app.py

import gradio as gr
import torch
from transformers import AutoTokenizer
from model import SentimentClassifier

model_state_dict = torch.load('sentiment_model.pth')
model = SentimentClassifier(2)
model.load_state_dict(model_state_dict)
model.eval()

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')


def preprocess(text):
    inputs = tokenizer(text, padding='max_length',
                       truncation=True, max_length=512, return_tensors='pt')
    return inputs
# Define a function to use the model to make predictions
def predict(review):
    inputs = preprocess(review)
    with torch.no_grad():
        outputs = model(inputs['input_ids'], inputs['attention_mask'])
        predicted_class = torch.argmax(outputs[0]).item()
    if(predicted_class==0):
        return "It was a negative review"
    return "It was a positive review"

# Create a Gradio interface
input_text = gr.inputs.Textbox(label="Input Text")
output_text = gr.outputs.Textbox(label="Output Text")
interface = gr.Interface(fn=predict, inputs=input_text, outputs=output_text)

# Run the interface
interface.launch()