app.py

# import gradio as gr
# from huggingface_hub import InferenceClient


# 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.ChatInterface(
#     respond,
#     type="messages",
#     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.Blocks() as demo:
#     with gr.Sidebar():
#         gr.LoginButton()
#     chatbot.render()


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



import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# Download your model automatically
model_path = hf_hub_download(
    repo_id="stevendhasoi/Iriseder",
    filename="model_q4_k_m.gguf"
)

# Load GGUF model
llm = Llama(
    model_path=model_path,
    n_ctx=2048,
    n_threads=4,
)

def chat_fn(message, history):
    prompt = ""
    for user, bot in history:
        prompt += f"User: {user}\nAssistant: {bot}\n"

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

    output = llm(
        prompt,
        max_tokens=256,
        stop=["User:"],
        echo=False
    )

    reply = output["choices"][0]["text"].strip()
    return reply

gr.ChatInterface(chat_fn).launch()