app.py

import gradio as gr
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch

# ✅ Load the model from Hugging Face Hub
MODEL_NAME = "Kaiyeee/fine_tuned_distilbert_imdb"  # Update this with your actual repo name
tokenizer = DistilBertTokenizer.from_pretrained(MODEL_NAME)
model = DistilBertForSequenceClassification.from_pretrained(MODEL_NAME)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    sentiment = "positive" if predicted_class_id == 1 else "negative"
    return sentiment

# ✅ Create a Gradio interface
demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=5, placeholder="Enter text for sentiment analysis..."),
    outputs="text",
    title="Sentiment Analysis with DistilBERT",
    description="Enter text to predict sentiment (positive or negative)."
)

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