Update app.py

app.py

@@ -2,12 +2,11 @@ import torch
 import numpy as np
 import gradio as gr
 from PIL import Image
-import cv2
 
 # Device configuration
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Load optimized YOLOv5s model
+# Load YOLOv5 model
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).to(device)
 
 # Set model confidence threshold
@@ -16,7 +15,7 @@ if device.type == 'cuda':
     model.half()
 
 def process_frame(image):
-    """Processes a frame from the webcam and applies YOLOv5 object detection."""
+    """Process a video frame or image and apply YOLOv5 object detection."""
     if image is None:
         return None
     
@@ -32,35 +31,17 @@ def process_frame(image):
         print(f"Error processing frame: {e}")
         return image
 
-def process_uploaded_image(image):
-    """Processes an uploaded image and applies YOLOv5 object detection."""
-    if image is None:
-        return None
-    
-    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
-    
-    except Exception as e:
-        print(f"Error processing image: {e}")
-        return image
-
 # Create Gradio UI
 with gr.Blocks(title="Real-Time Object Detection") as app:
-    gr.Markdown("# Real-Time Object Detection with Dual Input")
+    gr.Markdown("# Real-Time Object Detection")
 
     with gr.Tabs():
         # 📷 Live Webcam Tab
         with gr.TabItem("📷 Live Camera"):
             with gr.Row():
-                webcam_input = gr.Image(source="webcam", label="Live Feed")  
+                webcam_input = gr.Video(label="Live Feed")  
                 live_output = gr.Image(label="Processed Feed")
             
-            # Process frame when new frame is available
             webcam_input.change(process_frame, inputs=webcam_input, outputs=live_output)
 
         # 🖼️ Image Upload Tab (With Submit Button)
@@ -70,6 +51,6 @@ with gr.Blocks(title="Real-Time Object Detection") as app:
                 submit_button = gr.Button("Submit") 
                 upload_output = gr.Image(label="Detection Result")
 
-            submit_button.click(process_uploaded_image, inputs=upload_input, outputs=upload_output)  
+            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)