app.py

import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Repos
BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
ADAPTER_REPO = "cheberle/autotrain-35swc-b4r9z"

# 1. Load the PEFT config to confirm the base model
peft_config = PeftConfig.from_pretrained(ADAPTER_REPO)
print("PEFT Base Model:", peft_config.base_model_name_or_path)

# 2. Load the tokenizer & base model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True, language='de')
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    revision="4831ee1375be5b4ff5a4abf7984e13628db44e35",
    ignore_mismatched_sizes=True,
    trust_remote_code=True,
    device_map="auto",
)

# 3. Load your LoRA adapter weights onto the base model
model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_REPO,
    ignore_mismatched_sizes=True,
)

def extract_food_term(text):
    """
    Extract or simplify a German food term to a single word or best descriptor.
    """
    prompt = f"Extrahiere das beste ein Wort oder den Begriff, der dieses Nahrungsmittel beschreibt:\nInput: {text}\nOutput:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=16,  # Limit output to a single word
            temperature=0.0,   # Deterministic output
            top_p=1.0,         # Focus on the most likely word
            do_sample=False,   # Disable sampling
        )
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # Extract only the generated output
    answer = answer.split("Output:")[1].strip()
    return answer

with gr.Blocks() as demo:
    gr.Markdown("## Qwen + LoRA Adapter: Lebensmittelbegriffserkennung Demo")
    input_box = gr.Textbox(lines=1, label="Geben Sie ein Nahrungsmittel ein (z.B., 'Blaubeertorte')")
    output_box = gr.Textbox(lines=1, label="Beste ein Wort-Beschreibung")

    extract_btn = gr.Button("Begriff extrahieren")
    extract_btn.click(fn=extract_food_term, inputs=input_box, outputs=output_box)

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