import gradio as gr
import os
import requests

# Set your Hugging Face API key
HF_API_KEY = "HF_API_KEY"
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct"

headers = {"Authorization": f"Bearer {HF_API_KEY}"}

def query_huggingface(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

def ai_tutor(question):
    payload = {
        "inputs": question,
        "parameters": {"max_new_tokens": 100, "temperature": 0.7}
    }
    
    result = query_huggingface(payload)
    
    # Extract and format the response
    if "error" in result:
        return "Sorry, I couldn't process the request at the moment."
    
    return result[0]["generated_text"]

# Gradio Chatbot UI
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 AI-Powered Mechanical Engineering Learning Chatbot")
    chatbot = gr.Chatbot()
    input_text = gr.Textbox(label="Ask a Question")
    submit_btn = gr.Button("Submit")

    def respond(message, history):
        response = ai_tutor(message)
        history.append((message, response))
        return history, ""

    submit_btn.click(respond, inputs=[input_text, chatbot], outputs=[chatbot, input_text])

demo.launch()