app.py

from transformers import pipeline
import gradio as gr

# Load your model & tokenizer from your saved local folder or HF repo
model_path = "Nainglinthu/quest_model"  # your Hugging Face model repo name

# Initialize pipeline once
classifier = pipeline("text-classification", model=model_path)

# Define function to classify text
def classify_text(text):
    results = classifier(text)
    return results

# Gradio interface setup
iface = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=5, placeholder="Enter legal text here..."),
    outputs=gr.JSON(),
    title="Legal Text Classification",
    description="Classify legal text using your fine-tuned Legal BERT model."
)

if __name__ == "__main__":
    iface.launch()