Added: aws s3 replacement

.env.template

@@ -1,3 +1,3 @@
-AWS_ACCESS_KEY_ID=
-AWS_SECRET_ACCESS_KEY=
-USE_LOCAL_DATA=True
+USE_LOCAL_DATA=False
+TITILER_ENDPOINT=
+COG_URL=

inference.py

@@ -11,7 +11,6 @@ import numpy as np
 from rasterio.features import shapes
 from shapely.geometry import shape
 import geopandas as gpd
-import boto3
 from dotenv import load_dotenv
 
 from messis.messis import LogConfusionMatrix
@@ -203,28 +202,15 @@ def crop_predictions_to_gdf(field_ids, targets, predictions, transform, crs, cla
 
     return gdf
 
-def generate_presigned_url(bucket_name, object_key, expiration=3600):
-    """Generate a pre-signed URL for an S3 object."""
-    s3_client = boto3.client('s3', region_name='eu-central-1')
-    presigned_url = s3_client.generate_presigned_url('get_object', Params={'Bucket': bucket_name, 'Key': object_key}, ExpiresIn=expiration)
-    return presigned_url
-
 def perform_inference(lon, lat, model, config, debug=False):
-    # Bucket and object paths
-    bucket_name = 'messis-demo'
-    features_key = 'stacked_features_cog.tif'
-    labels_key = 'labels_cog.tif'
-    field_ids_key = 'field_ids_cog.tif'
-
     if os.environ.get('USE_LOCAL_DATA') == "True":
         features_path = "./data/stacked_features_cog.tif"
         labels_path = "./data/labels_cog.tif"
         field_ids_path = "./data/field_ids_cog.tif"
     else:
-        # Generate pre-signed URLs for the GeoTIFF files
-        features_path = generate_presigned_url(bucket_name, features_key)
-        labels_path = generate_presigned_url(bucket_name, labels_key)
-        field_ids_path = generate_presigned_url(bucket_name, field_ids_key)
+        features_path = "https://huggingface.co/datasets/crop-classification/zueri-crop-2/resolve/main/stacked_features_cog.tif"
+        labels_path = "https://huggingface.co/datasets/crop-classification/zueri-crop-2/resolve/main/labels_cog.tif"
+        field_ids_path = "https://huggingface.co/datasets/crop-classification/zueri-crop-2/resolve/main/field_ids_cog.tif"
 
     # Local paths for stats and dataset info
     stats_path = "./data/chips_stats.yaml"

pages/2_Perform_Crop_Classification.py

@@ -6,7 +6,6 @@ import os
 
 from messis.messis import Messis
 from inference import perform_inference
-from inference import generate_presigned_url
 
 from dotenv import load_dotenv
 
@@ -92,10 +91,10 @@ def perform_inference_step():
             vmax=vmin_vmax[selected_band][1],
         )
     else:
-        presigned_url = generate_presigned_url('messis-demo', 'stacked_features_cog.tif')
         m.add_cog_layer(
-            url=presigned_url,
+            url=os.environ.get("COG_URL"),
             name="Sentinel-2 Satellite Imagery",
+            titiler_endpoint=os.environ.get("TITILER_ENDPOINT"),
             bands=selected_bands,
             rescale=f"{vmin_vmax[selected_band][0]},{vmin_vmax[selected_band][1]}"
         )

requirements.txt

@@ -20,5 +20,4 @@ numpy==1.26.4
 lion-pytorch==0.2.2
 timm==0.9.16
 pyproj
-boto3
 python-dotenv