Update app.py

app.py

@@ -0,0 +1,59 @@
+import cv2
+from ultralytics import YOLO
+import gradio as gr
+import tempfile
+import os
+
+# Load trained model
+model = YOLO("best.pt") 
+
+def process_video(input_video):
+    cap = cv2.VideoCapture(input_video)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    tmp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    output_path = tmp_out.name
+    tmp_out.close()
+
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model(frame)[0]
+        for box in results.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            conf = box.conf[0]
+            if conf < 0.5:
+                continue
+            cls = int(box.cls[0])
+            label = f"Cleaner {conf:.2f}"
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 200, 0), 2)
+            cv2.putText(frame, label, (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 200, 0), 2)
+
+        out.write(frame)
+
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    return output_path
+
+with gr.Blocks(css=".gradio-container {background-color: #0f1f3d; color: #cce0ff;}") as demo:
+    gr.Markdown("## Video Object Detection (Bluish Tone)")
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Video(label="Upload Video")
+            btn = gr.Button("Process Video", variant="primary")
+        with gr.Column():
+            out = gr.Video(label="Output Video")
+    btn.click(fn=process_video, inputs=inp, outputs=out)
+
+if __name__ == "__main__":
+    demo.launch()