Update app.py

app.py

@@ -1,47 +1,31 @@
 import gradio as gr
 from transformers import pipeline
 
-# Use a global variable to lazy-load the model only when first called
+# Global variable for the classifier
 classifier = None
 
-def get_classifier():
+def classify_emotion(text: str):
     global classifier
+    # Load model on first request
     if classifier is None:
         classifier = pipeline(
             "text-classification",
             model="j-hartmann/emotion-english-roberta-large",
             return_all_scores=True,
+            device_map="auto"  # automatically uses GPU if available
         )
-    return classifier
-
-def classify_emotion(text: str):
-    clf = get_classifier()
-    # returns list of {label, score}
-    scores = clf(text)[0]
+    scores = classifier(text)[0]
     return {item["label"]: float(item["score"]) for item in scores}
 
-# Build the Gradio interface
+# Gradio interface
 iface = gr.Interface(
     fn=classify_emotion,
-    inputs=gr.Textbox(
-        lines=2,
-        placeholder="Type any English sentence here…",
-        label="Input Text"
-    ),
-    outputs=gr.Label(
-        num_top_classes=6,
-        label="Emotion Probabilities"
-    ),
-    examples=[
-        ["I love you!"],
-        ["The movie was heart breaking!"]
-    ],
+    inputs=gr.Textbox(lines=2, placeholder="Type any English sentence…"),
+    outputs=gr.Label(num_top_classes=6),
+    examples=[["I love you!"], ["The movie was heart breaking!"]],
     title="English Emotion Classifier",
     description=(
-        "Predicts one of Ekman's 6 basic emotions plus neutral "
-        "(anger 🤬, disgust 🤢, fear 😨, joy 😀, neutral 😐, "
-        "sadness 😭, surprise 😲)."
+        "Predicts one of Ekman's 6 basic emotions plus neutral:\n"
+        "anger 🤬, disgust 🤢, fear 😨, joy 😀, neutral 😐, sadness 😭, surprise 😲"
     )
-)
-
-# DO NOT call iface.launch() in Hugging Face Spaces
+)