Update app.py

app.py

@@ -3,31 +3,28 @@ 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,
-    get_prompt_for_problem,
-    get_ccss_practice_standards,
-    get_problem_posing_task,
-    get_creativity_discussion,
-    get_summary,
-)
+from prompts.main_prompt import MAIN_PROMPT
 
-# Load API key from .env file
+# Load OpenAI API Key from .env file
 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-mini", max_tokens=1024, temperature=0.7, top_p=0.95):
+def gpt_call(history, user_message,
+             model="gpt-4o-mini",
+             max_tokens=1024,
+             temperature=0.7,
+             top_p=0.95):
     """
     Calls OpenAI Chat API to generate responses.
     - history: [(user_text, assistant_text), ...]
     - user_message: latest message from user
     """
     messages = [{"role": "system", "content": MAIN_PROMPT}]
-
-    # Add history to conversation
+    
+    # Add conversation history
     for user_text, assistant_text in history:
         if user_text:
             messages.append({"role": "user", "content": user_text})
@@ -35,7 +32,8 @@ def gpt_call(history, user_message, model="gpt-4o-mini", max_tokens=1024, temper
             messages.append({"role": "assistant", "content": assistant_text})
 
     messages.append({"role": "user", "content": user_message})
-
+    
+    # OpenAI API Call
     completion = client.chat.completions.create(
         model=model,
         messages=messages,
@@ -43,74 +41,60 @@ def gpt_call(history, user_message, model="gpt-4o-mini", max_tokens=1024, temper
         temperature=temperature,
         top_p=top_p
     )
-
+    
     response = completion.choices[0].message.content
     
-    # Ensure long responses are handled
-    if len(response) > 1000:
-        response = response[:1000] + "...\n[Message too long. Type 'continue' to see more.]"
+    # Encourage teachers to explain their reasoning before providing guidance
+    if "solve" in user_message.lower() or "explain" in user_message.lower():
+        response = "Great! Before we move forward, can you explain your reasoning? Why do you think this is the right approach? Once you share your thoughts, I'll guide you further.\n\n" + response
+    
+    # Encourage problem posing
+    if "pose a problem" in user_message.lower():
+        response += "\n\nNow that you've explored this concept, try creating your own problem related to it. How would you challenge your students?"
+    
+    # Cover Common Core practice standards
+    if "common core" in user_message.lower():
+        response += "\n\nHow do you see this aligning with Common Core practice standards? Can you identify any specific standards this connects to?"
+    
+    # Encourage creativity-directed practices
+    if "creativity" in user_message.lower():
+        response += "\n\nHow did creativity play a role in this problem-solving process? Did you find any opportunities to think differently?"
+    
+    # Provide structured summary
+    if "summary" in user_message.lower():
+        response += "\n\nSummary: Today, we explored problem-solving strategies, reflected on reasoning, and connected ideas to teaching practices. We examined key characteristics of proportional and non-proportional relationships, explored their graphical representations, and considered pedagogical approaches. Keep thinking about how these concepts can be applied in your own classroom!"
     
     return response
 
 def respond(user_message, history):
     """
     Handles user input and chatbot responses.
-    - user_message: latest user input
-    - history: previous chat history
     """
     if not user_message:
         return "", history
 
-    # If user selects a problem number, redirect to the appropriate prompt
-    if user_message.strip() in ["1", "2", "3"]:
-        assistant_reply = get_prompt_for_problem(user_message.strip())
-
-    # If user is at reflection stage, ask about CCSS Practice Standards
-    elif user_message.lower().strip() == "common core":
-        assistant_reply = get_ccss_practice_standards()
-
-    # If user is at problem-posing stage, ask them to create a new problem
-    elif user_message.lower().strip() == "problem posing":
-        assistant_reply = get_problem_posing_task()
-
-    # If user is at creativity discussion stage, ask for their thoughts
-    elif user_message.lower().strip() == "creativity":
-        assistant_reply = get_creativity_discussion()
-
-    # If user requests a summary, provide the final learning summary
-    elif user_message.lower().strip() == "summary":
-        assistant_reply = get_summary()
-
-    else:
-        # Continue conversation normally with AI guidance
-        assistant_reply = gpt_call(history, user_message)
-
-    # Update history
+    assistant_reply = gpt_call(history, user_message)
     history.append((user_message, assistant_reply))
     return "", history
 
 ##############################
-#  Gradio UI Setup
+#  Gradio Blocks UI
 ##############################
 with gr.Blocks() as demo:
     gr.Markdown("## AI-Guided Math PD Chatbot")
 
-    # Initialize chatbot with first message
     chatbot = gr.Chatbot(
-        value=[("", INITIAL_PROMPT)],  # Initial system message
-        height=600  # Increased height for better visibility
+        value=[("", INITIAL_PROMPT)],
+        height=600
     )
 
-    # Maintain chat history state
     state_history = gr.State([("", INITIAL_PROMPT)])
 
-    # User input box
     user_input = gr.Textbox(
         placeholder="Type your message here...",
         label="Your Input"
     )
 
-    # Submit button
     user_input.submit(
         respond,
         inputs=[user_input, state_history],
@@ -121,6 +105,5 @@ with gr.Blocks() as demo:
         outputs=[state_history]
     )
 
-# Launch app
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)