Movie Review Sentiment Analysis - BERT

app-old.py

@@ -0,0 +1,8 @@
+#lesson81.py
+import gradio as gr
+
+def sentiment_analysis(text):
+    return "Positive" if "good" in text.lower() else "Negative"
+
+iface = gr.Interface(fn=sentiment_analysis, inputs="text", outputs="text")
+iface.launch()

app.py

@@ -1,8 +1,60 @@
-#lesson81.py
+# Import required libraries
 import gradio as gr
+from transformers import pipeline
 
-def sentiment_analysis(text):
-    return "Positive" if "good" in text.lower() else "Negative"
+def create_sentiment_analyzer():
+    """Initialize the BERT sentiment analyzer"""
+    return pipeline("sentiment-analysis", model="zavora/bert-sentiment-imdb")
 
-iface = gr.Interface(fn=sentiment_analysis, inputs="text", outputs="text")
-iface.launch()
+def analyze_sentiment(text, classifier):
+    """
+    Analyze sentiment of input text using BERT model
+    Returns sentiment and confidence score
+    """
+    try:
+        if not text.strip():
+            return "Please enter some text"
+            
+        result = classifier(text)[0]
+        label = result['label']
+        confidence = result['score']
+        
+        sentiment = "Positive" if label == "LABEL_1" else "Negative"
+        # Format the output
+        return f"Sentiment: {sentiment}\nConfidence: {confidence:.2%}"
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create and cache the classifier
+classifier = create_sentiment_analyzer()
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=lambda text: analyze_sentiment(text, classifier),
+    inputs=[
+        gr.Textbox(
+            lines=4,
+            placeholder="Enter your movie review here...",
+            label="Movie Review"
+        )
+    ],
+    outputs=[
+        gr.Textbox(
+            label="Analysis Result"
+        )
+    ],
+    title="Movie Review Sentiment Analysis",
+    description="""This app uses a BERT model fine-tuned on IMDB movie reviews to analyze sentiment.
+                   Enter your movie review and get an analysis of whether it's positive or negative.""",
+    examples=[
+        ["This movie was absolutely fantastic! The acting was superb and the plot kept me engaged throughout."],
+        ["I couldn't sit through this movie. The plot was confusing and the acting was terrible."],
+        ["While the movie had some good moments, overall it was just average."]
+    ],
+    theme=gr.themes.Soft()
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()