app.py

import gradio as gr
import torch
from transformers import pipeline
import os
import spaces
from huggingface_hub import CommitScheduler
from pathlib import Path
import uuid
import json
import time
from datetime import datetime
import logging

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("app.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger("darija-masked-lm")

#load_dotenv()
key=os.environ["HF_KEY"]


# Define the file where to save the data
submit_file = Path("user_submit/") / f"data_{uuid.uuid4()}.json"
feedback_file = submit_file

# Create directory if it doesn't exist
submit_file.parent.mkdir(exist_ok=True, parents=True)
logger.info(f"Created feedback file: {feedback_file}")

scheduler = CommitScheduler(
    repo_id="atlasia/atlaset_inference_ds",
    repo_type="dataset",
    folder_path=submit_file.parent,
    path_in_repo="masked_lm",
    every=5,
    token=key
)
logger.info(f"Initialized CommitScheduler for repo: atlasia/atlaset_inference_ds")


def save_feedback(input, output) -> None:
    """
    Append input/outputs and parameters to a JSON Lines file using a thread lock
    to avoid concurrent writes from different users.
    """
    logger.info(f"Saving feedback to {feedback_file}")
    
    with scheduler.lock:
        try:
            with feedback_file.open("a") as f:
                f.write(json.dumps({
                    "input": input, 
                    "output": output, 
                }))
                f.write("\n")
            logger.info("Feedback saved successfully")
        except Exception as e:
            logger.error(f"Error saving feedback: {str(e)}")


def load_model():
    print("[INFO] Loading model... This may take a minute on Spaces")
    pipe = pipeline(
        task="fill-mask",
        model="atlasia/XLM-RoBERTa-Morocco",
        token=key,
        device=0,
        torch_dtype=torch.float16  # Use half precision
    )
    print("[INFO] Model loaded successfully!")
    return pipe

print("[INFO] load model ...")
pipe=load_model()
print("[INFO] model loaded")

@spaces.GPU
def predict(text):
    outputs = pipe(text)
    scores= [x["score"] for x in outputs]
    tokens= [x["token_str"] for x in outputs]
    result={label: float(prob) for label, prob in zip(tokens, scores)}
    # Save feedback with additional metadata
    save_feedback(
        text, 
        result
    )
    return result

# Create Gradio interface
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            # Input text box
            input_text = gr.Textbox(
                label="Input",
                placeholder="Enter text here...",
                rtl=True
            )
            
            # Button row
            with gr.Row():
                clear_btn = gr.Button("Clear")
                submit_btn = gr.Button("Submit", variant="primary")
            
            # Output probabilities
            output_labels = gr.Label(
                label="Prediction Results",
                show_label=False
            )
            
            # Examples section with basic configuration
            gr.Examples(
                examples=["العاصمة د <mask> هي الرباط","المغرب <mask> زوين","انا سميتي مريم، و كنسكن ف<mask> العاصمة دفلسطين"],
                inputs=input_text,
                fn=predict,
                outputs=output_labels,
                cache_examples=True
            )

    # Button actions
    submit_btn.click(
        predict,
        inputs=input_text,
        outputs=output_labels
    )
    
    clear_btn.click(
        lambda: "",
        outputs=input_text
    )

# Launch the app with simple queue
demo.queue()  # No parameters for older Gradio versions
demo.launch()