| import gradio as gr
|
| from transformers import pipeline
|
|
|
|
|
| classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
|
|
|
|
|
| def classify_sentiment(text):
|
| if not text.strip():
|
| return "⚠️ Please enter a sentence to analyze.", ""
|
| result = classifier(text)[0]
|
| label = result['label']
|
| confidence = round(result['score'] * 100, 2)
|
| return f"**Prediction:** {label}", f"**Confidence:** {confidence}%"
|
|
|
|
|
| with gr.Blocks() as demo:
|
| gr.Markdown("# 🔍 Real-Time Sentiment Classifier")
|
| gr.Markdown("""
|
| ### 💡 Model Instruction:
|
| This model is trained for binary sentiment classification — **Positive** and **Negative** only.
|
| Neutral or mixed opinions may be interpreted as leaning toward one side.
|
| For best results, input clearly positive or negative sentences.
|
| """)
|
|
|
| with gr.Row():
|
| with gr.Column():
|
| text_input = gr.Textbox(
|
| label="Enter your sentence below 👇",
|
| placeholder='Example: "I love the features of this app!" or "The update ruined the experience."',
|
| lines=3
|
| )
|
| analyze_button = gr.Button("Analyze Sentiment")
|
|
|
| with gr.Column():
|
| prediction_output = gr.Markdown()
|
| confidence_output = gr.Markdown()
|
|
|
| analyze_button.click(fn=classify_sentiment, inputs=text_input, outputs=[prediction_output, confidence_output])
|
|
|
|
|
| demo.launch()
|
|
|
