Update app.py

app.py

@@ -6,12 +6,6 @@ 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
-from tensorflow.keras import layers, Model
 
 # Set page config
 st.set_page_config(
@@ -22,155 +16,54 @@ st.set_page_config(
 
 class DogBehaviorAnalyzer:
     def __init__(self):
-        # Use YOLOv4 instead of ResNet
-        self.model = self.load_yolov4_model()
-        
-        # Add support for sensor data (if available)
-        self.sensor_model = self.load_sensor_model()
-        
-        # Define CNN-LSTM fusion model
-        self.fusion_model = self.create_fusion_model()
-        
-        # Enhanced behavior detection with confidence thresholds
-        self.behavior_thresholds = {
-            'tail_wagging': 0.85,
-            'barking': 0.90,
-            'ears_perked': 0.85,
-            'lying_down': 0.80,
-            'jumping': 0.85,
-            'standing': 0.80,
-            'sitting': 0.80,
-            'running': 0.90
+        # Initialize model (using pretrained ResNet for this example)
+        self.model = models.resnet50(pretrained=True)
+        self.model.eval()
+        
+        # Define image transformations
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                              std=[0.229, 0.224, 0.225])
+        ])
+        
+        # Define behavior mappings
+        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!'
         }
 
-    def create_fusion_model(self):
-        """Create CNN-LSTM fusion model for better accuracy"""
-        # Implementation based on research paper architecture
-        video_input = layers.Input(shape=(None, 224, 224, 3))
-        sensor_input = layers.Input(shape=(None, sensor_features))
-        
-        # CNN for video processing
-        cnn = layers.Conv2D(64, (3, 3), activation='relu')(video_input)
-        # ... additional CNN layers ...
-        
-        # LSTM for temporal features
-        lstm = layers.LSTM(128, return_sequences=True)(cnn)
-        
-        # Fusion layer
-        fusion = layers.Concatenate()([lstm, sensor_input])
-        
-        # Output layer
-        output = layers.Dense(len(self.behaviors), activation='softmax')(fusion)
-        
-        return Model(inputs=[video_input, sensor_input], outputs=output)
-
-    def analyze_frame(self, frame, sensor_data=None):
-        """Enhanced frame analysis using fusion model"""
-        # Convert frame to appropriate format
-        processed_frame = self.preprocess_frame(frame)
-        
-        if sensor_data is not None:
-            # Use fusion model for more accurate detection
-            predictions = self.fusion_model.predict([processed_frame, sensor_data])
-        else:
-            # Fallback to video-only analysis
-            predictions = self.model.predict(processed_frame)
-        
-        # Apply confidence thresholds
-        detected_behaviors = []
-        for behavior, confidence in zip(self.behaviors.keys(), predictions[0]):
-            if confidence > self.behavior_thresholds[behavior]:
-                detected_behaviors.append({
-                    'behavior': behavior,
-                    'confidence': float(confidence),
-                    'timestamp': time.time()
-                })
-        
-        return detected_behaviors
-
-    def validate_detection(self, behaviors, previous_behaviors):
-        """Add temporal consistency check"""
-        validated_behaviors = []
-        for behavior in behaviors:
-            # Check temporal consistency
-            if self.is_temporally_consistent(behavior, previous_behaviors):
-                validated_behaviors.append(behavior)
-        return validated_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 create_visualization(self, behavior, frame):
-        """Create more accurate behavior visualizations"""
-        # Create overlay on actual frame instead of generic shapes
-        overlay = frame.copy()
+    def analyze_frame(self, frame):
+        # Convert frame to PIL Image
+        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
         
-        # Get dog keypoints using pose estimation
-        keypoints = self.detect_dog_keypoints(frame)
+        # Transform image
+        input_tensor = self.transform(image)
+        input_batch = input_tensor.unsqueeze(0)
         
-        if keypoints is not None:
-            if behavior == 'tail_wagging':
-                # Draw tail trajectory
-                self.draw_tail_trajectory(overlay, keypoints)
-            elif behavior == 'sitting':
-                # Draw pose skeleton
-                self.draw_pose_skeleton(overlay, keypoints)
-            # ... other behaviors ...
+        # Simulate behavior detection
+        # In a real implementation, you'd use a properly trained model
+        behaviors = []
+        confidence_scores = np.random.random(len(self.behaviors))
         
-        return cv2.addWeighted(frame, 0.7, overlay, 0.3, 0)
-
-    def evaluate_detection_quality(self, detections):
-        """Evaluate detection quality using metrics from the paper"""
-        metrics = {
-            'accuracy': 0,
-            'precision': 0,
-            'recall': 0,
-            'f_score': 0
-        }
-        
-        # Calculate metrics based on paper formulas
-        true_positives = len([d for d in detections if d['confidence'] > 0.9])
-        false_positives = len([d for d in detections if d['confidence'] < 0.7])
+        for behavior, score in zip(self.behaviors.keys(), confidence_scores):
+            if score > 0.7:  # Threshold for detection
+                behaviors.append(behavior)
         
-        metrics['precision'] = true_positives / (true_positives + false_positives)
-        # ... calculate other metrics ...
-        
-        return metrics
-
-    def analyze_sequence(self, frames, window_size=30):
-        """Analyze behavior over multiple frames"""
-        sequence_behaviors = []
-        
-        for i in range(len(frames) - window_size):
-            window = frames[i:i+window_size]
-            frame_behaviors = [self.analyze_frame(f) for f in window]
-            
-            # Apply temporal smoothing
-            smoothed_behavior = self.temporal_smoothing(frame_behaviors)
-            sequence_behaviors.append(smoothed_behavior)
-        
-        return sequence_behaviors
+        return behaviors
 
 def main():
     st.title("🐕 Dog Language Understanding")
-    st.write("Upload a video of your dog to analyze their behavior and emotions!")
+    st.write("Upload a video of your dog to analyze their behavior!")
 
+    # Initialize analyzer
     analyzer = DogBehaviorAnalyzer()
+
+    # File uploader
     video_file = st.file_uploader("Upload Video", type=['mp4', 'avi', 'mov'])
 
     if video_file is not None:
@@ -178,7 +71,7 @@ def main():
         tfile = tempfile.NamedTemporaryFile(delete=False)
         tfile.write(video_file.read())
 
-        # Video processing
+        # Video analysis
         cap = cv2.VideoCapture(tfile.name)
         
         # Create columns for layout
@@ -187,18 +80,20 @@ def main():
         with col1:
             st.subheader("Video Preview")
             video_placeholder = st.empty()
-        
+            
+        with col2:
+            st.subheader("Real-time Analysis")
+            analysis_placeholder = st.empty()
+            
+            # 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))
         
-        # Progress bar
-        progress_bar = st.progress(0)
-        progress_text = st.empty()
-
         while cap.isOpened():
             ret, frame = cap.read()
             if not ret:
@@ -207,67 +102,42 @@ 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 periodically (every 5th frame)
-            if frame_count % 5 == 0:
-                video_placeholder.image(
-                    cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),
-                    channels="RGB",
-                    use_container_width=True
-                )
+            # Update video preview
+            video_placeholder.image(
+                cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),
+                channels="RGB",
+                use_container_width=True  # Changed from use_column_width
+            )
             
             # Analyze frame
             detected_behaviors = analyzer.analyze_frame(frame)
             for behavior in detected_behaviors:
                 behavior_counts[behavior] += 1
-                current_emotions.add(behavior)
-        
-        cap.release()
-        progress_text.empty()
-        
-        # 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
-        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
+            
+            # Update analysis display
+            analysis_text = "Detected Behaviors:\n\n"
+            for behavior, count in behavior_counts.items():
+                if count > 0:
+                    analysis_text += f"• {behavior.replace('_', ' ').title()}: {count} times\n"
+                    analysis_text += f"  {analyzer.behaviors[behavior]}\n\n"
+            
+            analysis_placeholder.text_area(
+                "Analysis Results",
+                analysis_text,
+                height=300,
+                key=f"analysis_{frame_count}"  # Added unique key for each frame
             )
-            st.plotly_chart(fig, use_container_width=True)
+            
+            time.sleep(0.1)  # Add small delay for visualization
+            
+        cap.release()
         
-        # Summary metrics
+        # Final summary
         st.subheader("Analysis Summary")
+        st.write("Overall behavior analysis of your dog:")
+        
+        # Create summary metrics
         col1, col2, col3 = st.columns(3)
         
         with col1:
@@ -282,23 +152,18 @@ def main():
             behavior_variety = len([b for b in behavior_counts.values() if b > 0])
             st.metric("Behavior Variety", f"{behavior_variety} types")
         
-        # Final recommendations
+        # Recommendations
+        st.subheader("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 your dog's dominant behavior ({dominant_behavior.replace('_', ' ')}):
-            
-            {' '.join(analyzer.behaviors[dominant_behavior]['tips'])}
-            
-            **General recommendations:**
+            st.write("""
+            Based on the analysis, here are some 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.warning("No behaviors detected. Try uploading a different video with clearer dog movements.")
+            st.write("No behaviors detected. Try uploading a different video with clearer dog movements.")
 
 if __name__ == "__main__":
     main()