File size: 2,897 Bytes
105bf90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | 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()
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