Update app.py

app.py

@@ -1,29 +1,62 @@
-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"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+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
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+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):
-    # Get predictions
-    predictions = classifier(text)
-    return predictions
+    try:
+        logger.info(f"Classifying text: {text}")
+        predictions = classifier(text)
+        logger.info(f"Predictions: {predictions}")
+        return predictions
+    except Exception as e:
+        logger.error(f"Error classifying text: {e}")
+        return {"error": str(e)}
 
 # Create 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."
-)
+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__":
-    iface.launch()
+    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