Update app.py

app.py

@@ -3,54 +3,32 @@ import numpy as np
 import gradio as gr
 from PIL import Image
 
-# Device configuration
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Load YOLOv5 model
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).to(device)
 
-# Set model confidence threshold
-model.conf = 0.5
 if device.type == 'cuda':
     model.half()
 
-def process_frame(image):
-    """Process a video frame or image and apply YOLOv5 object detection."""
-    if image is None:
-        return None
+def detect_objects(image):
+    image_pil = Image.fromarray(image)
     
-    try:
-        image_pil = Image.fromarray(image)
-        with torch.no_grad():
-            results = model(image_pil)
-        
-        rendered_images = results.render()
-        return np.array(rendered_images[0]) if rendered_images else image
+    with torch.no_grad():
+        results = model(image_pil)
     
-    except Exception as e:
-        print(f"Error processing frame: {e}")
-        return image
-
-# Create Gradio UI
-with gr.Blocks(title="Real-Time Object Detection") as app:
-    gr.Markdown("# Real-Time Object Detection")
-
-    with gr.Tabs():
-        # 📷 Live Webcam Tab
-        with gr.TabItem("📷 Live Camera"):
-            with gr.Row():
-                webcam_input = gr.Video(label="Live Feed")  
-                live_output = gr.Image(label="Processed Feed")
-            
-            webcam_input.change(process_frame, inputs=webcam_input, outputs=live_output)
-
-        # 🖼️ Image Upload Tab (With Submit Button)
-        with gr.TabItem("🖼️ Image Upload"):
-            with gr.Row():
-                upload_input = gr.Image(type="numpy", label="Upload Image")
-                submit_button = gr.Button("Submit") 
-                upload_output = gr.Image(label="Detection Result")
-
-            submit_button.click(process_frame, inputs=upload_input, outputs=upload_output)
-
-app.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
+    rendered_images = results.render()
+    
+    return np.array(rendered_images[0]) if rendered_images else image
+
+# Gradio interface
+iface = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Detected Objects"),
+    title="Object Detection with YOLOv5",
+    description="Use webcam or upload an image to detect objects.",
+    allow_flagging="never",
+    examples=["spring_street_after.jpg", "pexels-hikaique-109919.jpg"]
+)
+
+iface.launch()