Update app.py

app.py

@@ -1,100 +1,168 @@
 import streamlit as st
 import cv2
 import numpy as np
-from PIL import Image
 import tempfile
+from PIL import Image
+import torch
+from torchvision import transforms, models
 import time
 
-class DogLanguageInterpreter:
+# Set page config
+st.set_page_config(
+    page_title="Dog Language Understanding",
+    page_icon="🐕",
+    layout="wide"
+)
+
+class DogBehaviorAnalyzer:
     def __init__(self):
-        # Initialize any models or configurations here
-        self.emotions = {
-            'tail_wagging': 'Happy and excited',
-            'ears_perked': 'Alert and interested',
-            'lying_down': 'Relaxed or tired',
-            'barking': 'Trying to communicate',
-            'jumping': 'Enthusiastic and playful'
-        }
-    
-    def analyze_video(self, video_file):
-        # Placeholder for video analysis logic
-        # In a real implementation, this would use CV models
-        return {
-            'primary_emotion': 'Happy and excited',
-            'confidence': 0.85,
-            'actions_detected': ['tail_wagging', 'jumping'],
-            'recommendations': [
-                'Your dog seems very playful!',
-                'Consider engaging in some active play',
-                'Reward this positive energy with treats'
-            ]
+        # 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 main():
-    st.set_page_config(
-        page_title="Dog Language Interpreter",
-        page_icon="🐕",
-        layout="wide"
-    )
+    def analyze_frame(self, frame):
+        # Convert frame to PIL Image
+        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        
+        # Transform image
+        input_tensor = self.transform(image)
+        input_batch = input_tensor.unsqueeze(0)
+        
+        # Simulate behavior detection
+        # In a real implementation, you'd use a properly trained model
+        behaviors = []
+        confidence_scores = np.random.random(len(self.behaviors))
+        
+        for behavior, score in zip(self.behaviors.keys(), confidence_scores):
+            if score > 0.7:  # Threshold for detection
+                behaviors.append(behavior)
+        
+        return behaviors
 
-    # Header
-    st.title("🐕 Pet Dog Language Understanding")
-    st.markdown("""
-    Upload a video of your dog, and our AI will help interpret their behavior and emotions!
-    """)
+def main():
+    st.title("🐕 Dog Language Understanding")
+    st.write("Upload a video of your dog to analyze their behavior!")
 
-    # Initialize interpreter
-    interpreter = DogLanguageInterpreter()
+    # Initialize analyzer
+    analyzer = DogBehaviorAnalyzer()
 
     # File uploader
-    video_file = st.file_uploader("Upload a video of your dog", type=['mp4', 'mov', 'avi'])
+    video_file = st.file_uploader("Upload Video", type=['mp4', 'avi', 'mov'])
 
-    if video_file:
-        # Save video to temporary file
+    if video_file is not None:
+        # Save uploaded file temporarily
         tfile = tempfile.NamedTemporaryFile(delete=False)
         tfile.write(video_file.read())
 
-        # Display video
-        st.video(tfile.name)
-
-        # Analysis button
-        if st.button("Analyze Dog Behavior"):
-            with st.spinner("Analyzing your dog's behavior..."):
-                # Simulate processing time
-                time.sleep(2)
+        # Video analysis
+        cap = cv2.VideoCapture(tfile.name)
+        
+        # Create columns for layout
+        col1, col2 = st.columns(2)
+        
+        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()}
+        
+        frame_count = 0
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
                 
-                # Get analysis results
-                results = interpreter.analyze_video(tfile.name)
-
-                # Display results in columns
-                col1, col2 = st.columns(2)
-
-                with col1:
-                    st.subheader("Primary Emotion")
-                    st.info(results['primary_emotion'])
-                    st.metric("Confidence", f"{results['confidence']*100:.1f}%")
-
-                with col2:
-                    st.subheader("Actions Detected")
-                    for action in results['actions_detected']:
-                        st.success(action.replace('_', ' ').title())
-
-                # Recommendations
-                st.subheader("Recommendations")
-                for rec in results['recommendations']:
-                    st.write("• " + rec)
-
-    # Additional information
-    with st.expander("About Dog Body Language"):
-        st.markdown("""
-        ### Common Dog Behaviors and Their Meanings:
-        - **Tail Wagging**: Usually indicates happiness, but the position matters
-        - **Ears Position**: Can show alertness, submission, or aggression
-        - **Body Posture**: Reveals confidence or anxiety levels
-        - **Facial Expressions**: Can indicate stress, happiness, or attention
+            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_column_width=True
+            )
+            
+            # 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:
+                    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
+            )
+            
+            time.sleep(0.1)  # Add small delay for visualization
+            
+        cap.release()
+        
+        # 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:
+            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 col3:
+            behavior_variety = len([b for b in behavior_counts.values() if b > 0])
+            st.metric("Behavior Variety", f"{behavior_variety} types")
         
-        Remember that each dog is unique and may express emotions differently!
-        """)
+        # 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.")
 
 if __name__ == "__main__":
     main()