update app

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,13 +1 @@
----
-title: Redaction Detector
-emoji: 🔍
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# redaction-detector-streamlit

allsynthetic-imgsize768.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65362ca715c08973334b0488fb0cada234bff39a20cfa6a6d79eb111e744864e
+size 131183383

app.py

@@ -1,4 +0,0 @@
-import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)

article.md

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+I've been working through the first two lessons of
+[the fastai course](https://course.fast.ai/). For lesson one I trained a model
+to recognise my cat, Mr Blupus. For lesson two the emphasis is on getting those
+models out in the world as some kind of demo or application.
+[Gradio](https://gradio.app) and
+[Huggingface Spaces](https://huggingface.co/spaces) makes it super easy to get a
+prototype of your model on the internet.
+
+This MVP app runs two models to mimic the experience of what a final deployed
+version of the project might look like.
+
+- The first model (a classification model trained with fastai, available on the
+  Huggingface Hub
+  [here](https://huggingface.co/strickvl/redaction-classifier-fastai) and
+  testable as a standalone demo
+  [here](https://huggingface.co/spaces/strickvl/fastai_redaction_classifier)),
+  classifies and determines which pages of the PDF are redacted. I've written
+  about how I trained this model [here](https://mlops.systems/fastai/redactionmodel/computervision/datalabelling/2021/09/06/redaction-classification-chapter-2.html).
+- The second model (an object detection model trained using [IceVision](https://airctic.com/) (itself
+  built partly on top of fastai)) detects which parts of the image are redacted.
+  This is a model I've been working on for a while and I described my process in
+  a series of blog posts (see below).
+
+This MVP app does several things:
+
+- it extracts any pages it considers to contain redactions and displays that
+  subset as an [image carousel](https://gradio.app/docs/#o_carousel). It also
+  displays some text alerting you to which specific pages were redacted.
+- if you click the "Analyse and extract redacted images" checkbox, it will:
+  - pass the pages it considered redacted through the object detection model
+  - calculate what proportion of the total area of the image was redacted as
+    well as what proportion of the actual content (i.e. excluding margins etc
+    where there is no content)
+  - create a PDF that you can download that contains only the redacted images,
+    with an overlay of the redactions that it was able to identify along with
+    the confidence score for each item.
+
+## The Dataset
+
+I downloaded a few thousand publicly-available FOIA documents from a government
+website. I split the PDFs up into individual `.jpg` files and then used
+[Prodigy](https://prodi.gy/) to annotate the data. (This process was described
+in
+[a blogpost written last
+year](https://mlops.systems/fastai/redactionmodel/computervision/datalabelling/2021/09/06/redaction-classification-chapter-2.html).)
+For the object detection model, the process was quite a bit more involved and I
+direct you to the series of articles referenced below in the 'Further Reading' section.
+
+## Training the model
+
+I trained the classification model with fastai's flexible `vision_learner`, fine-tuning
+`resnet18` which was both smaller than `resnet34` (no surprises there) and less
+liable to early overfitting. I trained the model for 10 epochs.
+
+The object detection model is trained using IceVision, with VFNet as the
+model and `resnet50` as the backbone. I trained the model for 50 epochs and
+reached 89% accuracy on the validation data.
+
+## Further Reading
+
+This initial dataset spurred an ongoing interest in the domain and I've since
+been working on the problem of object detection, i.e. identifying exactly which
+parts of the image contain redactions.
+
+Some of the key blogs I've written about this project:
+
+- How to annotate data for an object detection problem with Prodigy
+  ([link](https://mlops.systems/redactionmodel/computervision/datalabelling/2021/11/29/prodigy-object-detection-training.html))
+- How to create synthetic images to supplement a small dataset
+  ([link](https://mlops.systems/redactionmodel/computervision/python/tools/2022/02/10/synthetic-image-data.html))
+- How to use error analysis and visual tools like FiftyOne to improve model
+  performance
+  ([link](https://mlops.systems/redactionmodel/computervision/tools/debugging/jupyter/2022/03/12/fiftyone-computervision.html))
+- Creating more synthetic data focused on the tasks my model finds hard
+  ([link](https://mlops.systems/tools/redactionmodel/computervision/2022/04/06/synthetic-data-results.html))
+- Data validation for object detection / computer vision (a three part series —
+  [part 1](https://mlops.systems/tools/redactionmodel/computervision/datavalidation/2022/04/19/data-validation-great-expectations-part-1.html),
+  [part 2](https://mlops.systems/tools/redactionmodel/computervision/datavalidation/2022/04/26/data-validation-great-expectations-part-2.html),
+  [part 3](https://mlops.systems/tools/redactionmodel/computervision/datavalidation/2022/04/28/data-validation-great-expectations-part-3.html))

fastai-classification-model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ec0645d5b4428fa3da139e9138653657bb7b1fcad2a138937dcdeb5bb51cf0c
+size 87496043

packages.txt

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+python3-opencv
+python-tk

requirements.txt

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+--find-links https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
+mmcv-full==1.3.17
+mmdet==2.17.0
+gradio==2.7.5
+icevision[all]==0.12.0
+
+fastai
+scikit-image
+pymupdf
+fpdf
+

streamlit_app.py

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+from io import BytesIO
+import os
+import pathlib
+import tempfile
+import time
+
+import fitz
+import gradio as gr
+import PIL
+import skimage
+import streamlit as st
+from fastai.learner import load_learner
+from fastai.vision.all import *
+from fpdf import FPDF
+from icevision.all import *
+from icevision.models.checkpoint import *
+from PIL import Image as PILImage
+
+
+CHECKPOINT_PATH = "./allsynthetic-imgsize768.pth"
+
+
+@st.cache
+def load_icevision_model():
+    return model_from_checkpoint(CHECKPOINT_PATH)
+
+
+@st.cache
+def load_fastai_model():
+    return load_learner("fastai-classification-model.pkl")
+
+
+checkpoint_and_model = load_icevision_model()
+model = checkpoint_and_model["model"]
+model_type = checkpoint_and_model["model_type"]
+class_map = checkpoint_and_model["class_map"]
+
+img_size = checkpoint_and_model["img_size"]
+valid_tfms = tfms.A.Adapter(
+    [*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()]
+)
+
+
+learn = load_fastai_model()
+labels = learn.dls.vocab
+
+
+@st.experimental_memo
+def get_content_area(pred_dict) -> int:
+    if "content" not in pred_dict["detection"]["labels"]:
+        return 0
+    content_bboxes = [
+        pred_dict["detection"]["bboxes"][idx]
+        for idx, label in enumerate(pred_dict["detection"]["labels"])
+        if label == "content"
+    ]
+    cb = content_bboxes[0]
+    return (cb.xmax - cb.xmin) * (cb.ymax - cb.ymin)
+
+
+@st.experimental_memo
+def get_redaction_area(pred_dict) -> int:
+    if "redaction" not in pred_dict["detection"]["labels"]:
+        return 0
+    redaction_bboxes = [
+        pred_dict["detection"]["bboxes"][idx]
+        for idx, label in enumerate(pred_dict["detection"]["labels"])
+        if label == "redaction"
+    ]
+    return sum(
+        (bbox.xmax - bbox.xmin) * (bbox.ymax - bbox.ymin)
+        for bbox in redaction_bboxes
+    )
+
+
+st.title("Redaction Detector")
+
+st.image(
+    "./synthetic-redactions.jpg",
+    width=300,
+)
+uploaded_pdf = st.file_uploader(
+    "Upload a PDF...",
+    type="pdf",
+    accept_multiple_files=False,
+    help="This application processes PDF files. Please upload a document you believe to contain redactions.",
+    on_change=None,
+)
+
+# Add a selectbox to the sidebar:
+st.sidebar.header("Customisation Options")
+
+graph_checkbox = st.sidebar.checkbox(
+    "Show analysis charts",
+    value=True,
+    help="Display charts analysising the redactions found in the document.",
+)
+
+extract_images_checkbox = st.sidebar.checkbox(
+    "Extract redacted images",
+    value=True,
+    help="Create a PDF file containing the redacted images with an object detection overlay highlighting their locations and the confidence the model had when detecting the redactions.",
+)
+
+# Add a slider to the sidebar:
+confidence = st.sidebar.slider(
+    "Confidence level (%)",
+    min_value=0,
+    max_value=100,
+    value=80,
+)
+
+
+@st.cache
+def get_pdf_document(input):
+    with open(
+        pathlib.Path(filename_without_extension / "output.pdf"), "wb"
+    ) as f:
+        f.write(uploaded_pdf.getbuffer())
+    return fitz.open("output.pdf")
+
+
+@st.cache
+def get_image_predictions(img):
+    return model_type.end2end_detect(
+        img,
+        valid_tfms,
+        model,
+        class_map=class_map,
+        detection_threshold=confidence / 100,
+        display_label=True,
+        display_bbox=True,
+        return_img=True,
+        font_size=16,
+        label_color="#FF59D6",
+    )
+
+
+if uploaded_pdf is None:
+    st.markdown(pathlib.Path("article.md").read_text())
+else:
+    st.text("Opening PDF...")
+    filename_without_extension = uploaded_pdf.name[:-4]
+    results = []
+    images = []
+    document = get_pdf_document(uploaded_pdf)
+    total_image_areas = 0
+    total_content_areas = 0
+    total_redaction_area = 0
+    tmp_dir = tempfile.gettempdir()
+
+    for page_num, page in enumerate(document, start=1):
+        image_pixmap = page.get_pixmap()
+        image = image_pixmap.tobytes()
+        _, _, probs = learn.predict(image)
+        results.append(
+            {labels[i]: float(probs[i]) for i in range(len(labels))}
+        )
+        if probs[0] > (confidence / 100):
+            redaction_count = len(images)
+            if not os.path.exists(
+                os.path.join(tmp_dir, filename_without_extension or "abc")
+            ):
+                os.makedirs(os.path.join(tmp_dir, filename_without_extension))
+            image_pixmap.save(
+                os.path.join(
+                    tmp_dir, filename_without_extension, f"page-{page_num}.png"
+                )
+            )
+            images.append(
+                [
+                    f"Redacted page #{redaction_count + 1} on page {page_num}",
+                    os.path.join(
+                        tmp_dir,
+                        filename_without_extension,
+                        f"page-{page_num}.png",
+                    ),
+                ]
+            )
+    redacted_pages = [
+        str(page + 1)
+        for page in range(len(results))
+        if results[page]["redacted"] > (confidence / 100)
+    ]
+    report = os.path.join(
+        tmp_dir, filename_without_extension, "redacted_pages.pdf"
+    )
+
+    if extract_images_checkbox:
+        pdf = FPDF(unit="cm", format="A4")
+        pdf.set_auto_page_break(0)
+        imagelist = sorted(
+            [
+                i
+                for i in os.listdir(
+                    os.path.join(tmp_dir, filename_without_extension)
+                )
+                if i.endswith("png")
+            ]
+        )
+        for image in imagelist:
+            with PILImage.open(
+                os.path.join(tmp_dir, filename_without_extension, image)
+            ) as img:
+                size = img.size
+                width, height = size
+                if width > height:
+                    pdf.add_page(orientation="L")
+                else:
+                    pdf.add_page(orientation="P")
+                pred_dict = get_image_predictions(img)
+
+                total_image_areas += pred_dict["width"] * pred_dict["height"]
+                total_content_areas += get_content_area(pred_dict)
+                total_redaction_area += get_redaction_area(pred_dict)
+
+                pred_dict["img"].save(
+                    os.path.join(
+                        tmp_dir, filename_without_extension, f"pred-{image}"
+                    ),
+                )
+            pdf.image(
+                os.path.join(
+                    tmp_dir, filename_without_extension, f"pred-{image}"
+                ),
+                w=pdf.w,
+                h=pdf.h,
+            )
+        pdf.output(report, "F")
+
+    text_output = f"A total of {len(redacted_pages)} pages were redacted. \n\nThe redacted page numbers were: {', '.join(redacted_pages)}. \n\n"
+
+    if not extract_images_checkbox:
+        st.text(text_output)
+        # DISPLAY IMAGES
+    else:
+        total_redaction_proportion = round(
+            (total_redaction_area / total_image_areas) * 100, 1
+        )
+        content_redaction_proportion = round(
+            (total_redaction_area / total_content_areas) * 100, 1
+        )
+
+        redaction_analysis = f"- {total_redaction_proportion}% of the total area of the redacted pages was redacted. \n- {content_redaction_proportion}% of the actual content of those redacted pages was redacted."
+
+        st.text(text_output + redaction_analysis)
+        # DISPLAY IMAGES