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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()
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