app.py

# app.py
import gradio as gr
from transformers import pipeline
import logging
import sys
import pandas as pd

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    stream=sys.stdout,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Initialize the pipeline
logger.info("Initializing emotion classification pipeline...")
classifier = pipeline(
    "text-classification",
    model="bhadresh-savani/distilbert-base-uncased-emotion"
)
logger.info("Pipeline initialized successfully")

def predict_emotion(text):
    """Predict emotions from text and return formatted results."""
    try:
        if not text:
            logger.warning("Empty text received")
            return {}

        # Get predictions and handle result structure
        logger.info("Running prediction...")
        predictions = classifier(text)
        logger.debug(f"Raw predictions output: {predictions}")
        
        # Process predictions into a dict for display
        scores = {item['label']: item['score'] for item in predictions}
        logger.info(f"Processed scores: {scores}")
        return scores
        
    except Exception as e:
        logger.error(f"Error in prediction: {str(e)}")
        return {"error": "An error occurred during emotion prediction"}

# Create the Gradio interface
demo = gr.Interface(
    fn=predict_emotion,
    inputs=gr.Textbox(
        placeholder="Enter text to analyze...", 
        label="Input Text",
        lines=4
    ),
    outputs=gr.JSON(),  # Display the scores in a JSON format
    title="CREATIVE MACHINES: Emotion Detection with DistilBERT",
    description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter any text to analyze its emotional content.",
    examples=[
        "I am so happy to see you!",
        "I'm really angry about what happened.",
        "The sunset was absolutely beautiful today.",
        "I'm worried about the upcoming exam.",
        "Fear is the mind-killer. I will face my fear."
    ],
    allow_flagging="never"
)

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