app.py

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

# Load pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Define a function to make predictions using the model
def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=-1).item()
    return predicted_class

# Create Gradio interface
iface = gr.Interface(fn=predict, inputs="text", outputs="text", live=True)

# Launch the Gradio app
iface.launch()