Update app.py

app.py

@@ -1,23 +1,69 @@
+import os
+import gradio as gr
+from dotenv import load_dotenv
+from openai import OpenAI
+from prompts.main_prompt import MAIN_PROMPT, get_prompt_for_method, get_feedback_for_method
+from prompts.initial_prompt import INITIAL_PROMPT
+
+# ✅ Load API key from .env file
+if os.path.exists(".env"):
+    load_dotenv(".env")
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# ✅ Ensure API Key is available
+if not OPENAI_API_KEY:
+    raise ValueError("🚨 OpenAI API key is missing! Set it in the .env file.")
+
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# ✅ Chatbot Response Function
 def respond(user_message, history):
     """
-    Handles user input and determines how the chatbot responds.
-    - If the teacher selects a method, AI will **first ask them to explain their approach**.
-    - AI will not provide a full solution immediately.
-    - If the teacher already explained, AI will provide feedback and hints.
+    Handles user input for the chatbot.
+    - If a method is selected, AI asks for teacher's reasoning first.
+    - AI only provides feedback after hearing the teacher's approach.
     """
     user_message = user_message.strip().lower()
 
-    # If the user selects a method, prompt them to explain their approach first
+    # ✅ If user selects a method, ask them to explain their reasoning
     if user_message in ["bar model", "double number line", "equation"]:
-        return get_prompt_for_method(user_message), history
-    
-    # If AI is expecting an explanation, process teacher's response
-    elif len(history) > 0 and history[-1] in [
+        response = get_prompt_for_method(user_message)
+        history.append((user_message, response))
+        return "", history
+
+    # ✅ If AI expects an explanation, process the response
+    elif len(history) > 0 and history[-1][1] in [
         get_prompt_for_method("bar model"),
         get_prompt_for_method("double number line"),
         get_prompt_for_method("equation")
     ]:
-        last_method = history[-1].split("**")[1].split(" ")[0].lower()  # Extract method name from the last message
-        return get_feedback_for_method(last_method, user_message), history
-    
-    return "I didn’t quite understand that. Please select a method first (Bar Model, Double Number Line, or Equation).", history
+        last_method = history[-1][1].split("**")[1].split(" ")[0].lower()
+        response = get_feedback_for_method(last_method, user_message)
+        history.append((user_message, response))
+        return "", history
+
+    # ✅ Default response if the message 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, "")], height=500)  
+    state_history = gr.State([(INITIAL_PROMPT, "")])  
+
+    user_input = gr.Textbox(placeholder="Type your message here...", label="Your Input")
+
+    user_input.submit(
+        respond,
+        inputs=[user_input, state_history],
+        outputs=[user_input, chatbot]
+    ).then(
+        fn=lambda _, h: h,
+        inputs=[user_input, chatbot],
+        outputs=[state_history]
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)