app.py

# import gradio as gr

# gr.load("models/AnkitAI/reviews-roberta-base-sentiment-analysis").launch()

import gradio as gr
from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "AnkitAI/reviews-roberta-base-sentiment-analysis"
model = RobertaForSequenceClassification.from_pretrained(model_name)
tokenizer = RobertaTokenizer.from_pretrained(model_name)

# Define a function to perform sentiment analysis and map labels
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = torch.argmax(logits, dim=1).item()
    
    # Map class id to label
    labels = ["Negative", "Positive"]
    return labels[predicted_class_id]

# Create a Gradio interface
interface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a review here..."),
    outputs="text",
    title="Reviews Sentiment Analysis",
    description="Enter an Amazon review to see if it is positive or negative."
)

# Launch the interface
interface.launch()