Done

app.py

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+import gradio as gr
+
+
+
+import os
+import json
+from ultralytics import YOLO
+import supervision  as sv
+
+
+# --- 1. CONFIGURATION ---
+
+# Folder where the script is
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Go up one level to Om_Singh
+PROJECT_DIR = os.path.dirname(BASE_DIR)  # parent folder of app/
+
+# Paths for outputs in video_and_json folder
+VIDEO_DIR = os.path.join(PROJECT_DIR, "video_and_json")  # Om_Singh/video_and_json
+os.makedirs(VIDEO_DIR, exist_ok=True)  # ensure folder exists
+
+MODEL_PATH = os.path.join(BASE_DIR, "best.pt")  # model stays in app/
+OUTPUT_VIDEO_PATH = os.path.join(VIDEO_DIR, "output.mp4")
+OUTPUT_JSON_PATH  = os.path.join(VIDEO_DIR, "result.json")
+
+def process_video(INPUT_VIDEO_PATH, OUTPUT_VIDEO_PATH, OUTPUT_JSON_PATH):
+    print("Loading model...")
+    model = YOLO(MODEL_PATH)
+
+    print("Initializing tracker and annotators...")
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoxAnnotator(thickness=5)
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.TOP_CENTER, text_scale = 1, text_thickness= 1)
+
+    sv.LabelAnnotator()
+
+    frame_generator = sv.get_video_frames_generator(source_path=INPUT_VIDEO_PATH)
+    video_info = sv.VideoInfo.from_video_path(INPUT_VIDEO_PATH)
+
+    results_list = []
+
+    with sv.VideoSink(target_path=OUTPUT_VIDEO_PATH, video_info=video_info) as sink:
+        print("Processing video frames...")
+        for frame_number, frame in enumerate(frame_generator):
+            # Run YOLO prediction
+            results = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(results)
+
+            # Update tracker
+            tracked_detections = tracker.update_with_detections(detections=detections)
+
+            # Prepare labels
+            labels = [
+                f"ID: {det[4]} {model.model.names[int(det[3])]}"
+                for det in tracked_detections
+            ]
+
+            # Annotate frame
+            annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=tracked_detections)
+            annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=tracked_detections, labels=labels)
+
+            # Save tracking info
+            for det in tracked_detections:
+                bbox = det[0]
+                conf = det[2]
+                class_id = int(det[3])
+                tracker_id = det[4]
+
+                results_list.append({
+                    "frame_number": frame_number,
+                    "track_id": int(tracker_id),
+                    "class": model.model.names[class_id],
+                    "confidence": float(conf),
+                    "bounding_box": [int(coord) for coord in bbox]
+                })
+
+            # Write annotated frame
+            sink.write_frame(frame=annotated_frame)
+
+            if frame_number % 30 == 0:
+                print(f"Processed frame {frame_number}...")
+
+    print("Video processing complete. Saving results...")
+    with open(OUTPUT_JSON_PATH, 'w') as f:
+        json.dump(results_list, f, indent=4)
+
+    print("--- All tasks finished successfully! ---")
+
+
+
+# --- Main processing function ---
+def process(input_video):
+    output_video = "output.mp4"
+    output_json = "result.json"
+    
+    # During processing: red text
+    status_html = "<p style='color:red; font-weight:bold;'>Processing...</p>"
+    
+    # Run video processing
+    process_video(input_video, output_video, output_json)
+    
+    # After processing: green text
+    status_html = "<p style='color:limegreen; font-weight:bold;'>Processing complete!</p>"
+    return status_html, output_video, output_json
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align:center;'>Vehicle and Pedestrian Tracker</h1>")
+
+    input_video = gr.Video(label="Upload Video")
+    start_btn = gr.Button("Start Tracking")
+    status_display = gr.HTML("")  # Initially empty
+    output_video = gr.Video(label="Processed Video")
+    output_json = gr.File(label="Download JSON Output")
+
+    start_btn.click(
+        fn=process,
+        inputs=input_video,
+        outputs=[status_display, output_video, output_json]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share = True)