+            logging.info(f"Query processed successfully. Response: {response[:100]}...")
+
+            # Ensure response is a string
+            if response is None:
+                response = "I apologize, but I couldn't generate a response. Please try again."
+                logging.warning("Response was None, using default message")
+
+            # Add to history using the new messages format
+            new_history = list(history)  # Create a copy to avoid modifying the original
+            new_history.append({"role": "user", "content": message})
+            new_history.append({"role": "assistant", "content": response})
+
+            # Log the history for debugging
+            logging.info(f"Updated history length: {len(new_history)}")
+            if len(new_history) > 0:
+                logging.info(f"Last history item: {new_history[-1]}")
+
+            # Store in conversation history
+            self.conversation_history.append({
+                "user": message,
+                "bot": response,
+                "timestamp": time.time()
+            })
+
+            return new_history, ""
+
+        except Exception as e:
+            error_details = traceback.format_exc()
+            logging.error(f"Error processing query: {str(e)}")
+            logging.error(f"Traceback: {error_details}")
+
+            error_response = f"I apologize, but I encountered an error. Please make sure Ollama is running and try again. Error: {str(e)}"
+
+            new_history = list(history)  # Create a copy to avoid modifying the original
+            new_history.append({"role": "user", "content": message})
+            new_history.append({"role": "assistant", "content": error_response})
+
+            return new_history, ""
+
+    def show_capabilities(self):
+        """Display bot capabilities."""
+        return self.tutor_agent.get_capabilities()
+
+    def clear_conversation(self):
+        """Clear the conversation history."""
+        self.conversation_history = []
+        return []
+
+    def create_interface(self):
+        """Create and configure the Gradio interface."""
+        with gr.Blocks(
+            title="Multi-Agent Tutoring Bot",
+            theme=gr.themes.Soft(),
+            css="""
+            .main-header {
+                text-align: center;
+                color: #2E8B57;
+                margin-bottom: 20px;
+            }
+            .info-box {
+                background-color: #f0f8ff;
+                padding: 15px;
+                border-radius: 10px;
+                border: 1px solid #add8e6;
+                margin: 10px 0;
+            }
+            """
+        ) as demo:
+
+            gr.Markdown(
+                """
+                # 🎓 Multi-Agent Tutoring Bot
+                ### Powered by LangChain, Ollama, and Gradio
+
+                Get help with **Mathematics** and **Physics** from specialized AI agents!
+                """,
+                elem_classes=["main-header"]
+            )
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    chatbot = gr.Chatbot(
+                        height=500,
+                        show_label=False,
+                        avatar_images=[
+                            "https://cdn-icons-png.flaticon.com/512/3135/3135810.png",  # Student icon
+                            "https://cdn-icons-png.flaticon.com/512/4712/4712027.png"   # Robot icon
+                        ],
+                        type="messages",
+                        render_markdown=True
+                    )
+
+                    msg = gr.Textbox(
+                        placeholder="Ask me about mathematics or physics...",
+                        label="Your Question",
+                        lines=2
+                    )
+
+                    with gr.Row():
+                        submit_btn = gr.Button("Send", variant="primary")
+                        clear_btn = gr.Button("Clear Chat", variant="secondary")
+
+                with gr.Column(scale=1):
+                    gr.Markdown(
+                        """
+                        ### 📚 Quick Examples
+                        **Mathematics:**
+                        - "Solve the equation 2x + 5 = 11"
+                        - "What is the derivative of x²?"
+                        - "Calculate 15 × 23"
+
+                        **Physics:**
+                        - "What is Newton's second law?"
+                        - "Explain kinetic energy"
+                        - "What is the speed of light?"
+
+                        ### ⚙️ System Info
+                        - **Model:** Qwen3 0.6b via Ollama
+                        - **Framework:** LangChain
+                        - **Agents:** Math & Physics specialists
+                        """,
+                        elem_classes=["info-box"]
+                    )
+
+                    capabilities_btn = gr.Button("Show Full Capabilities")
+                    capabilities_output = gr.Markdown(visible=False)
+
+            # Event handlers
+            def submit_message(message, history):
+                logging.info(f"Submit message called with message: '{message}'")
+                logging.info(f"Current history length: {len(history) if history else 0}")
+
+                # Ensure message is not empty
+                if not message or not message.strip():
+                    logging.warning("Empty message submitted, ignoring")
+                    return history, ""
+
+                # Process the message and get updated history
+                updated_history, _ = self.chat_response(message, history)
+                logging.info(f"Updated history returned with length: {len(updated_history)}")
+
+                return updated_history, ""
+
+            def clear_chat():
+                return self.clear_conversation()
+
+            def toggle_capabilities():
+                capabilities_text = self.show_capabilities()
+                return gr.Markdown(capabilities_text, visible=True)
+
+            # Wire up the events
+            msg.submit(
+                submit_message,
+                inputs=[msg, chatbot],
+                outputs=[chatbot, msg]
+            )
+
+            submit_btn.click(
+                submit_message,
+                inputs=[msg, chatbot],
+                outputs=[chatbot, msg]
+            )
+
+            clear_btn.click(
+                clear_chat,
+                outputs=[chatbot]
+            )
+
+            capabilities_btn.click(
+                toggle_capabilities,
+                outputs=[capabilities_output]
+            )
+
+            # Welcome message - updated for new format
+            demo.load(
+                lambda: [{"role": "assistant", "content": "Hello! I'm your AI tutoring assistant. I can help you with mathematics and physics questions. What would you like to learn about today?"}],
+                outputs=[chatbot]
+            )
+
+        return demo
+
+def main():
+    """Main function to run the application."""
+    print("Starting Multi-Agent Tutoring Bot...")
+    print("Make sure Ollama is running with qwen3:0.6b model")
+
+    logging.info("Initializing Tutoring Bot application")
+
+    try:
+        app = TutoringBotApp()
+        demo = app.create_interface()
+
+        logging.info("Application initialized successfully")
+
+        # Launch the app
+        server_name = os.environ.get("SERVER_NAME", "127.0.0.1")
+        server_port = int(os.environ.get("SERVER_PORT", "7860"))
+        logging.info(f"Launching web interface on http://{server_name}:{server_port}")
+        demo.launch(
+            server_name="0.0.0.0", 
+            server_port=7860
+            share=False,
+            show_error=True,
+            quiet=False,
+            debug=True
+        )
+    except Exception as e:
+        error_details = traceback.format_exc()
+        logging.error(f"Failed to start application: {str(e)}")
+        logging.error(f"Traceback: {error_details}")
+        print(f"Error starting application: {str(e)}")
+        print("Check tutoring_bot.log for details")
+
+if __name__ == "__main__":
+    main()