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| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer | |
| import torch | |
| # —— Lazy‑loaded pipelines & tokenizers —— # | |
| summarizer = sentiment = ner = classifier = None | |
| ner_tokenizer = None | |
| def get_summarizer(): | |
| global summarizer | |
| if summarizer is None: | |
| summarizer = pipeline( | |
| "summarization", | |
| model="Curative/t5-summarizer-cnn", | |
| framework="pt" | |
| ) | |
| return summarizer | |
| def get_sentiment(): | |
| global sentiment | |
| if sentiment is None: | |
| sentiment = pipeline( | |
| "sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english", | |
| framework="pt" | |
| ) | |
| return sentiment | |
| def get_classifier(): | |
| global classifier | |
| if classifier is None: | |
| classifier = pipeline( | |
| "zero-shot-classification", | |
| model="facebook/bart-large-mnli", | |
| framework="pt" | |
| ) | |
| return classifier | |
| def get_ner(): | |
| global ner, ner_tokenizer | |
| if ner is None: | |
| # Load Fast tokenizer explicitly for proper aggregation | |
| ner_tokenizer = AutoTokenizer.from_pretrained( | |
| "elastic/distilbert-base-uncased-finetuned-conll03-english", | |
| use_fast=True | |
| ) | |
| ner = pipeline( | |
| "ner", | |
| model="elastic/distilbert-base-uncased-finetuned-conll03-english", | |
| tokenizer=ner_tokenizer, | |
| aggregation_strategy="simple", | |
| framework="pt" | |
| ) | |
| return ner | |
| # —— Helper functions —— # | |
| def chunk_and_summarize(text: str) -> str: | |
| """Split on sentences into ≤1,000 char chunks, summarize each, then join.""" | |
| summarizer = get_summarizer() | |
| max_chunk = 1000 | |
| sentences = text.split(". ") | |
| chunks, current = [], "" | |
| for sent in sentences: | |
| # +2 accounts for the period and space | |
| if len(current) + len(sent) + 2 <= max_chunk: | |
| current += sent + ". " | |
| else: | |
| chunks.append(current.strip()) | |
| current = sent + ". " | |
| if current: | |
| chunks.append(current.strip()) | |
| summaries = [] | |
| for chunk in chunks: | |
| part = summarizer( | |
| chunk, | |
| max_length=150, | |
| min_length=40, | |
| do_sample=False | |
| )[0]["summary_text"] | |
| summaries.append(part) | |
| return " ".join(summaries) | |
| def merge_entities(ents): | |
| """Merge sub‑word tokens (##…) into full words.""" | |
| merged = [] | |
| for e in ents: | |
| w, t = e["word"], e["entity_group"] | |
| if w.startswith("##") and merged: | |
| merged[-1]["word"] += w.replace("##", "") | |
| else: | |
| merged.append({"word": w, "type": t}) | |
| return merged | |
| def process(text, features): | |
| out = {} | |
| if "Summarization" in features: | |
| out["summary"] = chunk_and_summarize(text) # :contentReference[oaicite:7]{index=7} | |
| if "Sentiment" in features: | |
| s = get_sentiment()(text)[0] | |
| out["sentiment"] = {"label": s["label"], "score": s["score"]} | |
| if "Classification" in features: | |
| labels = ["technology","sports","business","politics", | |
| "health","science","travel","entertainment"] | |
| cls = get_classifier()(text, candidate_labels=labels) | |
| # Zip & sort | |
| pairs = sorted( | |
| zip(cls["labels"], cls["scores"]), | |
| key=lambda x: x[1], | |
| reverse=True | |
| ) | |
| out["classification"] = [ | |
| {"label": lbl, "score": scr} for lbl, scr in pairs | |
| ] | |
| if "Entities" in features: | |
| ents = get_ner()(text) | |
| out["entities"] = merge_entities(ents) # :contentReference[oaicite:8]{index=8} | |
| return out | |
| # —— Gradio UI —— # | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🛠️ Multi‑Feature NLP Service") | |
| inp = gr.Textbox(lines=8, 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() | |