Update app.py

app.py

@@ -10,6 +10,7 @@ import shutil
 import rasterio
 import cv2
 import tensorflow as tf
+ import tempfile
 
 # Configuration
 HEIGHT = WIDTH = 256
@@ -106,7 +107,14 @@ def normalizeImages(images, typeData):
     print("(INFO..) Normalization Image Done")
     return np.array(normalized_images)
 
-        
+
+
+def save_uploaded_file(uploaded_file, suffix=".tif"):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+        tmp.write(uploaded_file.read())
+        return tmp.name  
+
+
 # Streamlit App Title
 st.title("Satellite Mining Segmentation: SAR + Optic Image Inference")
 
@@ -120,19 +128,28 @@ num_samples = st.slider("Number of test samples to visualize", 1, 10, 3)
 if sar_file is not None and optic_file is not None and mask_file is not None:
     st.success("All files uploaded successfully!")
     st.write(f"Number of samples selected for visualization: {num_samples}")
+    sar_path = save_uploaded_file(sar_file, suffix=".tif")
+    optic_path = save_uploaded_file(optic_file, suffix=".tif")
+    mask_path = save_uploaded_file(mask_file, suffix=".tif")
+
+    st.write("Temporary paths created for inference:")
+    st.code(f"SAR Path: {sar_path}")
+    st.code(f"Optic Path: {optic_path}")
+    st.code(f"Mask Path: {mask_path}")
+    
+    
 else:
     st.warning("Please upload all three .tiff files to proceed.")
     
-sarImages = [sar_file]
-opticImages = [optic_file]
-masks = [mask_file]
+sarImages = [sar_path]
+opticImages = [optic_path]
+masks = [mask_path]
 model_path = "Residual_UNET_Bilinear.keras"
 
 
 if st.button("Run Inference"):
     with st.spinner("Loading data and model..."):
 
-
         sar_images = readImages(sarImages, typeData='s', width=WIDTH, height=HEIGHT)
         optic_images = readImages(opticImages, typeData='o', width=WIDTH, height=HEIGHT)
         masks = readImages(masks, typeData='m', width=WIDTH, height=HEIGHT)