app.py

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

# Load the model and tokenizer from the Hugging Face Hub
model = AutoModelForSequenceClassification.from_pretrained("preetidav/sentomodel")
tokenizer = AutoTokenizer.from_pretrained("preetidav/sentomodel")

# Function to predict sentiment
def predict_sentiment(text):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    # Forward pass through the model
    outputs = model(**inputs)
    # Get the prediction (0 or 1 for binary classification)
    prediction = torch.argmax(outputs.logits, dim=1).item()
    # Map prediction to sentiment labels
    return "positive" if prediction == 1 else "negative"

# Set up the Gradio interface
iface = gr.Interface(
    fn=predict_sentiment,              # Function to call
    inputs=gr.Textbox(label="Input Text"),  # Input field for the text
    outputs=gr.Textbox(label="Sentiment"), # Output field for the sentiment
    title="Sentiment Analysis Model",    # Title of the app
    description="This model predicts whether a given text has positive or negative sentiment.",  # Description of the app
)

# Launch the app
iface.launch()