Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import tempfile
+import os
+from ultralytics import YOLO
+
+def stream_object_detection(video_path, conf_threshold):
+    # Load the YOLO model
+    model = YOLO("weights/best.pt")
+    cap = cv2.VideoCapture(video_path)
+
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2)
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) // 2)
+
+    # Temporary file for processed video
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    temp_file_path = temp_file.name
+
+    # VideoWriter to save processed frames
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(temp_file_path, fourcc, fps, (width, height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = cv2.resize(frame, (width, height))
+
+        # Run YOLO predictions
+        results = model.predict(frame)
+
+        # Annotate frame with detection results
+        annotated_frame = results[0].plot()
+
+        # Write annotated frame to the video file
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+
+    return temp_file_path
+
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(label="Upload Video")
+            conf_threshold = gr.Slider(
+                label="Confidence Threshold",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.30,
+            )
+        with gr.Column():
+            video_output = gr.Video(label="Processed Video")
+    with gr.Row():
+        
+        with gr.Column():
+            detect_button = gr.Button("Start Detection", variant="primary")
+    detect_button.click(
+        fn=stream_object_detection,
+        inputs=[video_input, conf_threshold],
+        outputs=video_output,
+    )
+
+if __name__ == "__main__":
+    app.launch()
+