app.py

import gradio as gr
from transformers import pipeline

# The pipeline will automatically load the model and tokenizer
# from the current directory where you've put the files.
try:
    classifier = pipeline("text-classification", model="./", tokenizer="./")

    def classify_text(text):
        """
        Classifies a single piece of text and returns a human-readable prediction.
        """
        if not text:
            return "Please enter some text to classify."
            
        result = classifier(text)[0]
        label = "Hate Speech" if result['label'] == 'LABEL_1' else "Not Hate Speech"
        score = result['score']
        return f"Prediction: {label}\nConfidence: {score:.4f}"

    # Create the Gradio interface
    iface = gr.Interface(
        fn=classify_text,
        inputs=gr.Textbox(lines=5, placeholder="Enter a comment in English or Hindi..."),
        outputs=gr.Textbox(label="Result"),
        title="Multilingual Hate Speech Classifier",
        description="A model to classify comments in Hindi and English."
    )
    
    iface.launch()
    
except Exception as e:
    # A simple error message box for the user
    gr.Interface(
        lambda x: f"An error occurred: {e}",
        inputs="text",
        outputs="text",
        title="Error Loading Model",
        description="There was an issue loading the model. Please check your files and dependencies."
    ).launch()