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Update app.py
Browse files
app.py
CHANGED
@@ -9,7 +9,7 @@ import cv2
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import numpy as np
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from PIL import Image
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import torch
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sys.path.append('Utils')
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sys.path.append('model')
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@@ -18,17 +18,7 @@ from model.transform import transforms
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from model.unet import UNET
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from Utils.area import pixel_to_sqft, process_and_overlay_image
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from Utils.convert import read_pansharpened_rgb
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from huggingface_hub import HfApi, login
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HF_TOKEN = os.environ.get("HF_TOKEN")
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if not HF_TOKEN:
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raise ValueError("HF_TOKEN environment variable is not set")
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login(token=HF_TOKEN)
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hf_api = HfApi()
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REPO_ID = "Pavan2k4/Building_area"
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REPO_TYPE = "space"
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@st.cache_resource
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def load_model():
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@@ -71,24 +61,7 @@ def refine_mask(mask, blur_kernel=5, threshold_value=127, morph_kernel_size=3, m
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# save to dir func
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if not os.path.isfile(local_path):
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st.error(f"File not found at {local_path}")
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return
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try:
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with open(local_path, 'rb') as f:
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hf_api.upload_file(
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path_or_fileobj=f,
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path_in_repo=repo_path,
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repo_id=REPO_ID,
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repo_type=REPO_TYPE,
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token=HF_TOKEN
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)
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st.success(f"File uploaded successfully to {repo_path}")
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except Exception as e:
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st.error(f"Error during upload: {str(e)}")
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st.exception(e)
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@@ -202,13 +175,12 @@ def log_image_details(image_id, image_filename, mask_filename):
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writer.writerow([sno, date, time, image_id, image_filename, mask_filename])
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def upload_page():
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if 'file_uploaded' not in st.session_state:
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st.session_state.file_uploaded = False
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if 'filename' not in st.session_state:
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st.session_state.filename = None
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if 'mask_filename' not in st.session_state:
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st.session_state.mask_filename = None
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@@ -217,7 +189,6 @@ def upload_page():
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if image is not None and not st.session_state.file_uploaded:
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try:
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bytes_data = image.getvalue()
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timestamp = int(time.time())
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original_filename = image.name
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file_extension = os.path.splitext(original_filename)[1].lower()
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@@ -235,7 +206,6 @@ def upload_page():
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with open(filepath, "wb") as f:
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f.write(bytes_data)
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# Check if the uploaded file is a GeoTIFF
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if file_extension in ['.tiff', '.tif']:
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st.info('Processing GeoTIFF image...')
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rgb_image = read_pansharpened_rgb(filepath)
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@@ -256,13 +226,10 @@ def upload_page():
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st.image(img, caption='Uploaded Image', use_column_width=True)
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st.success(f'Image processed and saved as {converted_filename}')
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# Store the full path of the converted image
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st.session_state.filename = converted_filename
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# Convert image to numpy array
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img_array = np.array(img)
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# Check if image shape is more than 650x650
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if img_array.shape[0] > 650 or img_array.shape[1] > 650:
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st.info('Large image detected. Using patch-based processing.')
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with st.spinner('Analyzing large image...'):
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@@ -274,45 +241,12 @@ def upload_page():
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prediction = predict(img_transformed)
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full_mask = (prediction > 0.5).astype(np.uint8) * 255
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# Save the full mask
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full_mask = refine_mask(full_mask)
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mask_filename = f"mask_{timestamp}.png"
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mask_filepath = os.path.join(MASK_DIR, mask_filename)
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cv2.imwrite(mask_filepath, full_mask)
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st.session_state.mask_filename = mask_filename
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# Upload to Hugging Face repo
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try:
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with open(converted_filepath, 'rb') as f:
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image_repo_path = f"images/{converted_filename}"
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hf_api.upload_file(
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path_or_fileobj=f,
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path_in_repo=image_repo_path,
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repo_id=REPO_ID,
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repo_type=REPO_TYPE,
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token=HF_TOKEN
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)
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st.success(f"Image uploaded successfully to {image_repo_path}")
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except Exception as e:
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st.error(f"Error saving image to Hugging Face repo: {str(e)}")
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st.exception(e)
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try:
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with open(mask_filepath, 'rb') as f:
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mask_repo_path = f"masks/{mask_filename}"
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hf_api.upload_file(
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path_or_fileobj=f,
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path_in_repo=mask_repo_path,
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repo_id=REPO_ID,
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repo_type=REPO_TYPE,
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token=HF_TOKEN
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)
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st.success(f"Mask uploaded successfully to {mask_repo_path}")
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except Exception as e:
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st.error(f"Error saving mask to Hugging Face repo: {str(e)}")
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st.exception(e)
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# Log image details
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log_image_details(timestamp, converted_filename, mask_filename)
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st.session_state.file_uploaded = True
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@@ -321,7 +255,7 @@ def upload_page():
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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st.error("Please check the logs for more details.")
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print(f"Error in upload_page: {str(e)}")
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if st.session_state.file_uploaded and st.button('View result'):
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if st.session_state.filename is None:
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import numpy as np
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from PIL import Image
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import torch
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sys.path.append('Utils')
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sys.path.append('model')
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from model.unet import UNET
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from Utils.area import pixel_to_sqft, process_and_overlay_image
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from Utils.convert import read_pansharpened_rgb
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@st.cache_resource
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def load_model():
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# save to dir func
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writer.writerow([sno, date, time, image_id, image_filename, mask_filename])
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def upload_page():
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if 'file_uploaded' not in st.session_state:
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st.session_state.file_uploaded = False
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if 'filename' not in st.session_state:
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st.session_state.filename = None
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if 'mask_filename' not in st.session_state:
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st.session_state.mask_filename = None
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if image is not None and not st.session_state.file_uploaded:
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try:
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bytes_data = image.getvalue()
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timestamp = int(time.time())
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original_filename = image.name
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file_extension = os.path.splitext(original_filename)[1].lower()
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with open(filepath, "wb") as f:
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f.write(bytes_data)
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if file_extension in ['.tiff', '.tif']:
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st.info('Processing GeoTIFF image...')
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rgb_image = read_pansharpened_rgb(filepath)
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st.image(img, caption='Uploaded Image', use_column_width=True)
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st.success(f'Image processed and saved as {converted_filename}')
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st.session_state.filename = converted_filename
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img_array = np.array(img)
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if img_array.shape[0] > 650 or img_array.shape[1] > 650:
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st.info('Large image detected. Using patch-based processing.')
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with st.spinner('Analyzing large image...'):
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prediction = predict(img_transformed)
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full_mask = (prediction > 0.5).astype(np.uint8) * 255
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full_mask = refine_mask(full_mask)
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mask_filename = f"mask_{timestamp}.png"
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mask_filepath = os.path.join(MASK_DIR, mask_filename)
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cv2.imwrite(mask_filepath, full_mask)
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st.session_state.mask_filename = mask_filename
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log_image_details(timestamp, converted_filename, mask_filename)
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st.session_state.file_uploaded = True
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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st.error("Please check the logs for more details.")
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print(f"Error in upload_page: {str(e)}")
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if st.session_state.file_uploaded and st.button('View result'):
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if st.session_state.filename is None:
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