#import gradio as gr
#gr.load("models/mistralai/Mistral-7B-Instruct-v0.3").launch()

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

import gradio_client as grc
grc.Client("Z3ktrix/mistralai-Mistral-7B-Instruct-v0.3").deploy_discord({"Authorization": f"Bearer {os.getenv('DSTOK')}"})
# Load the environment variables from the .env file
load_dotenv()

API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
headers = {"Authorization": f"Bearer {os.getenv('HFREAD')}"}

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

def chatbot_response(input_text):
    response = query({"inputs": input_text})
    # Extract the generated text from the response
    if isinstance(response, dict) and 'generated_text' in response:
        return response['generated_text']
    elif isinstance(response, list) and len(response) > 0 and 'generated_text' in response[0]:
        return response[0]['generated_text']
    return 'No response generated.'

# Gradio interface
def main():
    with gr.Blocks() as demo:
        gr.Markdown("Chatty")
        
        with gr.Row():
            input_box = gr.Textbox(label="Input Text", placeholder="Type your question here...", lines=2)
        
        with gr.Row():
            output_box = gr.Textbox(label="Response", placeholder="The response will appear here...", lines=5)
        
        submit_button = gr.Button("Submit")
        
        submit_button.click(fn=chatbot_response, inputs=input_box, outputs=output_box)
 

   
    demo.launch()

if __name__ == "__main__":
    main()