Update prompts/main_prompt.py

prompts/main_prompt.py

@@ -71,115 +71,3 @@ x = \frac{600}{2} = 300
 🔹 **Conclusion:**  
 - *The new paint mixture has a **stronger red color** than before.*
 """
-
----
-
-### **🚀 Fully Updated `app.py` (Ensuring Proper OpenAI Handling & Math Formatting)**
-```python
-import os
-import gradio as gr
-from dotenv import load_dotenv
-from openai import OpenAI
-from prompts.initial_prompt import INITIAL_PROMPT
-from prompts.main_prompt import MAIN_PROMPT, PROBLEM_SOLUTIONS_PROMPT  # Ensure both are imported
-
-# Load the API key from the .env file if available
-if os.path.exists(".env"):
-    load_dotenv(".env")
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-client = OpenAI(api_key=OPENAI_API_KEY)
-
-
-def gpt_call(history, user_message,
-             model="gpt-4o",
-             max_tokens=512,
-             temperature=0.7,
-             top_p=0.95):
-    """
-    Calls the OpenAI API to generate a response.
-    - history: [(user_text, assistant_text), ...]
-    - user_message: The latest user message
-    """
-    # 1) Start with the system message (MAIN_PROMPT) for context
-    messages = [{"role": "system", "content": MAIN_PROMPT}]
-
-    # 2) Append conversation history
-    for user_text, assistant_text in history:
-        if user_text:
-            messages.append({"role": "user", "content": user_text})
-        if assistant_text:
-            messages.append({"role": "assistant", "content": assistant_text})
-
-    # 3) Add the user's new message
-    messages.append({"role": "user", "content": user_message})
-
-    # 4) Call OpenAI API
-    completion = client.chat.completions.create(
-        model=model,
-        messages=messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p
-    )
-
-    return completion.choices[0].message.content
-
-
-def respond(user_message, history):
-    """
-    Handles user input and gets GPT-generated response.
-    - user_message: The message from the user
-    - history: List of (user, assistant) conversation history
-    """
-    if not user_message:
-        return "", history
-
-    # If the user asks for a solution, inject PROBLEM_SOLUTIONS_PROMPT
-    if "solution" in user_message.lower():
-        assistant_reply = gpt_call(history, PROBLEM_SOLUTIONS_PROMPT)
-    else:
-        assistant_reply = gpt_call(history, user_message)
-
-    # Add conversation turn to history
-    history.append((user_message, assistant_reply))
-
-    return "", history
-
-
-##############################
-#  Gradio Blocks UI
-##############################
-with gr.Blocks() as demo:
-    gr.Markdown("## AI-Guided Math PD Chatbot")
-
-    # Chatbot initialization with the first AI message
-    chatbot = gr.Chatbot(
-        value=[("", INITIAL_PROMPT)],  # Initial system prompt
-        height=500
-    )
-
-    # Stores the chat history
-    state_history = gr.State([("", INITIAL_PROMPT)])
-
-    # User input field
-    user_input = gr.Textbox(
-        placeholder="Type your message here...",
-        label="Your Input"
-    )
-
-    # Submit action
-    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]
-    )
-
-# Run the Gradio app
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)