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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 get_prompt_for_method, get_feedback_for_method
# ✅ 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, selected_method):
if not user_message:
return "", history, selected_method
user_message = user_message.strip().lower() # Normalize input
valid_methods = ["bar model", "double number line", "equation"]
# ✅ 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)) # Save to history
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)) # Save user response and feedback
return feedback, history, selected_method
return "❌ Please select a method first (Bar Model, Double Number Line, or Equation).", history, selected_method
# ✅ 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, "")])
state_selected_method = gr.State(None) # ✅ New state to track selected method
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]
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
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