Upload app.py with huggingface_hub

app.py

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+
+import gradio as gr
+from transformers import pipeline
+
+# Load model from Hub
+classifier = pipeline(
+    "sentiment-analysis",
+    model="Nav772/distilbert-rotten-tomatoes-sentiment"
+)
+
+def predict_sentiment(text):
+    """
+    Takes user input text and returns sentiment prediction.
+    """
+    if not text.strip():
+        return "Please enter some text."
+    
+    result = classifier(text)[0]
+    label = result["label"]
+    confidence = result["score"]
+    
+    return f"{label} (confidence: {confidence:.2%})"
+
+# Create interface
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(
+        label="Enter a movie review",
+        placeholder="Type your review here...",
+        lines=3
+    ),
+    outputs=gr.Textbox(label="Prediction"),
+    title="🎬 Movie Review Sentiment Analyzer",
+    description="This model predicts whether a movie review is positive or negative. Fine-tuned on the Rotten Tomatoes dataset using DistilBERT.",
+    examples=[
+        ["This film was a masterpiece of storytelling and visual effects."],
+        ["Boring plot, terrible acting, waste of money."],
+        ["It was okay, not great but not terrible either."]
+    ],
+    theme="soft"
+)
+
+demo.launch()