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Remove get_python_interprator() usage and hard-coded interpreter paths for portable environment compatibility #99

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Remove get_python_interprator() usage and hard-coded interpreter paths for portable environment compatibility #99

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33 changes: 13 additions & 20 deletions AddaxAI_GUI.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2168,7 +2168,7 @@ def open_annotation_windows(recognition_file, class_list_txt, file_list_txt, lab
# init paths
labelImg_dir = os.path.join(AddaxAI_files, "Human-in-the-loop")
labelImg_script = os.path.join(labelImg_dir, "labelImg.py")
python_executable = get_python_interprator("base")
python_executable = sys.executable

# create command
command_args = []
Expand Down Expand Up @@ -2357,13 +2357,6 @@ def open_annotation_windows(recognition_file, class_list_txt, file_list_txt, lab
update_frame_states()])
btn_hitl_final_export_n.grid(row=0, column=1, rowspan=1, sticky='nesw', padx=5)

# os dependent python executables
def get_python_interprator(env_name):
if platform.system() == 'Windows':
return os.path.join(AddaxAI_files, "envs", f"env-{env_name}", "python.exe")
else:
return os.path.join(AddaxAI_files, "envs", f"env-{env_name}", "bin", "python")

# get the images and xmls from annotation session and store them with unique filename
def uniquify_and_move_img_and_xml_from_filelist(file_list_txt, recognition_file, hitl_final_window):
# log
Expand Down Expand Up @@ -2750,7 +2743,7 @@ def classify_detections(json_fpath, data_type, simple_mode = False):
cls_tax_levels_idx = model_vars["var_tax_levels_idx"] # take idx from model vars

# init paths
python_executable = get_python_interprator(cls_model_env)
python_executable = sys.executable
inference_script = os.path.join(AddaxAI_files, "AddaxAI", "classification_utils", "model_types", cls_model_type, "classify_detections.py")

# create command
Expand Down Expand Up @@ -2954,7 +2947,7 @@ def deploy_model(path_to_image_folder, selected_options, data_type, simple_mode
process_video_py = os.path.join(AddaxAI_files, "cameratraps", "megadetector", "detection", "process_video.py")
video_recognition_file = "--output_json_file=" + os.path.join(chosen_folder, "video_recognition_file.json")
GPU_param = "Unknown"
python_executable = get_python_interprator("base")
python_executable = sys.executable

# select model based on user input via dropdown menu, or take MDv5a for simple mode
custom_model_bool = False
Expand Down Expand Up @@ -5033,7 +5026,7 @@ def deploy_speciesnet(chosen_folder, sppnet_output_window, simple_mode = False):

# prepare variables
chosen_folder = str(Path(chosen_folder))
python_executable = get_python_interprator("speciesnet")
python_executable = sys.executable
sppnet_output_file = os.path.join(chosen_folder, "sppnet_output_file.json")

# save settings for next time
Expand Down Expand Up @@ -6305,15 +6298,15 @@ def download_model(model_dir, skip_ask=False):
progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
download_popup = ModelDownloadProgressWindow(model_title = model_title, total_size_str = format_size(total_size))
download_popup.open()
for download_url, fname in download_info:
file_path = os.path.normpath(os.path.join(model_dir, fname))
dir_name = os.path.dirname(file_path)
if dir_name:
os.makedirs(dir_name, exist_ok=True)
response = requests.get(download_url, stream=True, timeout=30, headers=headers)
response.raise_for_status()

with open(file_path, 'wb') as file:
for download_url, fname in download_info:
file_path = os.path.normpath(os.path.join(model_dir, fname))
dir_name = os.path.dirname(file_path)
if dir_name:
os.makedirs(dir_name, exist_ok=True)
response = requests.get(download_url, stream=True, timeout=30, headers=headers)
response.raise_for_status()
with open(file_path, 'wb') as file:
for chunk in response.iter_content(chunk_size=65536):
if chunk:
file.write(chunk)
Expand Down
37 changes: 14 additions & 23 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,53 +10,44 @@

print("\n")

# check os
system = platform.system()

# os dependent python executables
def get_python_interprator(env_name):
if system == 'Windows':
return os.path.join(AddaxAI_files, "envs", f"env-{env_name}", "python.exe")
else:
return os.path.join(AddaxAI_files, "envs", f"env-{env_name}", "bin", "python")

# clean path
if getattr(sys, 'frozen', False):
AddaxAI_files = os.path.dirname(sys.executable)
else:
AddaxAI_files = os.path.dirname(os.path.abspath(__file__))

if AddaxAI_files.endswith("main.app/Contents/MacOS"):
AddaxAI_files = AddaxAI_files.replace("main.app/Contents/MacOS", "")

if AddaxAI_files.endswith(".app/Contents/MacOS"):
AddaxAI_files = os.path.dirname(os.path.dirname(os.path.dirname(AddaxAI_files)))

# init paths
# init paths
GUI_script = os.path.join(AddaxAI_files, "AddaxAI", "AddaxAI_GUI.py")
first_startup_file = os.path.join(AddaxAI_files, "first-startup.txt")

# log
print(f" AddaxAI_files: {AddaxAI_files}")
print(f" sys.executable: {sys.executable.replace(AddaxAI_files, '.')}")
print(f" GUI_script: {GUI_script.replace(AddaxAI_files, '.')}")
print(f" python_executable: using current interpreter")

# python executable
python_executable = get_python_interprator("base")
print(f" python_executable: {python_executable.replace(AddaxAI_files, '.')}")

# cuda toolkit
#cuda toolkit
cuda_toolkit_path = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH")
print(f" cuda_toolkit_path: {cuda_toolkit_path}")

# run the GUI script
print("\nOpening application...")
# run the GUI script using CURRENT Python environment
print("\nOpening application...")

run_cmd = [sys.executable, GUI_script]

if system == 'Windows':
if sys.executable.endswith("debug.exe"):
subprocess.run([get_python_interprator("base"), GUI_script])
input("Press [Enter] to close console window...") # keep console open after closing app
subprocess.run(run_cmd)
input("Press [Enter] to close console window...")
else:
subprocess.run([get_python_interprator("base"), GUI_script],
creationflags=subprocess.CREATE_NO_WINDOW)
subprocess.run(run_cmd, creationflags=subprocess.CREATE_NO_WINDOW)
else:
subprocess.run([get_python_interprator("base"), GUI_script])
subprocess.run(run_cmd)