Update app.py

app.py

@@ -1,184 +1,43 @@
-"""
-A main training script.
-"""
-
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-import warnings
-warnings.filterwarnings('ignore') # never print matching warnings
-import logging
-import os
-from collections import OrderedDict
-import torch
-import uniperceiver.utils.comm as comm
-from uniperceiver.config import get_cfg, CfgNode
-from uniperceiver.engine import DefaultTrainer, default_argument_parser, default_setup, launch, build_engine, add_moe_arguments
-
-#!TODO re-implement hooks
-from uniperceiver.engine import hooks
-from uniperceiver.modeling import add_config
-from uniperceiver.utils.env import init_distributed_mode, check_dist_portfile
-try:
-    import deepspeed
-    DEEPSPEED_INSTALLED = True
-except:
-    DEEPSPEED_INSTALLED = False
-
-import copy
-
-def add_data_prefix(cfg):
-    # TODO: more flexible method
-    data_dir = os.getenv("DATA_PATH", None)
-    mapping_list = [
-        [cfg.DATALOADER, 'FEATS_FOLDER', ['DATALOADER',]],
-        [cfg.DATALOADER, 'ANNO_FOLDER', ['DATALOADER', ]],
-        [cfg.DATALOADER, 'CLASS_NAME_FILE', ['DATALOADER', ]],
-        [cfg.INFERENCE, 'VOCAB', ['INFERENCE', ]],
-        [cfg.INFERENCE, 'VAL_ANNFILE', ['INFERENCE', ]],
-        [cfg.INFERENCE, 'TEST_ANNFILE', ['INFERENCE',]],
-        [cfg.MODEL, 'WEIGHTS', ['MODEL',]],
-    ]
-    whitelist = ["BERT", "CLIP", "CLIP_CAPTION"]
-    if data_dir:
-        for node, attr ,_ in mapping_list:
-            if node[attr] != '' and not node[attr].startswith('.') and not node[attr].startswith('/') and not node[attr].startswith('work_dirs') and not node[attr].startswith('cluster') and not node[attr].startswith('s3://') and node[attr] not in whitelist:
-                setattr(node, attr, os.path.join(data_dir, node[attr]))
-    for task in cfg.TASKS:
-        for _, item, key_list in mapping_list:
-            config_tmp = task
-            for key in key_list:
-                if key in config_tmp:
-                    config_tmp = config_tmp[key]
-            if item in config_tmp and  config_tmp[item] != '' and not config_tmp[item].startswith('.') and not config_tmp[item].startswith('/') and not config_tmp[item].startswith('work_dirs') and not config_tmp[item].startswith('cluster') and not config_tmp[item].startswith('s3://') and config_tmp[item] not in whitelist:
-                config_tmp[item] =  os.path.join(data_dir, config_tmp[item])
-
-    mapping_list = [
-        ['', 'FILE_PATH', ['SHARED_TARGETS_CFG',]],
-    ]
-    if cfg.SHARED_TARGETS is None:
-        cfg.SHARED_TARGETS = []
-    for share_targets in cfg.SHARED_TARGETS:
-        for _, item, key_list in mapping_list:
-            config_tmp = share_targets
-            for key in key_list:
-                config_tmp = config_tmp[key]
-            if item in config_tmp and config_tmp[item] != '' and not config_tmp[item].startswith('.') and not config_tmp[item].startswith(
-                    '/') and not config_tmp[item].startswith('work_dirs') and not config_tmp[item].startswith(
-                        'cluster') and not config_tmp[item].startswith('s3://') and config_tmp[item] not in whitelist:
-                config_tmp[item] = os.path.join(data_dir, config_tmp[item])
-
-
-
-def add_default_setting_for_multitask_config(cfg):
-    # merge some default config in (CfgNode) uniperceiver/config/defaults.py to each task config (dict)
-
-    tasks_config_temp = cfg.TASKS
-    num_tasks = len(tasks_config_temp)
-    cfg.pop('TASKS', None)
-
-    cfg.TASKS = [copy.deepcopy(cfg) for _ in range(num_tasks)]
-
-    for i, task_config in enumerate(tasks_config_temp):
-        cfg.TASKS[i].merge_from_other_cfg(CfgNode(task_config))
-        cfg.TASKS[i] = cfg.TASKS[i].to_dict_object()
-        pass
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    tmp_cfg = cfg.load_from_file_tmp(args.config_file)
-    add_config(cfg, tmp_cfg)
-
-    cfg.merge_from_file(args.config_file)
-    add_data_prefix(cfg)
-
-    cfg.merge_from_list(args.opts)
-    #
-    add_default_setting_for_multitask_config(cfg)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-def main(args):
-    cfg = setup(args)
-
-    """
-    If you'd like to do anything fancier than the standard training logic,
-    consider writing your own training loop (see plain_train_net.py) or
-    subclassing the trainer.
-    """
-    trainer = build_engine(cfg)
-    trainer.resume_or_load(resume=args.resume)
-    trainer.cast_layers()
-
-    if args.eval_only:
-        print('---------------------------')
-        print('eval model only')
-        print('---------------------------\n')
-        res = None
-        if trainer.val_data_loader is not None:
-
-            if trainer.model_ema is not None and args.eval_ema:
-                if comm.is_main_process():
-                    print('using ema model for evaluation')
-                res = trainer.test(trainer.cfg, trainer.model_ema.ema, trainer.val_data_loader, trainer.val_evaluator, epoch=-1)
-            else:
-                if args.eval_ema and comm.is_main_process():
-                    print('no ema model exists! using master model for evaluation')
-                res = trainer.test(trainer.cfg, trainer.model, trainer.val_data_loader, trainer.val_evaluator, epoch=-1)
-
-        if comm.is_main_process():
-            print(res)
-
-        if trainer.test_data_loader is not None:
-            if trainer.model_ema is not None and args.eval_ema:
-                if comm.is_main_process():
-                    print('using ema model for evaluation')
-                res = trainer.test(trainer.cfg, trainer.model_ema.ema, trainer.test_data_loader, trainer.test_evaluator, epoch=-1)
-            else:
-                if args.eval_ema and comm.is_main_process():
-                    print('no ema model exists! using master model for evaluation')
-                res = trainer.test(trainer.cfg, trainer.model, trainer.test_data_loader, trainer.test_evaluator, epoch=-1)
-        if comm.is_main_process():
-            print(res)
-        return res
-
-    return trainer.train()
-
-def get_args_parser():
-    parser = default_argument_parser()
-    if DEEPSPEED_INSTALLED:
-        parser = deepspeed.add_config_arguments(parser)
-    parser = add_moe_arguments(parser)
-
-    parser.add_argument('--init_method', default='slurm', type=str)
-    parser.add_argument('--local_rank', default=0, type=int)
-    parser.add_argument("--eval-ema", action="store_true", help="perform evaluation using ema")
-    args = parser.parse_args()
-
-    return args
-
-if __name__ == "__main__":
-    args = get_args_parser()
-    print("Command Line Args:", args)
-    if args.init_method == 'slurm':
-        # slurm init
-        check_dist_portfile()
-        init_distributed_mode(args)
-        main(args)
-    elif args.init_method == 'pytorch':
-        main(args)
-    else:
-        # follow 'd2' use default `mp.spawn` to init dist training
-        print('using \'mp.spawn\' for dist init! ')
-        launch(
-            main,
-            args.num_gpus,
-            num_machines=args.num_machines,
-            machine_rank=args.machine_rank,
-            dist_url=args.dist_url,
-            args=(args,),
-        )
+from codecs import encode, decode
+import requests
+import gradio as gr
+
+
+def infer(im):
+    im.save("converted.png")
+    url = "https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition"
+    files = {
+        "image": ("converted.png", open("converted.png", "rb"), "image/png"),
+        "model_type": (None, "detection"),
+        "media_type": (None, "photo"),
+    }
+    headers = {"referer": "https://thehive.ai/"}
+
+    res = requests.post(url, headers=headers, files=files)
+
+    text = ""
+    blocks = []
+    for output in res.json()["response"]["output"]:
+        text += output["block_text"]
+        for poly in output["bounding_poly"]:
+            blocks.append(
+                {
+                    "text": "".join([c["class"] for c in poly["classes"]]),
+                    "rect": poly["dimensions"],
+                }
+            )
+
+    text = decode(encode(text, "latin-1", "backslashreplace"), "unicode-escape")
+
+    return text, blocks
+
+
+iface = gr.Interface(
+    fn=infer,
+    title="Hive OCR simple",
+    description="Demo for Hive OCR. Transcribe and analyze media depicting typed, written, or graphic text",
+    inputs=[gr.Image(type="pil")],
+    outputs=["text", "json"],
+    examples=["20131216170659.jpg"],
+    article='<a href="https://thehive.ai/hive-ocr-solutions">Hive OCR</a>',
+).launch()