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import gradio as gr | |
from transformers import pipeline | |
# Load a tweet classification model from Hugging Face | |
classifier = pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis") | |
def classify_tweet(tweet): | |
result = classifier(tweet)[0] | |
label = result['label'] | |
score = result['score'] | |
return f"Label: {label} (Confidence: {score:.2f})" | |
# Create the Gradio interface | |
iface = gr.Interface( | |
fn=classify_tweet, | |
inputs=gr.Textbox(lines=3, placeholder="Enter a tweet here..."), | |
outputs="text", | |
title="Tweet Classifier", | |
description="Enter a tweet and click the button to classify it!" | |
) | |
# Launch the app | |
iface.launch() |