import gradio as gr
from huggingface_hub import InferenceClient
import os
from huggingface_hub import login

# Fetch token from environment (automatically loaded from secrets)
hf_token = os.getenv("gemma3")
login(hf_token)

client = InferenceClient("hackergeek98/gemma-finetuned")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    prompt = f"{system_message}\n"
    
    # Add conversation history if needed
    for val in history:
        if val[0]:
            prompt += f"User: {val[0]}\n"
        if val[1]:
            prompt += f"Assistant: {val[1]}\n"

    prompt += f"User: {message}\nAssistant:"

    # Request generation from Hugging Face Inference API
    response = client.text_generation(
        model="hackergeek98/gemma-finetuned",
        inputs=prompt,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
    )

    return response['generated_text']

# Gradio interface setup
demo = gr.ChatInterface(
    respond,
    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)"),
    ],
)

# Run the app
if __name__ == "__main__":
    demo.launch()