first try

app.py

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+import torch
+import gradio as gr
+import numpy as np
+from PIL import Image
+import cv2
+
+# Загрузка модели
+midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
+midas.eval()
+
+midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
+transform = midas_transforms.small_transform
+
+def predict_depth(image):
+    img = np.array(image)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    input_tensor = transform(img_rgb).unsqueeze(0)
+
+    with torch.no_grad():
+        prediction = midas(input_tensor)
+        prediction = torch.nn.functional.interpolate(
+            prediction.unsqueeze(1),
+            size=img_rgb.shape[:2],
+            mode="bicubic",
+            align_corners=False,
+        ).squeeze()
+
+    depth_map = prediction.cpu().numpy()
+    depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
+    depth_map = (depth_map * 255).astype(np.uint8)
+    depth_img = Image.fromarray(depth_map)
+
+    return depth_img
+
+# Интерфейс Gradio
+iface = gr.Interface(
+    fn=predict_depth,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="MiDaS Depth Estimation",
+    description="Загрузите изображение и получите карту глубины."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+torchvision
+timm
+opencv-python
+gradio