app.py

import gradio as gr
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import torch
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

logger.info("Starting the script")

# Load model and tokenizer
model_name = "peterkros/immunization-classification-model"
try:
    logger.info(f"Loading model from {model_name}")
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    logger.info("Model and tokenizer loaded successfully")
except Exception as e:
    logger.error(f"Error loading model and tokenizer: {e}")
    raise e

# Define the pipeline
try:
    logger.info("Setting up the pipeline")
    classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
    logger.info("Pipeline set up successfully")
except Exception as e:
    logger.error(f"Error setting up the pipeline: {e}")
    raise e

def classify_text(text):
    try:
        logger.info(f"Classifying text: {text}")
        predictions = classifier(text)
        logger.info(f"Predictions: {predictions}")
        
        # Process predictions to add the custom logic
        result = []
        for prediction in predictions:
            if prediction['score'] > 0.92:
                label = "Immunization"
            else:
                label = "None"
            result.append({'label': label, 'score': prediction['score']})
        
        logger.info(f"Processed predictions: {result}")
        return result

    except Exception as e:
        logger.error(f"Error classifying text: {e}")
        return {"error": str(e)}

# Create Gradio interface
try:
    logger.info("Setting up Gradio interface")
    iface = gr.Interface(
        fn=classify_text,
        inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
        outputs=gr.JSON(),
        title="Text Classification with DistilBERT",
        description="Enter text to classify it using a DistilBERT model trained for text classification."
    )
    logger.info("Gradio interface set up successfully")
except Exception as e:
    logger.error(f"Error setting up Gradio interface: {e}")
    raise e

# Launch the app
if __name__ == "__main__":
    try:
        logger.info("Launching Gradio interface")
        iface.launch()
        logger.info("Gradio interface launched successfully")
    except Exception as e:
        logger.error(f"Error launching Gradio interface: {e}")
        raise e