feat: start refactor using samgis-core = 3.0.8

app.py

@@ -1,23 +1,47 @@
+import json
 import logging
 import os
 import sys
 from pathlib import Path
 
 import gradio as gr
+import structlog
 import uvicorn
+from dotenv import load_dotenv
 from fastapi import FastAPI
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
+from samgis_core.utilities import create_folders_if_not_exists
+from samgis_core.utilities import frontend_builder
+from samgis_core.utilities.session_logger import setup_logging
 from spaces import GPU as SPACES_GPU
 
+from lisa_on_cuda.utils import app_helpers
 from lisa_on_cuda import routes
-from lisa_on_cuda.utils import app_helpers, session_logger, utils, create_folders_and_variables_if_not_exists
-from lisa_on_cuda.utils import frontend_builder
 
 
-LOGLEVEL = os.getenv('LOGLEVEL', 'INFO').upper()
-session_logger.change_logging(LOGLEVEL)
+load_dotenv()
+project_root_folder = Path(globals().get("__file__", "./_")).absolute().parent
+workdir = os.getenv("WORKDIR", project_root_folder)
+model_folder = Path(project_root_folder / "machine_learning_models")
+
+log_level = os.getenv("LOG_LEVEL", "INFO")
+setup_logging(log_level=log_level)
+app_logger = structlog.stdlib.get_logger()
+app_logger.info(f"PROJECT_ROOT_FOLDER:{project_root_folder}, WORKDIR:{workdir}.")
+
+folders_map = os.getenv("FOLDERS_MAP", "{}")
+markdown_text = os.getenv("MARKDOWN_TEXT", "")
+examples_text_list = os.getenv("EXAMPLES_TEXT_LIST", "").split("\n")
+example_body = json.loads(os.getenv("EXAMPLE_BODY", "{}"))
+mount_gradio_app = bool(os.getenv("MOUNT_GRADIO_APP", ""))
+
+static_dist_folder = Path(project_root_folder) / "static" / "dist"
+input_css_path = os.getenv("INPUT_CSS_PATH", "src/input.css")
+
+
+create_folders_if_not_exists.folders_creation(folders_map)
 
 CUSTOM_GRADIO_PATH = "/"
 CUSTOM_STATIC_PATH = "/static"
@@ -25,37 +49,33 @@ FASTAPI_TITLE = "lisa_app"
 app = FastAPI(title=FASTAPI_TITLE, version="1.0")
 app.include_router(routes.router)
 
-fastapi_dict_folder = Path(utils.FASTAPI_STATIC) / "dist"
 frontend_builder.build_frontend(
-    project_root_folder=frontend_builder.env_project_root_folder,
-    input_css_path=frontend_builder.env_input_css_path,
-    output_dist_folder=fastapi_dict_folder
+    project_root_folder=workdir,
+    input_css_path=input_css_path,
+    output_dist_folder=static_dist_folder
 )
-create_folders_and_variables_if_not_exists.folders_creation()
-
-logging.info("build_frontend ok!")
+app_logger.info("build_frontend ok!")
 
-os.makedirs(utils.FASTAPI_STATIC, exist_ok=True)
-app.mount(CUSTOM_STATIC_PATH, StaticFiles(directory=fastapi_dict_folder), name="static")
+app.mount(CUSTOM_STATIC_PATH, StaticFiles(directory=static_dist_folder), name="static")
 
 
 @app.get(CUSTOM_STATIC_PATH)
 async def static() -> FileResponse:
-    return FileResponse(path=str(fastapi_dict_folder / "index.html"), media_type="text/html")
+    return FileResponse(path=str(static_dist_folder / "index.html"), media_type="text/html")
 
 
 templates = Jinja2Templates(directory="templates")
 
 
-app_helpers.app_logger.info(f"sys.argv:{sys.argv}.")
+app_logger.info(f"sys.argv:{sys.argv}.")
 args = app_helpers.parse_args([])
-app_helpers.app_logger.info(f"prepared default arguments:{args}.")
+app_logger.info(f"prepared default arguments:{args}.")
 inference_fn = app_helpers.get_inference_model_by_args(args, inference_decorator=SPACES_GPU)
-app_helpers.app_logger.info(f"prepared inference_fn function:{inference_fn.__name__}, creating gradio interface...")
-io = app_helpers.get_gradio_interface(inference_fn)
-app_helpers.app_logger.info("created gradio interface")
+app_logger.info(f"prepared inference_fn function:{inference_fn.__name__}, creating gradio interface...")
+io = app_helpers.get_gradio_interface(inference_fn, args=args)
+app_logger.info("created gradio interface")
 app = gr.mount_gradio_app(app, io, path=CUSTOM_GRADIO_PATH)
-app_helpers.app_logger.info("mounted gradio app within fastapi")
+app_logger.info("mounted gradio app within fastapi")
 
 
 if __name__ == '__main__':

lisa_on_cuda/utils/app_helpers.py

@@ -409,8 +409,15 @@ def get_inference_model_by_args(args_to_parse, internal_logger0: logging = None,
 
 @session_logger.set_uuid_logging
 def get_gradio_interface(
-        fn_inference: Callable
+        fn_inference: Callable,
+        args: str = None
 ):
+    description_and_demo_parameters = constants.description
+    if args is not None:
+        description_and_demo_parameters += "\n"
+        args_dict = {arg: getattr(args, arg) for arg in vars(args)}
+        for arg_k, arg_v in args_dict.items():
+            description_and_demo_parameters += "\n".join(f"{arg_k}: {arg_v};")
     return gr.Interface(
         fn_inference,
         inputs=[
@@ -423,7 +430,7 @@ def get_gradio_interface(
             gr.Textbox(lines=1, placeholder=None, label="Text Output")
         ],
         title=constants.title,
-        description=constants.description,
+        description=description_and_demo_parameters,
         article=constants.article,
         examples=constants.examples,
         allow_flagging="auto"

lisa_on_cuda/utils/constants.py

@@ -20,14 +20,12 @@ examples = [
 output_labels = ["Segmentation Output"]
 
 title = "LISA: Reasoning Segmentation via Large Language Model"
-
 description = """
 <font size=4>
-This is the online demo of LISA. \n
+This is the online demo of LISA... \n
 If multiple users are using it at the same time, they will enter a queue, which may delay some time. \n
 **Note**: **Different prompts can lead to significantly varied results**. \n
 **Note**: Please try to **standardize** your input text prompts to **avoid ambiguity**, and also pay attention to whether the **punctuations** of the input are correct. \n
-**Note**: Current model is **LISA-13B-llama2-v0-explanatory**, and 4-bit quantization may impair text-generation quality. \n
 **Usage**: <br>
 &ensp;(1) To let LISA **segment something**, input prompt like: "Can you segment xxx in this image?", "What is xxx in this image? Please output segmentation mask."; <br>
 &ensp;(2) To let LISA **output an explanation**, input prompt like: "What is xxx in this image? Please output segmentation mask and explain why."; <br>
@@ -36,6 +34,8 @@ Hope you can enjoy our work!
 </font>
 """
 
+demo_parameters = """This demo uses these parameters:"""
+
 article = """
 <p style='text-align: center'>
 <a href='https://arxiv.org/abs/2308.00692' target='_blank'>