Update app.py

app.py

@@ -1,80 +1,80 @@
-import streamlit as st
-from PIL import Image
-import cv2
-import numpy as np
-from ultralytics import YOLO
-import os
-
-# Model Initialization
-
-model = YOLO('C:\\Users\\SRUTHI\\Desktop\\python-app\\yolo11l.pt')
-
-# Adjust the path to your YOLOv11 model
-# Function to detect actions in images
-def detect_action(image_path):
-    results = model.predict(source=image_path, conf=0.25, save=False)
-    result = results[0]
-    detections = [
-        (model.names[int(box.cls[0])], float(box.conf[0])) for box in result.boxes
-    ]
-    
-    # Classify action based on detections
-    action_scores = classify_action(detections)
-    
-    return result.plot(), action_scores
-
-def classify_action(detections):
-    detected_objects = [d[0] for d in detections]
-    
-    action_scores = {
-        'Stealing': 0.0,
-        'Sneaking': 0.0,
-        'Peaking': 0.0,
-        'Normal': 0.0
-    }
-
-    if 'person' in detected_objects:
-        if any(obj in detected_objects for obj in ['backpack', 'handbag', 'suitcase']):
-            action_scores['Stealing'] += 0.4
-        if 'refrigerator' in detected_objects:
-            action_scores['Stealing'] += 0.3
-        if [conf for obj, conf in detections if obj == 'person'][0] < 0.6:
-            action_scores['Sneaking'] += 0.5
-        if len(detected_objects) <= 2:
-            action_scores['Peaking'] += 0.5
-
-    if not any(score > 0.3 for score in action_scores.values()):
-        action_scores['Normal'] = 0.4
-
-    return action_scores
-
-# Streamlit UI
-st.title('Suspicious Activity Detection')
-st.write('Upload an image to detect suspicious activities.')
-
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type="jpg")
-if uploaded_file is not None:
-    # Read the image
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image', use_column_width=True)
-    
-    # Save the uploaded file for processing
-    img_path = "/tmp/uploaded_image.jpg"
-    image.save(img_path)
-    
-    # Predict and display results
-    st.write("Detecting action...")
-    detected_image, action_scores = detect_action(img_path)
-    
-    st.image(detected_image, caption='Detected Image', use_column_width=True)
-    
-    # Display action scores
-    st.write("Action Probability Scores:")
-    for action, score in action_scores.items():
-        st.write(f"{action}: {score:.2%}")
-    
-    # Predict and display the most likely action
-    predicted_action = max(action_scores.items(), key=lambda x: x[1])
-    st.write(f"Predicted Action: {predicted_action[0]} ({predicted_action[1]:.2%} confidence)")
-
+import streamlit as st
+from PIL import Image
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import os
+
+# Model Initialization
+
+model = YOLO(https://colab.research.google.com/drive/1IAyIjPN1J_9s5MhJ0uCsccxfcA0WfXl2?authuser=1#scrollTo=bA29p0kfmhlP)
+
+# Adjust the path to your YOLOv11 model
+# Function to detect actions in images
+def detect_action(image_path):
+    results = model.predict(source=image_path, conf=0.25, save=False)
+    result = results[0]
+    detections = [
+        (model.names[int(box.cls[0])], float(box.conf[0])) for box in result.boxes
+    ]
+    
+    # Classify action based on detections
+    action_scores = classify_action(detections)
+    
+    return result.plot(), action_scores
+
+def classify_action(detections):
+    detected_objects = [d[0] for d in detections]
+    
+    action_scores = {
+        'Stealing': 0.0,
+        'Sneaking': 0.0,
+        'Peaking': 0.0,
+        'Normal': 0.0
+    }
+
+    if 'person' in detected_objects:
+        if any(obj in detected_objects for obj in ['backpack', 'handbag', 'suitcase']):
+            action_scores['Stealing'] += 0.4
+        if 'refrigerator' in detected_objects:
+            action_scores['Stealing'] += 0.3
+        if [conf for obj, conf in detections if obj == 'person'][0] < 0.6:
+            action_scores['Sneaking'] += 0.5
+        if len(detected_objects) <= 2:
+            action_scores['Peaking'] += 0.5
+
+    if not any(score > 0.3 for score in action_scores.values()):
+        action_scores['Normal'] = 0.4
+
+    return action_scores
+
+# Streamlit UI
+st.title('Suspicious Activity Detection')
+st.write('Upload an image to detect suspicious activities.')
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+if uploaded_file is not None:
+    # Read the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Save the uploaded file for processing
+    img_path = "/tmp/uploaded_image.jpg"
+    image.save(img_path)
+    
+    # Predict and display results
+    st.write("Detecting action...")
+    detected_image, action_scores = detect_action(img_path)
+    
+    st.image(detected_image, caption='Detected Image', use_column_width=True)
+    
+    # Display action scores
+    st.write("Action Probability Scores:")
+    for action, score in action_scores.items():
+        st.write(f"{action}: {score:.2%}")
+    
+    # Predict and display the most likely action
+    predicted_action = max(action_scores.items(), key=lambda x: x[1])
+    st.write(f"Predicted Action: {predicted_action[0]} ({predicted_action[1]:.2%} confidence)")
+