app.py

import gradio as gr
from huggingface_hub import InferenceClient
from datetime import datetime
import os
import uuid

# ---- System Prompt ----
with open("system_prompt.txt", "r") as f:
    SYSTEM_PROMPT = f.read()

# ---- Constants ----
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
DATASET_REPO = "frimelle/companion-chat-logs"
HF_TOKEN = os.environ.get("HF_TOKEN")  # set in Space secrets
client = InferenceClient(MODEL_NAME)

# ---- Upload to Dataset ----
def upload_chat_to_dataset(user_message, assistant_message, system_prompt):
    row = {
        "timestamp": datetime.now().isoformat(),
        "session_id": str(uuid.uuid4()),
        "user": user_message,
        "assistant": assistant_message,
        "system_prompt": system_prompt,
    }

    dataset = Dataset.from_dict({k: [v] for k, v in row.items()})
    dataset.push_to_hub(DATASET_REPO, private=True, token=HF_TOKEN)

# ---- Chat Function ----
def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]

    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})

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

    response = ""
    for chunk in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = chunk.choices[0].delta.content
        if token:
            response += token
            yield response

    # Log the final full message to the dataset
    upload_chat_to_dataset(message, response, system_message)

# ---- Gradio UI ----
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value=SYSTEM_PROMPT, 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)"),
    ],
    title="BoundrAI",
)

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