Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import os
 import gradio as gr
 from dotenv import load_dotenv
@@ -12,75 +11,50 @@ if os.path.exists(".env"):
 
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
-# ✅ Ensure API Key is available
+# ✅ Ensure API Key is available, otherwise show an error
 if not OPENAI_API_KEY:
-    raise ValueError("🚨 OpenAI API key is missing! Set it in the .env file.")
+    raise ValueError("🚨 OpenAI API key is missing! Set it in the .env file or hardcode it in app.py.")
 
 client = OpenAI(api_key=OPENAI_API_KEY)
 
-# ✅ Chatbot Response Function with Full Debugging
-def respond(user_message, history, selected_method):
+# ✅ Chatbot Response Function
+def respond(user_message, history):
     if not user_message:
-        return "❌ No input received.", history, selected_method
-
-    user_message = user_message.strip().lower()  # Normalize input
-
-    valid_methods = ["bar model", "double number line", "equation"]
-
-    # ✅ Ensure history is a list of tuples
-    if not isinstance(history, list):
-        history = []
-    history = [(str(h[0]), str(h[1])) for h in history if isinstance(h, tuple) and len(h) == 2]
-
-    # ✅ Debug Logs
-    print("\nDEBUG: Incoming User Message:", user_message)
-    print("DEBUG: Current History:", history)
-    print("DEBUG: Selected Method Before Processing:", selected_method)
-
-    # ✅ If user selects a method, store it and provide the method-specific prompt
-    if user_message in valid_methods:
-        selected_method = user_message  # Store the method
-        method_prompt = get_prompt_for_method(user_message)
-        history.append((user_message, method_prompt))  # Store correctly formatted tuple
-
-        print("DEBUG: Method Selected:", selected_method)
-        print("DEBUG: Sending Prompt for Method:", method_prompt)
-
-        return method_prompt, history, selected_method
-
-    # ✅ If a method has already been selected, provide feedback
-    if selected_method:
-        feedback = get_feedback_for_method(selected_method, user_message)
-        history.append((user_message, feedback))  # Store correctly formatted tuple
-
-        print("DEBUG: Feedback Given:", feedback)
-        print("DEBUG: Updated History:", history)
-
-        return feedback, history, selected_method
+        return "", history
 
-    # ✅ Ensure chatbot always responds with a valid tuple
-    error_msg = "❌ Please select a method first (Bar Model, Double Number Line, or Equation)."
-    history.append((user_message, error_msg))  # Store correctly formatted tuple
+    # ✅ Handle method selection
+    if user_message.lower() in ["bar model", "double number line", "equation"]:
+        response = get_prompt_for_method(user_message)
+        history.append((user_message, response))
+        return "", history
 
-    print("DEBUG: Error Triggered, No Method Selected")
+    # ✅ Handle teacher response to method
+    last_method = history[-1][0] if history else None
+    if last_method and last_method.lower() in ["bar model", "double number line", "equation"]:
+        response = get_feedback_for_method(last_method, user_message)
+        history.append((user_message, response))
+        return "", history
 
-    return error_msg, history, selected_method
+    # ✅ Default response if input is unclear
+    return "I didn’t understand that. Please select a method first (Bar Model, Double Number Line, or Equation).", history
 
 # ✅ Gradio UI Setup
 with gr.Blocks() as demo:
     gr.Markdown("## 🤖 AI-Guided Math PD Chatbot")
 
-    chatbot = gr.Chatbot(value=[(INITIAL_PROMPT, "Hello! Please select a method to begin.")], height=500)
-    state_history = gr.State([(INITIAL_PROMPT, "Hello! Please select a method to begin.")])
-    state_selected_method = gr.State(None)  # ✅ New state to track selected method
+    chatbot = gr.Chatbot(value=[(INITIAL_PROMPT, "")], height=500)  # ✅ Ensures AI starts with INITIAL_PROMPT
+    state_history = gr.State([(INITIAL_PROMPT, "")])  # ✅ Sets initial prompt as the first message
 
     user_input = gr.Textbox(placeholder="Type your message here...", label="Your Input")
 
-    # ✅ Handling user input and response logic
     user_input.submit(
         respond,
-        inputs=[user_input, state_history, state_selected_method],
-        outputs=[chatbot, state_history, state_selected_method]
+        inputs=[user_input, state_history],
+        outputs=[user_input, chatbot]
+    ).then(
+        fn=lambda _, h: h,
+        inputs=[user_input, chatbot],
+        outputs=[state_history]
     )
 
 if __name__ == "__main__":