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from transformers import AutoModelForSequenceClassification, AutoTokenizer
import gradio as gr
# Load model and tokenizer
model_name = "checkpoint-2500/"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define prediction function
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
logits = outputs.logits
probabilities = logits.softmax(dim=1)
sentiment = "Positive" if probabilities[0][1] > 0.5 else "Negative"
return sentiment
# Create Gradio interface
text_input = gr.Textbox(lines=7, label="Input Text", placeholder="Enter your text here...")
output_text = gr.Textbox(label="Predicted Sentiment")
# Author information
author = "Ajeetkumar Ukande"
# Create Gradio interface
interface = gr.Interface(predict_sentiment, text_input, output_text,
title="<div style='color: #336699; font-size: 24px; font-weight: bold; border: 2px solid #336699; padding: 10px; border-radius: 10px;'>Sentiment-Analysis-FineTuned-DistilBERT</div>",
description=f"""<div style='color: #666666; font-family: Arial, sans-serif;'>
<p style='margin-top: 10px;'>This model predicts the sentiment of text.</p>
<p>It uses a fine-tuned DistilBERT model trained on IMDb movie reviews dataset.</p>
<p>The sentiment is classified as Positive if the probability of positive sentiment is greater than 0.5, otherwise it's classified as Negative.</p>
<p>Developed by <span style='color: #336699; font-weight: bold;'>{author}</span>.</p>
</div>""",
theme="huggingface",
allow_flagging=False,
)
interface.launch(share=True)
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