# app.py
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load pretrained model and tokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Create sentiment analysis pipeline
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Define inference function
def predict_sentiment(text):
    result = classifier(text)
    label = result[0]['label']
    score = result[0]['score']
    return f"{label} ({score:.2f})"

# Gradio UI
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Type your text here..."),
    outputs="text",
    title="Sentiment Analysis App",
    description="Enter text and get sentiment prediction (positive/negative)."
)

# Launch app
iface.launch()