Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+# Charger le modèle YOLOv8 pré-entraîné
+model = YOLO("yolov8n.pt")
+
+# Fonction pour la détection sur image
+def detect_objects_image(img):
+    results = model(img)  # Détection
+    annotated_frame = results[0].plot()  # Annoter les résultats
+    return annotated_frame
+
+
+def detect_objects_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out_path = "annotated_video.mp4"
+    out = cv2.VideoWriter(out_path, fourcc, 20.0, (int(cap.get(3)), int(cap.get(4))))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        result = model(frame)
+        annotated = result[0].plot()
+        out.write(annotated)
+
+    cap.release()
+    out.release()
+    return out_path
+
+demo = gr.Blocks()
+
+#Interface Gradio
+
+image_input = gr.Image(type="numpy", label="Image à analyser")
+video_input = gr.Image(label="Video à analyser")
+
+image_output = gr.Image(type="numpy", label="Image annotée")
+video_output = gr.Image(label="Video annotée")
+
+interface1 = gr.Interface(fn=detect_objects_image, inputs=image_input, outputs=image_output, title="Détection sur Image")
+interface2 = gr.Interface(fn=detect_objects_video, inputs=video_input, outputs=video_output, title="Détection sur Video")
+
+with demo: 
+  gr.TabbedInterface(
+      [interface1, interface2],
+      ['Image', 'Video']
+  )
+demo.launch()