app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import os

# Define the model path within the Space
MODEL_PATH = "./model"

# Load your model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)

# Create a Hugging Face pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

def predict_sentiment(text):
    result = classifier(text)[0]
    label = result['label']
    score = result['score']
    return f"Label: {label}, Score: {score:.4f}"

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
    outputs="text",
    title="PolyGuard Model Demo",
    description="A simple Gradio interface to demonstrate the PolyGuard model."
)

iface.launch(share=True)