Update app.py

app.py

@@ -231,7 +231,7 @@ Perfect for beginners and experienced programmers alike!"""
 
 Step 1: Choose Your Path
 • Complete Beginner? Start with Python Fundamentals
-��� Some Programming Experience? Jump to Data Science Essentials
+• Some Programming Experience? Jump to Data Science Essentials
 • Want to Specialize? Pick Machine Learning or AI track
 
 Step 2: Learning Format
@@ -656,17 +656,17 @@ def create_chat_interface():
             setTyping(true);
             
             try {
-                const response = await fetch(window.location.origin + '/chat', {
+                const response = await fetch('/predict', {
                     method: 'POST',
                     headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify({ message })
+                    body: JSON.stringify({ data: [message] })
                 });
                 
                 const data = await response.json();
                 
                 await new Promise(resolve => setTimeout(resolve, 1200));
                 
-                addMessage(data.response || 'Sorry, I could not generate a response.', 'bot');
+                addMessage(data.data[0] || 'Sorry, I could not generate a response.', 'bot');
                 setConnected(true);
                 
             } catch (error) {
@@ -806,18 +806,8 @@ def create_chat_interface():
 </html>
     """
 
-def chat_response(message, history):
-    if not message.strip():
-        return history, ""
-    
-    if history is None:
-        history = []
-    
-    history = history + [[message, None]]
-    
+def chat_response(message):
     try:
-        time.sleep(1)
-        
         if retriever:
             docs = retriever.get_relevant_documents(message)
             context = "\n\n".join([doc.page_content for doc in docs[:3]])
@@ -825,28 +815,12 @@ def chat_response(message, history):
             context = "I'm an AI assistant for Atomcamp, a data science education platform."
         
         response = generate_response_with_groq(message, context)
-        
-        history[-1][1] = response
-        
-    except Exception as e:
-        history[-1][1] = f"Sorry, I encountered an error: {str(e)}. Please try again."
-    
-    return history, ""
-
-def api_chat(message):
-    try:
-        if retriever:
-            docs = retriever.get_relevant_documents(message)
-            context = "\n\n".join([doc.page_content for doc in docs[:3]])
-        else:
-            context = "I'm an AI assistant for Atomcamp."
-        
-        response = generate_response_with_groq(message, context)
-        return {"response": response}
+        return response
         
     except Exception as e:
-        return {"response": f"Sorry, I encountered an error: {str(e)}"}
+        return f"Sorry, I encountered an error: {str(e)}. Please try again."
 
+# Create the Gradio interface
 with gr.Blocks(
     title="atomcamp AI Chatbot",
     css="""
@@ -861,14 +835,22 @@ with gr.Blocks(
     """
 ) as demo:
     
+    # Hidden Gradio components for API functionality
     with gr.Row(visible=False):
-        chatbot_hidden = gr.Chatbot(type="messages")
-        msg_hidden = gr.Textbox()
-        submit_hidden = gr.Button()
+        input_text = gr.Textbox()
+        output_text = gr.Textbox()
+        submit_btn = gr.Button()
     
+    # Custom HTML interface
     gr.HTML(create_chat_interface())
-
-demo.mount_gradio_app(api_chat, path="/chat")
+    
+    # Set up the API endpoint
+    submit_btn.click(
+        fn=chat_response,
+        inputs=input_text,
+        outputs=output_text,
+        api_name="predict"
+    )
 
 if __name__ == "__main__":
     demo.launch(