Fixed errors

app.py

@@ -6,15 +6,19 @@ import torch
 import gradio as gr
 from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.responses import Response
-from transformers import DepthProImageProcessorFast, DepthProForDepthEstimation
+from transformers import DepthProImageProcessor, DepthProForDepthEstimation
+
 
 # 1. Initialize FastAPI & Load Apple Depth Pro
 fastapi_app = FastAPI(title="True Metric ToF Server")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# Updated to avoid the transformers deprecation crash
+
 print("Loading Apple Depth Pro components...")
-image_processor = DepthProImageProcessorFast.from_pretrained("apple/DepthPro-hf")
+image_processor = DepthProImageProcessor.from_pretrained("apple/DepthPro-hf")
 model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device)
+
 model.eval()
 print(f"Model successfully loaded on target device: {device}")
 
@@ -74,9 +78,11 @@ def create_3d_point_cloud(input_pil_image):
         outputs = model(**inputs)
     
     post_processed = image_processor.post_process_depth_estimation(
-        outputs, target_sizes=[(image.height, image.width)],
-    )
-    depth_matrix = post_processed["predicted_depth"].detach().cpu().numpy().astype(np.float32)
+    outputs, target_sizes=[(image.height, image.width)],
+)
+# Your list index fix: grabbing the first dictionary element out of the post-processed array
+    depth_matrix = post_processed[0]["predicted_depth"].detach().cpu().numpy().astype(np.float32)
+
 
     # Step B: Pinhole camera math projection (u, v, depth) -> (X, Y, Z)
     height, width = depth_matrix.shape

requirements.txt

@@ -2,6 +2,7 @@ fastapi
 uvicorn[standard]
 python-multipart
 torch --index-url https://pytorch.org
+torchvision --index-url https://pytorch.org
 transformers
 accelerate
 numpy