add files

app.py

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+import os
+os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
+credentials_kwargs={"aws_access_key_id": os.environ["ACCESS_KEY"],"aws_secret_access_key": os.environ["SECRET_KEY"]}
+
+# work around: https://discuss.huggingface.co/t/how-to-install-a-specific-version-of-gradio-in-spaces/13552
+os.system("pip uninstall -y gradio")
+os.system("pip install gradio==3.4.1")
+os.system(os.environ["DD_ADDONS"])
+
+import time
+from os import getcwd, path
+
+import deepdoctection as dd
+from deepdoctection.dataflow.serialize import DataFromList
+from deepdoctection.utils.settings import get_type
+
+from dd_addons.analyzer.loader import get_loader
+from dd_addons.extern.guidance import TOKEN_DEFAULT_INSTRUCTION
+from dd_addons.utils.settings import register_llm_token_tag, register_string_categories_from_list
+from dd_addons.extern.openai import OpenAiLmmTokenClassifier
+
+import gradio as gr
+
+analyzer = get_loader(reset_config_file=True)
+
+demo = gr.Blocks(css="scrollbar.css")
+
+
+def process_analyzer(openai_api_key, categories_str, instruction_str, img, pdf, max_datapoints):
+    categories_list = categories_str.split(",")
+    register_string_categories_from_list(categories_list, "custom_token_classes")
+    custom_token_class = dd.object_types_registry.get("custom_token_classes")
+    print([token_class for token_class in custom_token_class])
+    register_llm_token_tag([token_class for token_class in custom_token_class])
+    categories = {
+        str(idx + 1): get_type(val) for idx, val in enumerate(categories_list)
+    }
+
+    gpt_token_classifier = OpenAiLmmTokenClassifier(
+        model_name="gpt-3.5-turbo",
+        categories=categories,
+        api_key=openai_api_key,
+        instruction= instruction_str if instruction_str else None,
+    )
+    analyzer.pipe_component_list[8].language_model = gpt_token_classifier
+
+    if img is not None:
+        image = dd.Image(file_name=str(time.time()).replace(".","") + ".png", location="")
+        image.image = img[:, :, ::-1]
+
+        df = DataFromList(lst=[image])
+        df = analyzer.analyze(dataset_dataflow=df)
+    elif pdf:
+        df = analyzer.analyze(path=pdf.name, max_datapoints=max_datapoints)
+    else:
+        raise ValueError
+
+    df.reset_state()
+
+    json_out = {}
+    dpts = []
+
+    for idx, dp in enumerate(df):
+        dpts.append(dp)
+        json_out[f"page_{idx}"] = dp.get_token()
+
+    return [dp.viz(show_cells=False, show_layouts=False, show_tables=False, show_words=True, show_token_class=True, ignore_default_token_class=True)
+            for dp in dpts], json_out
+
+
+with demo:
+    with gr.Box():
+        gr.Markdown("<h1><center>Document AI GPT</center></h1>")
+        gr.Markdown("<h2 ><center>Zero or few-shot Entity Extraction powered by ChatGPT and <strong>deep</strong>doctection </center></h2>"
+                    "<center>This pipeline consists of a stack of models powered for layout analysis and table recognition "
+                    "to prepare a prompt for ChatGPT. </center>"
+                    "<center>Be aware! The Space is still very fragile.</center><br />")
+    with gr.Box():
+        gr.Markdown("<h2><center>Upload a document and choose setting</center></h2>")
+        with gr.Row():
+            with gr.Column():
+                with gr.Tab("Image upload"):
+                    with gr.Column():
+                        inputs = gr.Image(type='numpy', label="Original Image")
+                with gr.Tab("PDF upload *"):
+                    with gr.Column():
+                        inputs_pdf = gr.File(label="PDF")
+                    gr.Markdown("<sup>* If an image is cached in tab, remove it first</sup>")
+                with gr.Box():
+                    gr.Examples(
+                        examples=[path.join(getcwd(), "sample_2.png")],
+                        inputs = inputs)
+                with gr.Box():
+                    gr.Markdown("Enter your OpenAI API Key* ")
+                    user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", show_label=False)
+                    gr.Markdown("<sup>* Your API key will not be saved. However, it is always recommended to deactivate the"
+                                "API key once it is entered into an unknown source</sup>")
+            with gr.Column():
+                with gr.Box():
+                    gr.Markdown(
+                        "Enter a list of comma seperated entities. Use a snake case style. Avoid special characters. "
+                        "Best way is to only use `a-z` and `_`")
+                    categories = gr.Textbox(value='', placeholder="mitarbeiter_anzahl", show_label=False)
+                with gr.Box():
+                    gr.Markdown("Optional: Enter a prompt for additional guidance. Will use the placeholder as fallback")
+                    instruction = gr.Textbox(value='', placeholder=TOKEN_DEFAULT_INSTRUCTION, show_label=False)
+        with gr.Row():
+            max_imgs = gr.Slider(1, 3, value=1, step=1, label="Number of pages in multi page PDF",
+                                 info="Will stop after 3 pages")
+
+        with gr.Row():
+            btn = gr.Button("Run model", variant="primary")
+
+    with gr.Box():
+        gr.Markdown("<h2><center>Outputs</center></h2>")
+        with gr.Row():
+            with gr.Column():
+                with gr.Box():
+                    gr.Markdown("<center><strong>JSON</strong></center>")
+                    json = gr.JSON()
+            with gr.Column():
+                with gr.Box():
+                    gr.Markdown("<center><strong>Layout detection</strong></center>")
+                    gallery = gr.Gallery(
+                        label="Output images", show_label=False, elem_id="gallery"
+                    ).style(grid=2)
+        with gr.Row():
+            with gr.Box():
+                gr.Markdown("<center><strong>Table</strong></center>")
+                html = gr.HTML()
+
+    btn.click(fn=process_analyzer, inputs=[user_token, categories,  instruction, inputs, inputs_pdf, max_imgs],
+              outputs=[gallery, json])
+
+demo.launch()

packages.txt

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+poppler-utils

requirements.txt

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+Pillow==9.5.0
+torch==1.12.0
+torchvision==0.13.0
+git+https://github.com/deepdoctection/deepdoctection#egg=deepdoctection[hf]