Update app.py

app.py

@@ -15,7 +15,15 @@ st.set_page_config(
 )
 
 class DogBehaviorAnalyzer:
-    def _init_(self):
+
+    behaviors = {
+        'tail_wagging': {'description': 'Your dog is happy and excited!', 'threshold': 0.75},
+        'barking': {'description': 'Your dog is trying to communicate or alert you.', 'threshold': 0.80},
+        'ears_perked': {'description': 'Your dog is alert and interested.', 'threshold': 0.70},
+        'lying_down': {'description': 'Your dog is relaxed and comfortable.', 'threshold': 0.85},
+        'jumping': {'description': 'Your dog is energetic and playful!', 'threshold': 0.75}
+    }
+    def __init__(self):
         # Use a more sophisticated pre-trained model (ResNet50 with ImageNet weights)
         self.model = models.resnet50(pretrained=True)
         
@@ -96,109 +104,105 @@ def main():
     video_file = st.file_uploader("Upload Video", type=['mp4', 'avi', 'mov', 'mkv'])
 
     if video_file is not None:
-        try:
-            # Save uploaded file temporarily
-            tfile = tempfile.NamedTemporaryFile(delete=False)
-            tfile.write(video_file.read())
+        # Save uploaded file temporarily
+        tfile = tempfile.NamedTemporaryFile(delete=False)
+        tfile.write(video_file.read())
 
-            # Video analysis
-            cap = cv2.VideoCapture(tfile.name)
+        # Video analysis
+        cap = cv2.VideoCapture(tfile.name)
             
-            # Create columns for layout
-            col1, col2 = st.columns(2)
+        # Create columns for layout
+        col1, col2 = st.columns(2)
             
-            with col1:
+        with col1:
                 st.subheader("Video Preview")
-                video_placeholder = st.empty()
-                
-            with col2:
-                st.subheader("Real-time Analysis")
-                analysis_placeholder = st.empty()
+            video_placeholder = st.empty()
+
+        with col2:
+            st.subheader("Real-time Analysis")
+            analysis_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()}
+        # Analysis results storage
+        behavior_counts = {behavior: 0 for behavior in analyzer.behaviors.keys()}
             
-            frame_count = 0
-            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_count = 0
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
             
-            while cap.isOpened():
-                ret, frame = cap.read()
-                if not ret:
-                    break
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
                     
-                frame_count += 1
-                progress = frame_count / total_frames
-                progress_bar.progress(progress)
+            frame_count += 1
+            progress = frame_count / total_frames
+            progress_bar.progress(progress)
                 
-                # 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
+            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
+            # Analyze frame
+            detected_behaviors = analyzer.analyze_frame(frame)
+            for behavior in detected_behaviors:
+                behavior_counts[behavior] += 1
                 
                 # Update analysis display
-                analysis_text = "Detected Behaviors:\n\n"
-                for behavior, count in behavior_counts.items():
-                    if count > 0:
-                        confidence = sum(behavior_scores[behavior]) / count
-                        analysis_text += (f"• {behavior.replace('_', ' ').title()}: "
-                                        f"{count} times (Confidence: {confidence:.2%})\n"
+            analysis_text = "Detected Behaviors:\n\n"
+            for behavior, count in behavior_counts.items():
+                if count > 0:
+                    confidence = sum(behavior_scores[behavior]) / count
+                    analysis_text += (f"• {behavior.replace('_', ' ').title()}: "
+                                    f"{count} times (Confidence: {confidence:.2%})\n"
                                         f"  {analyzer.behaviors[behavior]['description']}\n\n")
                 
-                analysis_placeholder.text_area(
+            analysis_placeholder.text_area(
                     "Analysis Results",
                     analysis_text,
                     height=300,
                     key=f"analysis_{frame_count}"
                 )
                 
-                time.sleep(0.1)  # Add small delay for visualization
+            time.sleep(0.1)  # Add small delay for visualization
                 
-            cap.release()
+        cap.release()
             
-            # Final summary
-            st.subheader("Analysis Summary")
-            st.write("Overall behavior analysis of your dog:")
+        # Final summary
+        st.subheader("Analysis Summary")
+        st.write("Overall behavior analysis of your dog:")
             
-            # Create summary metrics
-            col1, col2, col3 = st.columns(3)
+        # Create summary metrics
+        col1, col2, col3 = st.columns(3)
             
-            with col1:
-                most_common = max(behavior_counts.items(), key=lambda x: x[1])[0]
-                st.metric("Most Common Behavior", most_common.replace('_', ' ').title())
+        with col1:
+            most_common = max(behavior_counts.items(), key=lambda x: x[1])[0]
+            st.metric("Most Common Behavior", most_common.replace('_', ' ').title())
                 
-            with col2:
-                total_behaviors = sum(behavior_counts.values())
-                st.metric("Total Behaviors Detected", total_behaviors)
+        with col2:
+            total_behaviors = sum(behavior_counts.values())
+            st.metric("Total Behaviors Detected", total_behaviors)
                 
-            with col3:
-                behavior_variety = len([b for b in behavior_counts.values() if b > 0])
-                st.metric("Behavior Variety", f"{behavior_variety} types")
+        with col3:
+            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")
-            if total_behaviors > 0:
-                st.write("""
+        # Recommendations
+        st.subheader("Recommendations")
+        if total_behaviors > 0:
+            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.write("No behaviors detected. Try uploading a different video with clearer dog movements.")
-
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}")
+        else:
+            st.write("No behaviors detected. Try uploading a different video with clearer dog movements.")
 
 if __name__ == "__main__":
     main()