import gradio as gr
import os
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""

model_name = "meta-llama/Llama-3.2-1B"
huggingface_token = os.getenv("SECRET_ENV_VARIABLE")
#client = InferenceClient(api_key=huggingface_token)
client = InferenceClient(model=model_name, token=huggingface_token)

'''
import requests

API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B"
headers = {"Authorization": "Bearer "}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
output = query({
	"inputs": "Can you please let us know more details about your ",
})
'''

def generate_text(
    prompt,
    system_message,
    max_tokens,
    temperature,
    top_p
):
    try:
        print(f"Attempting to generate text for prompt: {prompt[:50]}...")
        
        response = client.text_generation(
            prompt,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_k=50,
            top_p=top_p,
            do_sample=True
        )
        
        print(f"Generated text: {response[:100]}...")
        return response
    except Exception as e:
        print(f"Error in generate_text: {type(e).__name__}: {str(e)}")
        return f"An error occurred: {type(e).__name__}: {str(e)}"



def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface

demo = gr.ChatInterface(
    #respond,
    generate_text,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)
"""

with gr.Tab("Generate Email"):
    Query = gr.Textbox(label="Query")
    generate_button = gr.Button("Ask Query")
    output = gr.Textbox(label="Generated Answer", lines=10)
    
    generate_button.click(generate_text, 
    #inputs=[industry, recipient_role, company_details], 
        additional_inputs=[
            Query,
            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
            gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.95,
                step=0.05,
                label="Top-p (nucleus sampling)",
            ),
        ],
    
    
    outputs=output)



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