Update app.py

app.py

@@ -6,29 +6,25 @@ from PIL import Image
 model = YOLO("Suspicious_Activities_nano.pt")
 
 # Define the prediction function
-def predict_suspicious_activity(image, conf_threshold):
-    results = model.predict(source=image, show=False, conf=conf_threshold)
+def predict_suspicious_activity(image):
+    results = model.predict(source=image, show=False, conf=0.6)
     results_img = results[0].plot()  # Get the image with bounding boxes/annotations
-    detected_classes = results[0].names  # Get class names
+    return Image.fromarray(results_img)  # Get class names
     
-    # Create a string of detected classes
-    detected_text = ', '.join(set(detected_classes)) if detected_classes else "No suspicious activities detected."
-    
-    return Image.fromarray(results_img), detected_text  # Return both image and detected classes
 
 # Create Gradio interface
 interface = gr.Interface(
     fn=predict_suspicious_activity,  # Function to run on input
-    inputs=[
-        gr.Image(type="pil"),  # Input type is image (PIL)
-        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.6, label="Confidence Threshold")  # Slider for confidence
-    ],
-    outputs=[
-        gr.Image(type="pil"),  # Output type is image (PIL)
-        gr.Textbox(label="Detected Activities")  # Output type for detected activities
-    ],
+    inputs=gr.Image(type="pil"),  # Input type is image (PIL)
+    outputs=gr.Image(type="pil"),  # Output type is image (PIL)
+
+
+
+
+
+
     title="Suspicious Activity Detection with YOLO",  # Interface title
-    description="Upload an image to detect suspicious activities such as Assault, Fighting, Gun incidents, etc.",  # Description
+    description="Upload an image to detect suspicious activities.",  # Description
 )
 
 # Launch the interface