Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+import tempfile
+# charger le modèle
+model = YOLO("yolov8n-seg.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):
+    # Si l'entrée est une chaîne, utiliser telle quelle. Sinon, utiliser .name (cas Gradio)
+    video_path = video.name if hasattr(video, 'name') else video
+
+    temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    cap = cv2.VideoCapture(video_path)
+
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(temp_output.name, fourcc, fps, (width, height))
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model(frame)
+        annotated_frame = results[0].plot()
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+
+    return temp_output.name
+
+
+demo = gr.Blocks()
+
+#Interface Gradio
+image_input = gr.Image(type="numpy", label="Image à analyser")
+
+image_output = gr.Image(type="numpy", label="Image annotée")
+
+
+interface = gr.Interface(fn=detect_objects_image, inputs=image_input, outputs=image_output, title="Détection sur Image")
+
+interface.launch()