app.py

import gradio as gr
from huggingface_hub import login

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os

hf_token = os.getenv("HF_TOKEN")
login(token=hf_token)

model_id = "meta-llama/Llama-3.2-1B"  # small enough to run locally on CPU
tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(model_id, token=hf_token)


def chat(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
# def respond(
#     message,
#     history: list[dict[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
#     hf_token: gr.OAuthToken,
# ):
#     """
#     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
#     """
#     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")

#     messages = [{"role": "system", "content": system_message}]

#     messages.extend(history)

#     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,
#     ):
#         choices = message.choices
#         token = ""
#         if len(choices) and choices[0].delta.content:
#             token = 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
"""
chatbot = gr.Interface(fn=chat, inputs="text", outputs="text", title="Local HF Model Chatbot")

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


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