| import gradio as gr |
| import spaces |
| from transformers import pipeline |
|
|
| MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english" |
|
|
| classifier = pipeline("sentiment-analysis", model=MODEL_NAME, device=0) |
|
|
|
|
| @spaces.GPU |
| def analyze_single(text): |
| if not text or not text.strip(): |
| return "Please enter some text.", None |
| result = classifier(text)[0] |
| label = result["label"] |
| score = result["score"] |
| emoji = "😊" if label == "POSITIVE" else "😞" |
| label_display = f"{emoji} {label} ({score:.1%} confidence)" |
| return label_display, {label: score, ("NEGATIVE" if label == "POSITIVE" else "POSITIVE"): 1 - score} |
|
|
|
|
| @spaces.GPU |
| def analyze_batch(batch_text): |
| lines = [line.strip() for line in batch_text.split("\n") if line.strip()] |
| if not lines: |
| return [["Please enter at least one line of text.", "", ""]] |
| results = classifier(lines) |
| rows = [] |
| for line, r in zip(lines, results): |
| rows.append([line, r["label"], f"{r['score']:.1%}"]) |
| return rows |
|
|
|
|
| with gr.Blocks(title="Sentiment Analysis Demo") as demo: |
| gr.Markdown( |
| f"# 🤗 Sentiment Analysis Demo\n" |
| f"Classify text as **positive** or **negative** using " |
| f"[`{MODEL_NAME}`](https://huggingface.co/{MODEL_NAME})." |
| ) |
|
|
| with gr.Tab("Single text"): |
| text_input = gr.Textbox( |
| label="Enter text to analyze", |
| placeholder="I really loved this movie, the acting was fantastic!", |
| lines=3, |
| ) |
| analyze_btn = gr.Button("Analyze", variant="primary") |
| label_output = gr.Textbox(label="Result", interactive=False) |
| confidence_output = gr.Label(label="Confidence breakdown") |
|
|
| analyze_btn.click( |
| fn=analyze_single, |
| inputs=text_input, |
| outputs=[label_output, confidence_output], |
| ) |
| text_input.submit( |
| fn=analyze_single, |
| inputs=text_input, |
| outputs=[label_output, confidence_output], |
| ) |
|
|
| with gr.Tab("Batch analysis"): |
| gr.Markdown("Enter multiple lines of text (one per line) to classify them all at once.") |
| batch_input = gr.Textbox( |
| label="Batch text", |
| placeholder="This product exceeded my expectations!\nWorst customer service I've ever had.\nIt was okay, nothing special.", |
| lines=6, |
| ) |
| batch_btn = gr.Button("Analyze batch", variant="primary") |
| batch_output = gr.Dataframe( |
| headers=["Text", "Sentiment", "Confidence"], |
| label="Results", |
| ) |
|
|
| batch_btn.click( |
| fn=analyze_batch, |
| inputs=batch_input, |
| outputs=batch_output, |
| ) |
|
|
| gr.Markdown( |
| "---\nBuilt with [Gradio](https://gradio.app) and " |
| "[🤗 Transformers](https://huggingface.co/docs/transformers)." |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
