Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLOv10
+import cv2
+import spaces
+
+# Load YOLOv10 model
+model = YOLOv10.from_pretrained('jameslahm/yolov10x')
+
+# Define object categories to classify activities
+activity_categories = {
+    "Working": ["laptop", "computer", "keyboard", "office chair"],
+    "Meal Time": ["fork", "spoon", "plate", "food"],
+    "Exercise": ["dumbbell", "bicycle", "yoga mat", "treadmill"],
+    "Outdoors": ["car", "tree", "bicycle", "road"],
+    # Add more categories and associated objects as needed
+}
+
+# Function to map detected objects to categorized activities
+def categorize_activity(detected_objects):
+    activity_summary = {}
+    
+    for activity, objects in activity_categories.items():
+        if any(obj in detected_objects for obj in objects):
+            if activity not in activity_summary:
+                activity_summary[activity] = 0
+            activity_summary[activity] += 1  # Increase count for that activity
+    
+    return activity_summary
+
+# Function to process the video and generate the journal
+@spaces.GPU
+def generate_journal(video):
+    cap = cv2.VideoCapture(video)
+    frame_rate = cap.get(cv2.CAP_PROP_FPS)
+    journal_entries = {}
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Make predictions using YOLOv10
+        results = model.predict(source=frame)
+        detected_objects = [res.name for res in results]
+        
+        # Get current timestamp in the video
+        timestamp = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000  # Convert ms to seconds
+        
+        # Categorize the detected objects into activities
+        activity_summary = categorize_activity(detected_objects)
+        
+        # Store the activities with their timestamp
+        for activity, count in activity_summary.items():
+            if activity not in journal_entries:
+                journal_entries[activity] = []
+            journal_entries[activity].append(f"At {timestamp:.2f} seconds: {count} objects related to {activity}")
+    
+    cap.release()
+    
+    # Create a formatted journal
+    formatted_journal = []
+    for activity, entries in journal_entries.items():
+        formatted_journal.append(f"**{activity}:**")
+        formatted_journal.extend(entries)
+    
+    return "\n".join(formatted_journal)
+
+# Gradio interface for uploading video and generating journal
+iface = gr.Interface(
+    fn=generate_journal,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="Generated Daily Journal"),
+    title="AI-Powered Daily Journal"
+)
+
+iface.launch()