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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 TASK_PROMPT | |
| # Load the 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=512, | |
| temperature=0.7, | |
| top_p=0.95): | |
| """ | |
| Calls OpenAI's ChatCompletion API to generate responses. | |
| - history: [(user_text, assistant_text), ...] | |
| - user_message: User's latest input | |
| """ | |
| # System message (TASK_PROMPT) at the beginning | |
| messages = [{"role": "system", "content": TASK_PROMPT}] | |
| # Convert history into OpenAI format | |
| 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}) | |
| # Add the latest user input | |
| messages.append({"role": "user", "content": user_message}) | |
| # AI-controlled gradual guidance | |
| if "bar model" in user_message.lower(): | |
| return "Great! You've started using a bar model. Can you explain how you divided it? What does each section represent?" | |
| elif "double number line" in user_message.lower(): | |
| return "Nice! How does your number line show the relationship between time and distance? Did you mark the correct intervals?" | |
| elif "ratio table" in user_message.lower(): | |
| return "Good choice! Before I check, how did you determine the ratio for 1 hour?" | |
| elif "graph" in user_message.lower(): | |
| return "Graphs are powerful! What key points did you plot, and why?" | |
| else: | |
| # OpenAI API call (fallback response) | |
| 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 chatbot response in Gradio. | |
| - user_message: The latest input from the user. | |
| - history: A list of (user, assistant) message pairs. | |
| """ | |
| if not user_message: | |
| return "", history | |
| # Generate AI response | |
| assistant_reply = gpt_call(history, user_message) | |
| # Append to history | |
| history.append((user_message, assistant_reply)) | |
| # Return the updated history and clear the input box | |
| return "", history | |
| ############################## | |
| # Gradio Chatbot UI | |
| ############################## | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## AI-Guided Teacher PD Chatbot") | |
| # Initial chatbot message (starts with the task) | |
| chatbot = gr.Chatbot( | |
| value=[("", INITIAL_PROMPT)], | |
| height=500 | |
| ) | |
| # Chat history state | |
| state_history = gr.State([("", INITIAL_PROMPT)]) | |
| # User input box | |
| user_input = gr.Textbox( | |
| placeholder="Type your response here...", | |
| label="Your Input" | |
| ) | |
| # When user submits input → respond() updates chatbot | |
| 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] | |
| ) | |
| # Launch the chatbot | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860, share=True) | |