Update app1.py

app1.py

@@ -2,8 +2,8 @@ import cv2
 import numpy as np
 
 # Load the SSD model and configuration
-model_path = 'ssd_mobilenet_v2_coco.pb'   # Path to the pre-trained SSD model
-config_path = ' pipeline.config'           # Path to the deploy prototxt file
+model_path = 'saved_model.pb'   # Path to the pre-trained SSD model
+config_path = 'pipeline.config'           # Path to the deploy prototxt file
 
 # Load the class labels from the COCO dataset
 CLASSES = [
@@ -56,28 +56,35 @@ def detect_objects_in_frame(frame):
 
     return frame
 
-# Capture video from a file or camera (0 for the default camera)
-cap = cv2.VideoCapture(0)  # Use 0 for webcam or provide a path to a video file
+import gradio as gr
+from gradio_webrtc import WebRTC
 
-# Check if the video capture is initialized correctly
-if not cap.isOpened():
-    print("Error: Could not open video stream.")
-    exit()
+css = """.my-group {max-width: 600px !important; max-height: 600px !important;}
+         .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}"""
 
-while True:
-    # Read a new frame from the video feed
-    ret, frame = cap.read()
-    
-    if not ret:
-        print("Error: Failed to read frame from video stream.")
-        break
-    
-    # Detect objects in the current frame
-    output_frame = detect_objects_in_frame(frame)
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+        <h1 style='text-align: center'>
+        YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
+        </h1>
+        """
+    )
+    with gr.Column(elem_classes=["my-column"]):
+        with gr.Group(elem_classes=["my-group"]):
+            image = WebRTC(label="Stream", rtc_configuration=None)
+            conf_threshold = gr.Slider(
+                label="Confidence Threshold",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.30,
+            )
+
+        image.stream(
+            fn=detect_objects_in_frame, inputs=[image, conf_threshold], outputs=[image], time_limit=10
+        )
+
+if __name__ == "__main__":
+    demo.launch()
 
-    # Display the resulting frame
-    cv2.imshow("Detected Objects in Video", output_frame)
-    
-    # Break the loop if the user presses the 'Esc' key
-    if cv2.waitKey(1) & 0xFF == 27:  # 27 is the keycode for 'Esc'
-        break