Update prompts/main_prompt.py

prompts/main_prompt.py

@@ -70,45 +70,116 @@ x = \frac{600}{2} = 300
 
 🔹 **Conclusion:**  
 - *The new paint mixture has a **stronger red color** than before.*
+"""
 
 ---
-### **🔹 Common Core Mathematical Practices Discussion**  
-*"Now that you've worked through multiple problems and designed your own, let’s reflect on the Common Core Mathematical Practices we engaged with!"*  
-
-- "Which Common Core practices do you think we used in solving these problems?"  
-
-🔹 **Possible Responses (AI guides based on teacher input):**  
-- **If the teacher mentions MP1 (Make sense of problems & persevere), AI responds:**  
-  - "Yes! These tasks required **analyzing proportional relationships, setting up ratios, and reasoning through different methods**."  
-- **If the teacher mentions MP2 (Reason abstractly and quantitatively), AI responds:**  
-  - "Great point! You had to think about **how numbers and relationships apply to real-world contexts**."  
-- **If the teacher mentions MP7 (Look for and make use of structure), AI responds:**  
-  - "Yes! Recognizing **consistent patterns in ratios and proportions** was key to solving these problems."  
-- **If unsure, AI provides guidance:**  
-  - "Some key Common Core connections include:  
-    - **MP1 (Problem-Solving & Perseverance):** Breaking down complex proportional relationships.  
-    - **MP2 (Reasoning Abstractly & Quantitatively):** Thinking flexibly about numerical relationships.  
-    - **MP7 (Recognizing Structure):** Identifying **consistent ratios and proportional reasoning strategies**."  
-  - "How do you think these skills help students become better problem solvers?"  
-
----
-### **🔹 Creativity-Directed Practices Discussion**  
-*"Creativity is essential in math! Let’s reflect on the creativity-directed practices involved in these problems."*  
-
-- "What creativity-directed practices do you think were covered?"  
-
-🔹 **Possible Responses (AI guides based on teacher input):**  
-- **If the teacher mentions "Exploring multiple solutions," AI responds:**  
-  - "Absolutely! Each problem allowed for multiple approaches—**setting up proportions, using scaling factors, or applying unit rates**."  
-- **If the teacher mentions "Making connections," AI responds:**  
-  - "Yes! These problems linked proportional reasoning to **real-world contexts like maps, financial decisions, and color mixing**."  
-- **If the teacher mentions "Flexible Thinking," AI responds:**  
-  - "Great insight! You had to decide between **ratios, proportions, and numerical calculations**, adjusting your strategy based on the type of problem."  
-- **If unsure, AI guides them:**  
-  - "Key creative practices in this module included:  
-    - **Exploring multiple approaches** to solving proportion problems.  
-    - **Connecting math to real-life contexts** like money, distance, and color mixing.  
-    - **Thinking flexibly**—adjusting strategies based on different types of proportional relationships."  
-  - "How do you think encouraging creativity in problem-solving benefits students?"  
-"""
 
+### **🚀 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)