Update app.py

app.py

@@ -1,11 +1,72 @@
 import gradio as gr
 from transformers import pipeline
 
-summarizer = pipeline("summarization", model="Curative/t5-summarizer-cnn")
+# 1️⃣ Lazy‑load your pipelines
+summarizer = None
+sentiment  = None
+classifier = None
+ner        = None
 
-def summarize(text):
-    result = summarizer(text, max_length=150, min_length=40, do_sample=False)
-    return result[0]['summary_text']
+def get_summarizer():
+    global summarizer
+    if summarizer is None:
+        summarizer = pipeline("summarization", model="Curative/t5-summarizer-cnn")
+    return summarizer
 
-demo = gr.Interface(fn=summarize, inputs="text", outputs="text")
-demo.launch()
+def get_sentiment():
+    global sentiment
+    if sentiment is None:
+        sentiment = pipeline("sentiment-analysis", model="DT12the/distilbert-sentiment-analysis")
+    return sentiment
+
+def get_classifier():
+    global classifier
+    if classifier is None:
+        classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+    return classifier
+
+def get_ner():
+    global ner
+    if ner is None:
+        ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
+    return ner
+
+# 2️⃣ Processing function
+def process(text, features):
+    """Run only the selected features on the input text."""
+    results = {}
+    if "Summarization" in features:
+        summ = get_summarizer()(text, max_length=150, min_length=40, do_sample=False)[0]["summary_text"]
+        results["summary"] = summ
+    if "Sentiment" in features:
+        sent = get_sentiment()(text)[0]
+        results["sentiment"] = sent
+    if "Classification" in features:
+        cls = get_classifier()(text)[0]
+        results["classification"] = cls
+    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
+
+# 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
+    )
+
+# 4️⃣ Launch with API enabled
+demo.queue(api_open=True).launch()