app.py

import os
from typing import List, Tuple
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "Balab2021/qwen-workflow-planner-qwen2p5-lora"

HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("HF_TOKEN environment variable is missing. Please add it in Space Settings → Secrets.")

# Load model at startup
print(f"Loading model: {MODEL_ID} ...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    token=HF_TOKEN,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

def build_messages(history: List[Tuple[str, str]], user_message: str):
    messages = []
    for user_text, assistant_text in history:
        if user_text:
            messages.append({"role": "user", "content": user_text})
        if assistant_text:
            messages.append({"role": "assistant", "content": assistant_text})
    messages.append({"role": "user", "content": user_message})
    return messages

def chat_fn(
    message: str,
    history: List[Tuple[str, str]],
    temperature: float | None = 0.7,      # <-- default here
    max_new_tokens: int | None = 512,     # <-- default here
) -> str:
    # Handle None values (from example caching)
    temperature = temperature if temperature is not None else 0.7
    max_new_tokens = max_new_tokens if max_new_tokens is not None else 512

    messages = build_messages(history, message)
    
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    with torch.no_grad():
        output_ids = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=0.9,
            top_k=40,
            do_sample=temperature > 0.01,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
            renormalize_logits=True,
        )
    
    generated_ids = output_ids[0][inputs["input_ids"].shape[-1] :]
    response = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
    
    return response


demo = gr.ChatInterface(
    fn=chat_fn,
    additional_inputs=[
        gr.Slider(0.0, 1.5, value=0.7, step=0.05, label="Temperature"),
        gr.Slider(32, 2048, value=512, step=32, label="Max New Tokens"),
    ],
    additional_inputs_accordion=gr.Accordion("Generation Settings", open=False),
    title="Qwen Workflow Planner Chat",
    description=f"Model: {MODEL_ID}",
    examples=[
        ["Plan a simple content creation workflow"],
        ["How to automate a daily report generation process?"],
    ],
    cache_examples=False,   # Recommended on HF Spaces with additional inputs
)

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