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| import gradio as gr | |
| from transformers import pipeline | |
| # Lazy‑load pipelines | |
| sentiment = classifier = ner = summarizer = None | |
| def get_sentiment(): | |
| global sentiment | |
| if not sentiment: | |
| sentiment = pipeline("sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english") | |
| return sentiment | |
| def get_classifier(): | |
| global classifier | |
| if not classifier: | |
| classifier = pipeline( | |
| "zero-shot-classification", | |
| model="facebook/bart-large-mnli") | |
| return classifier | |
| def get_ner(): | |
| global ner | |
| if not ner: | |
| ner = pipeline("ner", | |
| model="elastic/distilbert-base-uncased-finetuned-conll03-english", | |
| aggregation_strategy="simple") | |
| return ner | |
| def get_summarizer(): | |
| global summarizer | |
| if not summarizer: | |
| summarizer = pipeline("summarization", | |
| model="Curative/t5-summarizer-cnn") | |
| return summarizer | |
| def process(text, features): | |
| result = {} | |
| if "Summarization" in features: | |
| result["summary"] = get_summarizer()( | |
| text, max_length=150, min_length=40, do_sample=False | |
| )[0]["summary_text"] | |
| if "Sentiment" in features: | |
| sent = get_sentiment()(text)[0] | |
| result["sentiment"] = {"label": sent["label"], "score": sent["score"]} | |
| if "Classification" in features: | |
| candidate_labels = [ | |
| "technology", "sports", "business", "politics", | |
| "health", "science", "travel", "entertainment" | |
| ] | |
| cls = get_classifier()(text, candidate_labels=candidate_labels) | |
| # Map labels → scores | |
| result["classification"] = dict(zip(cls["labels"], cls["scores"])) | |
| if "Entities" in features: | |
| ents = get_ner()(text) | |
| result["entities"] = [ | |
| {"word": e["word"], "type": e["entity_group"]} for e in ents | |
| ] | |
| return result | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🛠️ Multi‑Feature NLP Service") | |
| inp = gr.Textbox(lines=6, placeholder="Enter your text here…") | |
| feats = gr.CheckboxGroup( | |
| ["Summarization","Sentiment","Classification","Entities"], | |
| label="Select features to run" | |
| ) | |
| btn = gr.Button("Run") | |
| out = gr.JSON(label="Results") | |
| btn.click(process, [inp, feats], out) | |
| demo.queue(api_open=True).launch() | |