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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 MAIN_PROMPT
# Load 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 API to generate a response based on conversation history.
- history: [(user_text, assistant_text), ...]
- user_message: The latest user input
"""
messages = [{"role": "system", "content": MAIN_PROMPT}]
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})
messages.append({"role": "user", "content": user_message})
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 chatbot responses.
- Ensures teachers must explain their reasoning before AI provides hints or feedback.
- Guides the conversation to include CCSS practice standards, problem-posing, creativity-directed practices, and summary.
"""
if not user_message:
return "", history
# Extract the last interaction
last_message = history[-1][0] if history else ""
if "problem" in last_message.lower() and "solve" in last_message.lower():
# If the bot is expecting an explanation, store the response and move forward
history.append((user_message, "Thanks for sharing your reasoning! Let's analyze your response."))
else:
# Regular OpenAI GPT response
assistant_reply = gpt_call(history, user_message)
history.append((user_message, assistant_reply))
return "", history
##############################
# Gradio Blocks UI
##############################
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)])
user_input = gr.Textbox(
placeholder="Type your message here...",
label="Your Input"
)
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]
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
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