Update app.py

app.py

@@ -1,39 +1,34 @@
-# lab1_sentiment_local.py
-
+import gradio as gr
 from transformers import pipeline
 
-# Load sentiment pipeline locally (downloads model once)
 MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
-classifier = pipeline("sentiment-analysis", model=MODEL)
 
-# Our test reviews (in production, load from CSV or database)
-reviews = [
-    {"id": 1, "product": "Wireless Headphones",
-     "text": "Incredible sound quality and the noise cancellation is superb."},
-    {"id": 2, "product": "Wireless Headphones",
-     "text": "Broke after 2 weeks. Cheap plastic. Do not buy."},
-    {"id": 3, "product": "Standing Desk",
-     "text": "Easy to assemble. Sturdy build. Motor is a bit noisy."},
-    {"id": 4, "product": "Standing Desk",
-     "text": "Arrived with a huge scratch on the surface. Disappointed."},
-    {"id": 5, "product": "Coffee Maker",
-     "text": "Makes decent coffee. Nothing special for the price."},
-    {"id": 6, "product": "Coffee Maker",
-     "text": "Best purchase this year! Perfect espresso every morning."},
-    {"id": 7, "product": "Laptop Stand",
-     "text": "It does the job. Average quality."},
-    {"id": 8, "product": "Laptop Stand",
-     "text": "Wobbly and unstable. Returned it immediately."},
-]
+# Load once at startup
+classifier = pipeline(
+    "sentiment-analysis",
+    model=MODEL
+)
+
+label_map = {
+    "LABEL_0": "Negative",
+    "LABEL_1": "Neutral",
+    "LABEL_2": "Positive"
+}
 
-print(f"{'ID':<4} {'Product':<20} {'Sentiment':<10} {'Conf':>6}  Text")
-print("-" * 90)
+def analyze(text):
+    result = classifier(text)[0]
+    return {
+        "Sentiment": label_map[result["label"]],
+        "Confidence": f"{result['score']:.2%}"
+    }
 
-for review in reviews:
-    result = classifier(review["text"])[0]
+demo = gr.Interface(
+    fn=analyze,
+    inputs=gr.Textbox(lines=4, placeholder="Enter review text here..."),
+    outputs="json",
+    title="Product Sentiment Analyzer",
+    description="Runs locally using Transformers inside HF Spaces"
+)
 
-    print(f"{review['id']:<4} "
-          f"{review['product']:<20} "
-          f"{result['label']:<10} "
-          f"{result['score']:>5.1%}  "
-          f"{review['text'][:40]}...")
+if __name__ == "__main__":
+    demo.launch()