app.py

from typing import Any

import gradio as gr
from transformers import Pipeline, pipeline


MODEL_NAME = "distilbert/distilbert-base-uncased-finetuned-sst-2-english"

classifier: Pipeline = pipeline(
    task="sentiment-analysis",
    model=MODEL_NAME,
)


def analyze_sentiment(text: str) -> dict[str, float]:
    """Analyze text and return probabilities for Gradio's Label component."""

    cleaned_text = text.strip()

    if not cleaned_text:
        raise gr.Error("Please enter a sentence before analyzing.")

    if len(cleaned_text) > 1000:
        raise gr.Error("Please keep the text below 1,000 characters.")

    predictions: list[dict[str, Any]] = classifier(
        cleaned_text,
        top_k=None,
    )

    return {
        prediction["label"].title(): float(prediction["score"])
        for prediction in predictions
    }


examples = [
    ["I loved working on this machine-learning project."],
    ["The application was confusing and frustrating."],
    ["The workshop was useful, but it was quite long."],
]

demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(
        lines=5,
        max_lines=10,
        label="Your text",
        placeholder="Example: Learning Hugging Face is exciting!",
    ),
    outputs=gr.Label(
        label="Sentiment prediction",
        num_top_classes=2,
    ),
    examples=examples,
    title="🤗 Beginner Sentiment Analyzer",
    description=(
        "Enter an English sentence and let a pretrained Hugging Face "
        "model classify its sentiment."
    ),
    article=(
        "This beginner project uses DistilBERT, Transformers, "
        "PyTorch, and Gradio."
    ),
    submit_btn="Analyze sentiment",
    clear_btn="Clear",
)


if __name__ == "__main__":
    demo.launch()