app.py

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

# Load the model from the hub
model = AutoModelForSequenceClassification.from_pretrained("MALEKSAHLIA/fine-tuned-sentiment-model-imdb")
tokenizer = AutoTokenizer.from_pretrained("MALEKSAHLIA/fine-tuned-sentiment-model-imdb")

# Create a pipeline for sentiment analysis
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

def predict_sentiment(sentence):
    result = nlp(sentence)
    sentiment = "Positive" if result[0]['label'] == 'LABEL_1' else "Negative"  # Adjust the label to match your model's output
    return sentiment

iface = gr.Interface(
    fn=predict_sentiment, 
    inputs="text", 
    outputs="text",
    title="Sentiment Analysis",
    description="Enter a sentence to get the sentiment (Positive or Negative)."
)

iface.launch()