import gradio as gr
import requests
from dotenv import load_dotenv
import os

load_dotenv()
HUGGINGFACE_TOKEN = os.getenv('HUGGINGFACE_TOKEN')

# Hugging Face API configuration
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
HEADERS = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}

# Function to interact with the Hugging Face model
def query_huggingface_api(input_text):
    payload = {"inputs": input_text}
    try:
        response = requests.post(API_URL, headers=HEADERS, json=payload)
        response.raise_for_status()  # Raise error for HTTP errors
        return response.json()[0]["generated_text"]
    except requests.exceptions.RequestException as e:
        return f"Error: {str(e)}"

# Gradio interface
def chatbot(input_text):
    response = query_huggingface_api(input_text)
    return response

iface = gr.Interface(
    fn=chatbot,
    inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
    outputs=gr.Textbox(),
    title="AI Chatbot",
    description="Chat with the AI powered by Hugging Face Mistral-7B-Instruct-v0.3.",
)

if __name__ == "__main__":
    iface.launch()