import gradio as gr def update(name): return f"Welcome to Gradio, {name}!" with gr.Blocks() as demo: gr.Markdown("Start typing below and then click **Run** to see the output.") with gr.Row(): inp = gr.Textbox(placeholder="What is your name?") out = gr.Textbox() btn = gr.Button("Run") btn.click(fn=update, inputs=inp, outputs=out) demo.launch() exit() import requests import os from io import BytesIO from PIL import Image import numpy as np from pathlib import Path import gradio as gr import warnings warnings.filterwarnings("ignore") # os.system( # "pip install einops shapely timm yacs tensorboardX ftfy prettytable pymongo click opencv-python inflect nltk scipy scikit-learn pycocotools") # os.system("pip install transformers") os.system("python setup.py build develop --user") from maskrcnn_benchmark.config import cfg from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo # Use this command for evaluate the GLIP-T model #config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml" #weight_file = "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth" config_file = "configs/pretrain_new/desco_glip.yaml" weight_file = "MODEL/desco_glip_tiny.pth" # Use this command if you want to try the GLIP-L model # ! wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth -O MODEL/glip_large_model.pth # config_file = "configs/pretrain/glip_Swin_L.yaml" # weight_file = "MODEL/glip_large_model.pth" # update the config options with the config file # manual override some options #cfg.local_rank = 0 #cfg.num_gpus = 1 cfg.merge_from_file(config_file) #cfg.merge_from_list(["MODEL.WEIGHT", weight_file]) #cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) glip_demo = GLIPDemo( cfg, min_image_size=800, confidence_threshold=0.7, show_mask_heatmaps=False ) def predict(image, text): result, _ = glip_demo.run_on_web_image(image[:, :, [2, 1, 0]], text, 0.5) return result[:, :, [2, 1, 0]] image = gr.inputs.Image() gr.Interface( description="Object Detection in the Wild through GLIP (https://github.com/microsoft/GLIP).", fn=predict, inputs=["image", "text"], outputs=[ gr.outputs.Image( type="pil", # label="grounding results" ), ], examples=[ #["./flickr_9472793441.jpg", "bobble heads on top of the shelf ."], #["./flickr_9472793441.jpg", "sofa . remote . dog . person . car . sky . plane ."], ["./coco_000000281759.jpg", "A green umbrella. A pink striped umbrella. A plain white umbrella."], ["./coco_000000281759.jpg", "a flowery top. A blue dress. An orange shirt ."], ["./coco_000000281759.jpg", "a car . An electricity box ."], #["./flickr_7520721.jpg", "A woman figure skater in a blue costume holds her leg by the blade of her skate ."] ], article=Path("docs/intro.md").read_text() ).launch() # ).launch(server_name="0.0.0.0", server_port=7000, share=True)