🔧 Fix button interaction issue in Gradio app

Problem: Buttons were not clickable due to scoping issues
Solution:
- Moved event handler functions outside of interface creation scope
- Added global app_instance variable for proper state management
- Simplified function structure to ensure accessibility
- Fixed JavaScript event binding issues

Changes:
- Redefined all button event handlers as global functions
- Used global app_instance for state management
- Maintained all functionality while fixing interaction
- Added error handling for uninitialized app state

🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -14,6 +14,9 @@
 import os
 import pandas as pd
 
+# Global app instance
+app_instance = None
+
 class SentimentGradioApp:
     def __init__(self, model_name="5CD-AI/Vietnamese-Sentiment-visobert", max_batch_size=10):
         self.model_name = model_name
@@ -246,12 +249,58 @@ def batch_predict(self, texts):
             self.cleanup_memory()
             return [], f"❌ Error during batch processing: {str(e)}"
 
+# Define functions outside of interface creation for better scoping
+def analyze_sentiment(text):
+    if not app_instance:
+        return "❌ App not initialized. Please refresh the page.", gr.BarPlot(visible=False)
+
+    result, output = app_instance.predict_sentiment(text)
+    if result:
+        # Prepare data for confidence plot
+        plot_data = pd.DataFrame([
+            {"sentiment": "Negative", "confidence": result["probabilities"]["Negative"]},
+            {"sentiment": "Neutral", "confidence": result["probabilities"]["Neutral"]},
+            {"sentiment": "Positive", "confidence": result["probabilities"]["Positive"]}
+        ])
+        return output, gr.BarPlot(visible=True, value=plot_data)
+    else:
+        return output, gr.BarPlot(visible=False)
+
+def clear_inputs():
+    return "", "", gr.BarPlot(visible=False)
+
+def analyze_batch(texts):
+    if not app_instance:
+        return "❌ App not initialized. Please refresh the page."
+
+    if texts:
+        text_list = [line.strip() for line in texts.split('\n') if line.strip()]
+        results, summary = app_instance.batch_predict(text_list)
+        return summary
+    return "❌ Please enter some texts to analyze."
+
+def clear_batch():
+    return ""
+
+def update_memory_info():
+    if not app_instance:
+        return "App not initialized"
+    return f"{app_instance.get_memory_usage():.1f}MB used"
+
+def manual_memory_cleanup():
+    if not app_instance:
+        return "App not initialized"
+    app_instance.cleanup_memory()
+    return f"Memory cleaned. Current usage: {app_instance.get_memory_usage():.1f}MB"
+
 def create_interface():
     """Create the Gradio interface for Hugging Face Spaces"""
-    app = SentimentGradioApp()
+    global app_instance
+
+    app_instance = SentimentGradioApp()
 
     # Load model
-    if not app.load_model():
+    if not app_instance.load_model():
         print("❌ Failed to load model. Please try again.")
         return None
 
@@ -327,12 +376,12 @@ def create_interface():
             # Batch Analysis Tab
             with gr.Tab("📊 Batch Analysis"):
                 gr.Markdown(f"### 📝 Memory-Efficient Batch Processing")
-                gr.Markdown(f"**Maximum batch size:** {app.max_batch_size} texts (for memory efficiency)")
-                gr.Markdown(f"**Memory limit:** {app.max_memory_mb}MB")
+                gr.Markdown(f"**Maximum batch size:** {app_instance.max_batch_size} texts (for memory efficiency)")
+                gr.Markdown(f"**Memory limit:** {app_instance.max_memory_mb}MB")
 
                 batch_input = gr.Textbox(
                     label="Enter Multiple Texts (one per line)",
-                    placeholder=f"Enter up to {app.max_batch_size} Vietnamese texts, one per line...",
+                    placeholder=f"Enter up to {app_instance.max_batch_size} Vietnamese texts, one per line...",
                     lines=8,
                     max_lines=20
                 )
@@ -345,7 +394,7 @@ def create_interface():
                 batch_result_output = gr.Markdown(label="Batch Analysis Result")
                 memory_info = gr.Textbox(
                     label="Memory Usage",
-                    value=f"{app.get_memory_usage():.1f}MB used",
+                    value=f"{app_instance.get_memory_usage():.1f}MB used",
                     interactive=False
                 )
 
@@ -355,16 +404,16 @@ def create_interface():
                 ## 🤖 Model Details
 
                 **Model Architecture:** Transformer-based sequence classification
-                **Base Model:** {app.model_name}
+                **Base Model:** {app_instance.model_name}
                 **Languages:** Vietnamese (optimized)
                 **Labels:** Negative, Neutral, Positive
-                **Max Batch Size:** {app.max_batch_size} texts
+                **Max Batch Size:** {app_instance.max_batch_size} texts
 
                 ## 📊 Performance Metrics
 
                 - **Processing Speed:** ~100ms per text
                 - **Max Sequence Length:** 512 tokens
-                - **Memory Limit:** {app.max_memory_mb}MB
+                - **Memory Limit:** {app_instance.max_memory_mb}MB
 
                 ## 💡 Usage Tips
 
@@ -376,7 +425,7 @@ def create_interface():
                 ## 🛡️ Memory Management
 
                 - **Automatic Cleanup:** Memory is cleaned after each prediction
-                - **Batch Limits:** Maximum {app.max_batch_size} texts per batch to prevent overflow
+                - **Batch Limits:** Maximum {app_instance.max_batch_size} texts per batch to prevent overflow
                 - **Memory Monitoring:** Real-time memory usage tracking
                 - **GPU Optimization:** CUDA cache clearing when available
 
@@ -388,43 +437,9 @@ def create_interface():
                 - Model loaded directly from Hugging Face Hub (no local training required)
                 """)
 
-        # Event handlers
-        def analyze_text(text):
-            result, output = app.predict_sentiment(text)
-            if result:
-                # Prepare data for confidence plot
-                plot_data = pd.DataFrame([
-                    {"sentiment": "Negative", "confidence": result["probabilities"]["Negative"]},
-                    {"sentiment": "Neutral", "confidence": result["probabilities"]["Neutral"]},
-                    {"sentiment": "Positive", "confidence": result["probabilities"]["Positive"]}
-                ])
-                return output, gr.BarPlot(visible=True, value=plot_data)
-            else:
-                return output, gr.BarPlot(visible=False)
-
-        def clear_inputs():
-            return "", "", gr.BarPlot(visible=False)
-
-        def analyze_batch(texts):
-            if texts:
-                text_list = [line.strip() for line in texts.split('\n') if line.strip()]
-                results, summary = app.batch_predict(text_list)
-                return summary
-            return "❌ Please enter some texts to analyze."
-
-        def clear_batch():
-            return ""
-
-        def update_memory_info():
-            return f"{app.get_memory_usage():.1f}MB used"
-
-        def manual_memory_cleanup():
-            app.cleanup_memory()
-            return f"Memory cleaned. Current usage: {app.get_memory_usage():.1f}MB"
-
         # Connect events
         analyze_btn.click(
-            fn=analyze_text,
+            fn=analyze_sentiment,
             inputs=[text_input],
             outputs=[result_output, confidence_plot]
         )