Update app.py

app.py

@@ -1,72 +1,66 @@
 import gradio as gr
 from transformers import pipeline
 
-# 1️⃣ Lazy‑load your pipelines
-summarizer = None
-sentiment  = None
-classifier = None
-ner        = None
-
-def get_summarizer():
-    global summarizer
-    if summarizer is None:
-        summarizer = pipeline("summarization", model="Curative/t5-summarizer-cnn")
-    return summarizer
+# Lazy‑load pipelines
+sentiment = classifier = ner = summarizer = None
 
 def get_sentiment():
     global sentiment
-    if sentiment is None:
-        sentiment = pipeline("sentiment-analysis", model="DT12the/distilbert-sentiment-analysis")
+    if not sentiment:
+        sentiment = pipeline("sentiment-analysis",
+            model="distilbert-base-uncased-finetuned-sst-2-english")
     return sentiment
 
 def get_classifier():
     global classifier
-    if classifier is None:
-        classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+    if not classifier:
+        classifier = pipeline("text-classification",
+            model="textattack/distilbert-base-uncased-ag-news")
     return classifier
 
 def get_ner():
     global ner
-    if ner is None:
-        ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
+    if not ner:
+        ner = pipeline("ner",
+            model="elastic/distilbert-base-uncased-finetuned-conll03-english",
+            aggregation_strategy="simple")
     return ner
 
-# 2️⃣ Processing function
+def get_summarizer():
+    global summarizer
+    if not summarizer:
+        summarizer = pipeline("summarization",
+            model="Curative/t5-summarizer-cnn")
+    return summarizer
+
 def process(text, features):
-    """Run only the selected features on the input text."""
-    results = {}
+    result = {}
     if "Summarization" in features:
-        summ = get_summarizer()(text, max_length=150, min_length=40, do_sample=False)[0]["summary_text"]
-        results["summary"] = summ
+        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]
-        results["sentiment"] = sent
+        result["sentiment"] = {"label": sent["label"], "score": sent["score"]}
     if "Classification" in features:
         cls = get_classifier()(text)[0]
-        results["classification"] = cls
+        result["classification"] = {"label": cls["label"], "score": cls["score"]}
     if "Entities" in features:
         ents = get_ner()(text)
-        # Format entities as list of dicts
-        results["entities"] = [{"word": e["word"], "type": e["entity_group"]} for e in ents]
-    return results
+        result["entities"] = [
+            {"word": e["word"], "type": e["entity_group"]} for e in ents
+        ]
+    return result
 
-# 3️⃣ Build the Gradio Blocks UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 📚 Multi‑Feature NLP Demo")
-    text_input = gr.Textbox(lines=5, placeholder="Enter your text here…")
-    feature_select = gr.CheckboxGroup(
-        choices=["Summarization", "Sentiment", "Classification", "Entities"],
-        label="Select features to run",
-        info="You can pick one or more models to apply"
-    )
-    run_button = gr.Button("Run")
-    output = gr.JSON(label="Results")
-
-    run_button.click(
-        fn=process,
-        inputs=[text_input, feature_select],
-        outputs=output
+    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)
 
-# 4️⃣ Launch with API enabled
 demo.queue(api_open=True).launch()