Update app.py

app.py

@@ -123,10 +123,7 @@ def main():
     st.title("🐕 Dog Language Understanding")
     st.write("Upload a video of your dog to analyze their behavior and emotions!")
 
-    # Initialize analyzer
     analyzer = DogBehaviorAnalyzer()
-
-    # File uploader
     video_file = st.file_uploader("Upload Video", type=['mp4', 'avi', 'mov'])
 
     if video_file is not None:
@@ -134,7 +131,7 @@ def main():
         tfile = tempfile.NamedTemporaryFile(delete=False)
         tfile.write(video_file.read())
 
-        # Video analysis
+        # Video processing
         cap = cv2.VideoCapture(tfile.name)
         
         # Create columns for layout
@@ -143,14 +140,6 @@ def main():
         with col1:
             st.subheader("Video Preview")
             video_placeholder = st.empty()
-            
-        with col2:
-            st.subheader("Real-time Analysis")
-            analysis_placeholder = st.empty()
-            emotion_placeholder = st.empty()
-            
-        # Progress bar
-        progress_bar = st.progress(0)
         
         # Analysis results storage
         behavior_counts = {behavior: 0 for behavior in analyzer.behaviors.keys()}
@@ -159,6 +148,10 @@ def main():
         frame_count = 0
         total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
         
+        # Progress bar
+        progress_bar = st.progress(0)
+        progress_text = st.empty()
+
         while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
@@ -167,71 +160,67 @@ def main():
             frame_count += 1
             progress = frame_count / total_frames
             progress_bar.progress(progress)
+            progress_text.text(f"Analyzing video: {int(progress * 100)}%")
             
-            # Update video preview
-            video_placeholder.image(
-                cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),
-                channels="RGB",
-                use_container_width=True  # Changed from use_column_width
-            )
+            # Update video preview periodically (every 5th frame)
+            if frame_count % 5 == 0:
+                video_placeholder.image(
+                    cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),
+                    channels="RGB",
+                    use_container_width=True
+                )
             
             # Analyze frame
             detected_behaviors = analyzer.analyze_frame(frame)
             for behavior in detected_behaviors:
                 behavior_counts[behavior] += 1
                 current_emotions.add(behavior)
-            
-            # Update analysis display with enhanced information
-            analysis_text = "Detected Behaviors & Emotions:\n\n"
-            for behavior, count in behavior_counts.items():
-                if count > 0:
-                    behavior_info = analyzer.behaviors[behavior]
-                    analysis_text += f"• {behavior.replace('_', ' ').title()}: {count} times\n"
-                    analysis_text += f"  Emotion: {behavior_info['emotion']}\n"
-                    analysis_text += f"  {behavior_info['description']}\n\n"
-                    
-                    # Add animation for the behavior
-                    animation_data = analyzer.create_animation(behavior)
-                    st.image(f"data:image/png;base64,{animation_data}", 
-                            width=100, 
-                            caption=f"{behavior.replace('_', ' ').title()} Animation")
-            
-            analysis_placeholder.text_area(
-                "Analysis Results",
-                analysis_text,
-                height=300
-            )
-            
-            # Display training tips
-            if len(current_emotions) > 0:
-                st.subheader("Training Tips")
-                for behavior in current_emotions:
-                    tips = analyzer.behaviors[behavior]['tips']
-                    for tip in tips:
-                        st.info(tip)
-            
-            time.sleep(0.1)
         
         cap.release()
+        progress_text.empty()
         
-        # Final summary with enhanced visualizations
-        st.subheader("Analysis Summary")
+        # Display final analysis
+        st.subheader("Behavior Analysis Results")
         
+        # Display detected behaviors and their interpretations
+        for behavior, count in behavior_counts.items():
+            if count > 0:
+                with st.expander(f"{behavior.replace('_', ' ').title()} - Detected {count} times"):
+                    behavior_info = analyzer.behaviors[behavior]
+                    st.write(f"**Emotion:** {behavior_info['emotion']}")
+                    st.write(f"**Description:** {behavior_info['description']}")
+                    
+                    # Display behavior animation
+                    animation_data = analyzer.create_animation(behavior)
+                    st.image(
+                        f"data:image/png;base64,{animation_data}",
+                        width=100,
+                        caption=f"{behavior.replace('_', ' ').title()} Visual"
+                    )
+                    
+                    # Display training tips
+                    st.subheader("Training Tips:")
+                    for tip in behavior_info['tips']:
+                        st.info(tip)
+
         # Create emotion timeline
-        emotions_df = pd.DataFrame(list(current_emotions), columns=['Emotion'])
-        fig = go.Figure(data=[go.Scatter(x=emotions_df.index, 
-                                       y=emotions_df['Emotion'],
-                                       mode='lines+markers')])
-        fig.update_layout(title='Emotional Journey',
-                         xaxis_title='Time',
-                         yaxis_title='Emotion')
-        st.plotly_chart(fig)
+        if current_emotions:
+            st.subheader("Emotional Journey")
+            emotions_df = pd.DataFrame(list(current_emotions), columns=['Emotion'])
+            fig = go.Figure(data=[go.Scatter(
+                x=emotions_df.index,
+                y=emotions_df['Emotion'],
+                mode='lines+markers'
+            )])
+            fig.update_layout(
+                xaxis_title='Time',
+                yaxis_title='Emotion',
+                height=400
+            )
+            st.plotly_chart(fig, use_container_width=True)
         
-        # Final summary
+        # Summary metrics
         st.subheader("Analysis Summary")
-        st.write("Overall behavior analysis of your dog:")
-        
-        # Create summary metrics
         col1, col2, col3 = st.columns(3)
         
         with col1:
@@ -246,23 +235,23 @@ def main():
             behavior_variety = len([b for b in behavior_counts.values() if b > 0])
             st.metric("Behavior Variety", f"{behavior_variety} types")
         
-        # Enhanced recommendations
-        st.subheader("Personalized Recommendations")
+        # Final recommendations
         if total_behaviors > 0:
+            st.subheader("Personalized Recommendations")
             dominant_behavior = max(behavior_counts.items(), key=lambda x: x[1])[0]
             st.write(f"""
-            Based on the analysis, here are personalized recommendations for {dominant_behavior}:
+            Based on the analysis, here are personalized recommendations for your dog's dominant behavior ({dominant_behavior.replace('_', ' ')}):
             
             {' '.join(analyzer.behaviors[dominant_behavior]['tips'])}
             
-            General recommendations:
+            **General recommendations:**
             - Maintain regular exercise routines
             - Provide mental stimulation through toys and training
             - Continue positive reinforcement training
             - Monitor your dog's body language for better communication
             """)
         else:
-            st.write("No behaviors detected. Try uploading a different video with clearer dog movements.")
+            st.warning("No behaviors detected. Try uploading a different video with clearer dog movements.")
 
 if __name__ == "__main__":
     main()