Spaces:
Sleeping
Sleeping
File size: 2,102 Bytes
e56279f 950842b e56279f fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be 950842b fc959be |
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 |
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("text-classification",
model="textattack/distilbert-base-uncased-ag-news")
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:
cls = get_classifier()(text)[0]
result["classification"] = {"label": cls["label"], "score": cls["score"]}
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()
|