Delete app.py

app.py

@@ -1,136 +0,0 @@
-import os
-# os.system("sudo apt-get update && sudo apt-get install -y git")
-# os.system("sudo apt-get -y install pybind11-dev")
-# os.system("git clone https://github.com/facebookresearch/detectron2.git")
-# os.system("pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html")
-os.system("cd detectron2 && pip install detectron2-0.6-cp310-cp310-linux_x86_64.whl")
-# os.system("pip3 install torch torchvision torchaudio")
-os.system("pip install deepspeed==0.7.0")
-
-import site
-from importlib import reload
-reload(site)
-
-from PIL import Image
-from io import BytesIO
-import argparse
-import sys
-import numpy as np
-import torch
-import gradio as gr
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.utils.logger import setup_logger
-
-sys.path.insert(0, "third_party/CenterNet2/projects/CenterNet2/")
-from centernet.config import add_centernet_config
-from grit.config import add_grit_config
-
-from grit.predictor import VisualizationDemo
-
-def get_parser():
-    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
-    parser.add_argument(
-        "--config-file",
-        default="configs/GRiT_B_DenseCap_ObjectDet.yaml",
-        metavar="FILE",
-        help="path to config file",
-    )
-    parser.add_argument("--cpu", action="store_true", help="Use CPU only.")
-    parser.add_argument(
-        "--confidence-threshold",
-        type=float,
-        default=0.5,
-        help="Minimum score for instance predictions to be shown",
-    )
-    parser.add_argument(
-        "--test-task",
-        type=str,
-        default="",
-        help="Choose a task to have GRiT perform",
-    )
-    parser.add_argument(
-        "--opts",
-        help="Modify config options using the command-line 'KEY VALUE' pairs",
-        default=["MODEL.WEIGHTS", "./models/grit_b_densecap_objectdet.pth"],
-        nargs=argparse.REMAINDER,
-    )
-    return parser
-
-def setup_cfg(args):
-    cfg = get_cfg()
-    if args.cpu:
-        cfg.MODEL.DEVICE = "cpu"
-    add_centernet_config(cfg)
-    add_grit_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    # Set score_threshold for builtin models
-    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
-    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = (
-        args.confidence_threshold
-    )
-    if args.test_task:
-        cfg.MODEL.TEST_TASK = args.test_task
-    cfg.MODEL.BEAM_SIZE = 1
-    cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
-    cfg.USE_ACT_CHECKPOINT = False
-    cfg.freeze()
-    return cfg
-
-def predict(image_file):
-    image_array = np.array(image_file)[:, :, ::-1]  # BGR
-    predictions, visualized_output = dense_captioning_demo.run_on_image(image_array)
-    buffer = BytesIO()
-    visualized_output.fig.savefig(buffer, format='png')
-    buffer.seek(0)
-    detections = {}
-    predictions = predictions["instances"].to(torch.device("cpu"))
-
-    for box, description, score in zip(
-        predictions.pred_boxes,
-        predictions.pred_object_descriptions.data,
-        predictions.scores,
-    ):
-        if description not in detections:
-            detections[description] = []
-        detections[description].append(
-            {
-                "xmin": float(box[0]),
-                "ymin": float(box[1]),
-                "xmax": float(box[2]),
-                "ymax": float(box[3]),
-                "score": float(score),
-            }
-        )
-
-    output = {
-        "dense_captioning_results": {
-            "detections": detections,
-        }
-    }
-
-    return Image.open(buffer), output
-
-
-
-args = get_parser().parse_args()
-args.test_task = "DenseCap"
-setup_logger(name="fvcore")
-logger = setup_logger()
-logger.info("Arguments: " + str(args))
-
-cfg = setup_cfg(args)
-
-dense_captioning_demo = VisualizationDemo(cfg)
-
-demo = gr.Interface(
-    title="Dense Captioning - GRiT",
-    fn=predict,
-    inputs=gr.Image(type='pil', label="Original Image"),
-    outputs=[gr.Image(type="pil",label="Output Image"), "json"],
-    examples=["example_1.jpg", "example_2.jpg"],
-)
-
-demo.launch()