Update app.py

app.py

@@ -1,93 +1,39 @@
 import os
-from langchain.llms import OpenAI, OpenAIChat
-os.system("pip install -U gradio")
-import sys 
-import radio as gr
-cmd22 = "pip install pydantic==1.*" 
-
-cmd0 = "pip -m pip install 'https://github.com/facebookresearch/detectron2.git@5aeb252b194b93dc2879b4ac34bc51a31b5aee13'"
-# cmd0 = "python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'"
-# cmd0 = "python -m pip install 'https://github.com/facebookresearch/detectron2.git'"
-
-os.system(cmd0)
-os.system(cmd22)
-
-# clone and install Detic
-os.system(
-    "git clone https://github.com/facebookresearch/Detic.git --recurse-submodules"
-)
-os.chdir("Detic")
-
-# Install detectron2
+import gradio as gr
 import torch
-
-# Some basic setup:
-# Setup detectron2 logger
-import detectron2
-from detectron2.utils.logger import setup_logger
-
-setup_logger()
-
-# import some common libraries
-import sys
+import cv2
 import numpy as np
-import os, json, cv2, random
-
-# import some common detectron2 utilities
-from detectron2 import model_zoo
+from PIL import Image
+from detectron2.utils.logger import setup_logger
 from detectron2.engine import DefaultPredictor
 from detectron2.config import get_cfg
 from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog, DatasetCatalog
-
-# Detic libraries
-sys.path.insert(0, "third_party/CenterNet2/projects/CenterNet2/")
-sys.path.insert(0, "third_party/CenterNet2/")
+from detectron2.data import MetadataCatalog
+from langchain.llms import OpenAI, OpenAIChat
 from centernet.config import add_centernet_config
 from detic.config import add_detic_config
 from detic.modeling.utils import reset_cls_test
+from pydantic import BaseModel, Field, PydanticUserError
 
-from PIL import Image
+class BaseModelWithA(BaseModel):
+    a: float
 
-# Build the detector and download our pretrained weights
-cfg = get_cfg()
-add_centernet_config(cfg)
-add_detic_config(cfg)
-cfg.MODEL.DEVICE = "cpu"
-cfg.merge_from_file("configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml")
-cfg.MODEL.WEIGHTS = "https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth"
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
-cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = "rand"
-cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = (
-    True  # For better visualization purpose. Set to False for all classes.
-)
-predictor = DefaultPredictor(cfg)
-
-BUILDIN_CLASSIFIER = {
-    "lvis": "datasets/metadata/lvis_v1_clip_a+cname.npy",
-    "objects365": "datasets/metadata/o365_clip_a+cnamefix.npy",
-    "openimages": "datasets/metadata/oid_clip_a+cname.npy",
-    "coco": "datasets/metadata/coco_clip_a+cname.npy",
-}
-
-BUILDIN_METADATA_PATH = {
-    "lvis": "lvis_v1_val",
-    "objects365": "objects365_v2_val",
-    "openimages": "oid_val_expanded",
-    "coco": "coco_2017_val",
-}
-
-session_token = os.environ.get("SessionToken")
+class Foo(BaseModelWithA):
+    pass
 
+try:
+    class Bar(Foo):
+        x: float = 12.3
+        a: float = 123.0  # Add type annotation here
+except PydanticUserError as exc_info:
+    assert exc_info.code == 'model-field-overridden'
 
 def generate_caption(object_list_str, api_key, temperature):
     query = f"You are an intelligent image captioner. I will hand you the objects and their position, and you should give me a detailed description that IS BOTH SUPER CONCISE AND SHORT for the photo. In this photo we have the following objects\n{object_list_str}"
 
-    # query = f"You are an intelligent image captioner. I will hand you the objects and their position, and you should give me a detailed description for the photo. In this photo we have the following objects\n{object_list_str}"
     llm = OpenAIChat(
         model_name="gpt-3.5-turbo", openai_api_key=api_key, temperature=temperature
     )
-    # not gpt-4 yet!
 
     try:
         caption = llm(query)
@@ -97,7 +43,6 @@ def generate_caption(object_list_str, api_key, temperature):
 
     return caption
 
-
 def inference(img, vocabulary, api_key, temperature):
     metadata = MetadataCatalog.get(BUILDIN_METADATA_PATH[vocabulary])
     classifier = BUILDIN_CLASSIFIER[vocabulary]
@@ -144,7 +89,6 @@ def inference(img, vocabulary, api_key, temperature):
         gpt_response,
     )
 
-
 with gr.Blocks() as demo:
     with gr.Column():
         gr.Markdown("# Image Captioning using Detic and ChatGPT with LangChain 🦜️🔗")
@@ -180,5 +124,4 @@ with gr.Blocks() as demo:
         outputs=[outviz, output_desc],
     )
 
-
 demo.launch(debug=False)