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README.md
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---
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title: Detic
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emoji: 👀
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colorFrom:
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colorTo: red
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sdk: gradio
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app_file: app.py
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---
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title: Detic+ChatGPT
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emoji: 👀
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colorFrom: blue
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colorTo: red
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sdk: gradio
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app_file: app.py
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app.py
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@@ -77,12 +77,6 @@ BUILDIN_METADATA_PATH = {
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"coco": "coco_2017_val",
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}
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vocabulary = "lvis" # change to 'lvis', 'objects365', 'openimages', or 'coco'
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metadata = MetadataCatalog.get(BUILDIN_METADATA_PATH[vocabulary])
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classifier = BUILDIN_CLASSIFIER[vocabulary]
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num_classes = len(metadata.thing_classes)
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reset_cls_test(predictor.model, classifier, num_classes)
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session_token = os.environ.get("SessionToken")
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return response
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def inference(img):
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im = cv2.imread(img)
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)
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with gr.Blocks() as demo:
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gr.Markdown("# Detic+ChatGPT")
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gr.Markdown(
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with gr.Column():
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btn_detic = gr.Button("Run Detic+ChatGPT")
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with gr.
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outviz = gr.Image(label="Visualization", type="pil")
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output_desc = gr.Textbox(label="
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# outputjson = gr.JSON(label="Detected Objects")
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btn_detic.click(fn=inference, inputs=inp, outputs=[outviz, output_desc])
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demo.launch()
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"coco": "coco_2017_val",
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}
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session_token = os.environ.get("SessionToken")
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return response
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def inference(img, vocabulary):
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metadata = MetadataCatalog.get(BUILDIN_METADATA_PATH[vocabulary])
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classifier = BUILDIN_CLASSIFIER[vocabulary]
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num_classes = len(metadata.thing_classes)
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reset_cls_test(predictor.model, classifier, num_classes)
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im = cv2.imread(img)
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)
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# create a gradio block for image classification
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with gr.Blocks() as demo:
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gr.Markdown("# Detic+ChatGPT")
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gr.Markdown(
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"Use Detic to detect objects in an image and then use ChatGPT to describe the image."
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)
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gr.HTML(
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"<p>You can duplicating this space and use your own session token: <a style='display:inline-block' href='https://huggingface.co/spaces/yizhangliu/chatGPT?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>"
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)
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gr.HTML(
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"<p> Instruction on how to get session token can be seen in video <a style='display:inline-block' href='https://www.youtube.com/watch?v=TdNSj_qgdFk'><font style='color:blue;weight:bold;'>here</font></a>. Add your session token by going to settings and add under secrets. </p>"
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)
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with gr.Column():
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with gr.Row():
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inp = gr.Image(label="Input Image", type="filepath")
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vocab = gr.Dropdown(
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["lvis", "objects365", "openimages", "coco"],
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label="Vocabulary",
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value="lvis",
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)
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btn_detic = gr.Button("Run Detic+ChatGPT")
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with gr.Row():
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outviz = gr.Image(label="Visualization", type="pil")
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output_desc = gr.Textbox(label="ChatGPT Description", lines=5)
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# outputjson = gr.JSON(label="Detected Objects")
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btn_detic.click(fn=inference, inputs=[inp, vocab], outputs=[outviz, output_desc])
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demo.launch()
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