app.py

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

#load model
model = AutoModelForSequenceClassification.from_pretrained("./")
tokenizer = AutoTokenizer.from_pretrained("./")


# Define the inference function
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
    outputs = model(**inputs)
    predicted_label = torch.argmax(outputs.logits).item()
    label_map = {0: "Negative", 1: "Positive"}
    return label_map[predicted_label]

# Gradio interface set-up
title = "Movie Review Sentiment Analysis"
description = ("Enter a movie review and find out whether it's Positive or Negative! "
               "The fine-tuned distilbert-base-uncased model trained on the imdb dataset will try to classify your review.\n\n"
               "Below are some examples you can try")
review = gr.Textbox(lines=10, label="Enter your movie review here...")
prediction = gr.Textbox(label="Sentiment Label (Prediction)")
examples = [
    ["The movie was amazing!"],
    ["I didn't like the movie, it was really boring and the cast was terrible"],
    ["Absolutely loved it! One of the best movies of all time"]
]

intf = gr.Interface(fn=predict_sentiment,
                   title=title,
                   description=description,
                   inputs=review,
                   outputs=prediction,
                   examples=examples)

intf.launch(inline=False)