enhnacements

app.py

@@ -6,6 +6,11 @@ from PIL import Image
 import torch
 from torchvision import transforms, models
 import time
+import plotly.graph_objects as go
+from PIL import Image, ImageDraw
+import base64
+from io import BytesIO
+import pandas as pd
 
 # Set page config
 st.set_page_config(
@@ -28,13 +33,53 @@ class DogBehaviorAnalyzer:
                               std=[0.229, 0.224, 0.225])
         ])
         
-        # Define behavior mappings
+        # Enhanced behavior mappings with emotions and tips
         self.behaviors = {
-            'tail_wagging': 'Your dog is happy and excited!',
-            'barking': 'Your dog is trying to communicate or alert you.',
-            'ears_perked': 'Your dog is alert and interested.',
-            'lying_down': 'Your dog is relaxed and comfortable.',
-            'jumping': 'Your dog is energetic and playful!'
+            'tail_wagging': {
+                'emotion': 'Happy and excited',
+                'description': 'Your dog is expressing joy and enthusiasm!',
+                'tips': [
+                    'This is a great time for positive reinforcement training',
+                    'Consider engaging in play or exercise',
+                    'Use this excitement for teaching new tricks'
+                ]
+            },
+            'barking': {
+                'emotion': 'Alert or communicative',
+                'description': 'Your dog is trying to communicate or alert you.',
+                'tips': [
+                    'Check what triggered the barking',
+                    'Use positive reinforcement for quiet behavior',
+                    'Consider training "quiet" and "speak" commands'
+                ]
+            },
+            'ears_perked': {
+                'emotion': 'Alert and interested',
+                'description': 'Your dog is focused and attentive.',
+                'tips': [
+                    'Great moment for training exercises',
+                    'Consider mental stimulation activities',
+                    'Use this attention for bonding exercises'
+                ]
+            },
+            'lying_down': {
+                'emotion': 'Relaxed and comfortable',
+                'description': 'Your dog is calm and at ease.',
+                'tips': [
+                    'Perfect time for gentle petting',
+                    'Maintain a peaceful environment',
+                    'Consider quiet bonding activities'
+                ]
+            },
+            'jumping': {
+                'emotion': 'Excited and playful',
+                'description': 'Your dog is energetic and seeking interaction!',
+                'tips': [
+                    'Channel energy into structured play',
+                    'Practice "four paws on floor" training',
+                    'Consider agility exercises'
+                ]
+            }
         }
 
     def analyze_frame(self, frame):
@@ -56,9 +101,27 @@ class DogBehaviorAnalyzer:
         
         return behaviors
 
+    def create_animation(self, behavior):
+        """Create simple animations for behaviors"""
+        # Create a simple animation frame
+        img = Image.new('RGB', (200, 200), 'white')
+        draw = ImageDraw.Draw(img)
+        
+        if behavior == 'tail_wagging':
+            # Draw a simple tail wagging animation
+            draw.arc([50, 50, 150, 150], 0, 180, fill='black', width=2)
+        elif behavior == 'barking':
+            # Draw speech-bubble like shapes
+            draw.ellipse([50, 50, 150, 150], outline='black', width=2)
+        
+        # Convert to base64 for display
+        buffered = BytesIO()
+        img.save(buffered, format="PNG")
+        return base64.b64encode(buffered.getvalue()).decode()
+
 def main():
     st.title("🐕 Dog Language Understanding")
-    st.write("Upload a video of your dog to analyze their behavior!")
+    st.write("Upload a video of your dog to analyze their behavior and emotions!")
 
     # Initialize analyzer
     analyzer = DogBehaviorAnalyzer()
@@ -84,12 +147,14 @@ def main():
         with col2:
             st.subheader("Real-time Analysis")
             analysis_placeholder = st.empty()
+            emotion_placeholder = st.empty()
             
-            # Progress bar
-            progress_bar = st.progress(0)
-            
+        # Progress bar
+        progress_bar = st.progress(0)
+        
         # Analysis results storage
         behavior_counts = {behavior: 0 for behavior in analyzer.behaviors.keys()}
+        current_emotions = set()
         
         frame_count = 0
         total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
@@ -114,25 +179,54 @@ def main():
             detected_behaviors = analyzer.analyze_frame(frame)
             for behavior in detected_behaviors:
                 behavior_counts[behavior] += 1
+                current_emotions.add(behavior)
             
-            # Update analysis display
-            analysis_text = "Detected Behaviors:\n\n"
+            # 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"  {analyzer.behaviors[behavior]}\n\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,
-                key=f"analysis_{frame_count}"  # Added unique key for each frame
+                height=300
             )
             
-            time.sleep(0.1)  # Add small delay for visualization
+            # 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()
         
+        # Final summary with enhanced visualizations
+        st.subheader("Analysis Summary")
+        
+        # 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)
+        
         # Final summary
         st.subheader("Analysis Summary")
         st.write("Overall behavior analysis of your dog:")
@@ -152,11 +246,16 @@ def main():
             behavior_variety = len([b for b in behavior_counts.values() if b > 0])
             st.metric("Behavior Variety", f"{behavior_variety} types")
         
-        # Recommendations
-        st.subheader("Recommendations")
+        # Enhanced recommendations
+        st.subheader("Personalized Recommendations")
         if total_behaviors > 0:
-            st.write("""
-            Based on the analysis, here are some 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}:
+            
+            {' '.join(analyzer.behaviors[dominant_behavior]['tips'])}
+            
+            General recommendations:
             - Maintain regular exercise routines
             - Provide mental stimulation through toys and training
             - Continue positive reinforcement training