[refactor] start reverting to original app.py content/1

app.py

@@ -1,10 +1,29 @@
+import argparse
+import cv2
 import gradio as gr
 import json
 import logging
-from fastapi import FastAPI
+import nh3
+import numpy as np
+import os
+import re
+import sys
+import torch
+import torch.nn.functional as F
+from fastapi import FastAPI, File, UploadFile, Request
+from fastapi.responses import HTMLResponse, RedirectResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor
+from typing import Callable
 
+from model.LISA import LISAForCausalLM
+from model.llava import conversation as conversation_lib
+from model.llava.mm_utils import tokenizer_image_token
+from model.segment_anything.utils.transforms import ResizeLongestSide
 from utils import session_logger
-
+from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                         DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX)
 
 session_logger.change_logging(logging.DEBUG)
 
@@ -12,6 +31,11 @@ session_logger.change_logging(logging.DEBUG)
 CUSTOM_GRADIO_PATH = "/"
 app = FastAPI(title="lisa_app", version="1.0")
 
+FASTAPI_STATIC = os.getenv("FASTAPI_STATIC")
+os.makedirs(FASTAPI_STATIC, exist_ok=True)
+app.mount("/static", StaticFiles(directory=FASTAPI_STATIC), name="static")
+templates = Jinja2Templates(directory="templates")
+
 
 @app.get("/health")
 @session_logger.set_uuid_logging
@@ -25,20 +49,74 @@ def health() -> str:
 
 
 @session_logger.set_uuid_logging
-def request_formatter(text: str) -> str:
-    logging.info("start request formatting...")
-    formatted_text = f"transformed {text}."
-    logging.info(f"formatted request as {formatted_text}.")
-    return formatted_text
+def get_cleaned_input(input_str):
+    logging.info(f"start cleaning of input_str: {input_str}.")
+    input_str = nh3.clean(
+        input_str,
+        tags={
+            "a",
+            "abbr",
+            "acronym",
+            "b",
+            "blockquote",
+            "code",
+            "em",
+            "i",
+            "li",
+            "ol",
+            "strong",
+            "ul",
+        },
+        attributes={
+            "a": {"href", "title"},
+            "abbr": {"title"},
+            "acronym": {"title"},
+        },
+        url_schemes={"http", "https", "mailto"},
+        link_rel=None,
+    )
+    logging.info(f"cleaned input_str: {input_str}.")
+    return input_str
+
+
+@session_logger.set_uuid_logging
+def get_inference_model_by_args(args_to_parse):
+    logging.info(f"args_to_parse:{args_to_parse}.")
+
+    @session_logger.set_uuid_logging
+    def inference(input_str, input_image):
+        ## filter out special chars
 
+        input_str = get_cleaned_input(input_str)
+        logging.info(f"input_str type: {type(input_str)}, input_image type: {type(input_image)}.")
+        logging.info(f"input_str: {input_str}.")
 
-io = gr.Interface(
-    request_formatter,
+        return output_image, output_str
+
+    return inference
+
+
+@session_logger.set_uuid_logging
+def get_gradio_interface(fn_inference: Callable):
+    return gr.Interface(
+        fn_inference,
     inputs=[
-        gr.Textbox(lines=1, placeholder=None, label="Text input"),
+            gr.Textbox(lines=1, placeholder=None, label="Text Instruction"),
+            gr.Image(type="filepath", label="Input Image")
     ],
     outputs=[
+            gr.Image(type="pil", label="Segmentation Output"),
         gr.Textbox(lines=1, placeholder=None, label="Text Output"),
     ],
+        title=title,
+        description=description,
+        article=article,
+        examples=examples,
+        allow_flagging="auto",
 )
+
+
+args = parse_args(sys.argv[1:])
+inference_fn = get_inference_model_by_args(args)
+io = get_gradio_interface(inference_fn)
 app = gr.mount_gradio_app(app, io, path=CUSTOM_GRADIO_PATH)