initial commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.bin filter=lfs diff=lfs merge=lfs -text
+**/*.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,16 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Ignored Files/Dirs
+**/Server
+**/Keys
+**/Client
+*-emb.txt

.python-version

@@ -0,0 +1 @@
+3.13

README.md

@@ -1,13 +0,0 @@
----
-title: Histopathologic Cancer FHE
-emoji: ⚡
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-short_description: Binary classification of histopathologic cancer cells
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,277 @@
+import os
+import subprocess
+import time
+
+import gradio as gr
+import numpy as np
+import timm
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+REF_CANCER_IMG = "./examples/a24ce148f6ffa7ef8eefb4efb12ebffe8dd700da.jpg"
+SECURITYLEVELS = ["128", "196", "256"]
+EXAMPLE_IMAGES = [
+    "./examples/0b820b71670c039dd0a51333d1c919f471a9e940.jpg",
+    "./examples/7f6ccae485af121e0b6ee733022e226ee6b0c65f.jpg",
+    "./examples/8eaaa7a400aa79d36c2440a4aa101cc14256cda4.jpg",
+    "./examples/94fa32b29cc1c00403176c0795fffa3cfaa0f20e.jpg",
+    "./examples/730431efa2f79927156dcc4382819e9a6cc2c5bb.jpg",
+    "./examples/a1c001f6b242c72d3066f15ac6eb059ea72d30ba.jpg",
+    "./examples/a106469bbfda4cdc5a9da7ac0152927bf1b4a92d.jpg",
+    "./examples/c3d660212bf2a11c994e0eadff13770a9927b731.jpg",
+    "./examples/f416de7491a31951f79b3cee75b002f4d1bf0162.jpg",
+]
+
+TRUE_LABELS = {
+    "./examples/0b820b71670c039dd0a51333d1c919f471a9e940.jpg": 1,
+    "./examples/7f6ccae485af121e0b6ee733022e226ee6b0c65f.jpg": 1,
+    "./examples/8eaaa7a400aa79d36c2440a4aa101cc14256cda4.jpg": 0,
+    "./examples/94fa32b29cc1c00403176c0795fffa3cfaa0f20e.jpg": 1,
+    "./examples/730431efa2f79927156dcc4382819e9a6cc2c5bb.jpg": 0,
+    "./examples/a1c001f6b242c72d3066f15ac6eb059ea72d30ba.jpg": 0,
+    "./examples/a106469bbfda4cdc5a9da7ac0152927bf1b4a92d.jpg": 0,
+    "./examples/c3d660212bf2a11c994e0eadff13770a9927b731.jpg": 1,
+    "./examples/f416de7491a31951f79b3cee75b002f4d1bf0162.jpg": 0,
+}
+
+
+def display_image(image):
+    return image
+
+
+def get_selected_image(evt: gr.SelectData):
+    image_path = EXAMPLE_IMAGES[evt.index]
+    return Image.open(image_path).convert("RGB")
+
+
+def runBinFile(*args):
+    binary_path = args[0]
+    if not os.path.isfile(binary_path):
+        return "Error: Compiled binary not Match."
+    try:
+        os.chmod(binary_path, 0o755)
+        start = time.time()
+        result = subprocess.run(
+            list(args), stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
+        )
+        end = time.time()
+        duration = (end - start) * 1000
+        if "print" in args:
+            return result.stdout
+        elif "styledPrint" in args:
+            return styled_output(result.stdout)
+        elif result.returncode == 0:
+            return True, f"<b>⏱️ Processing Time:</b> {duration:.0f} ms"
+        else:
+            return (
+                False,
+                f"""
+                ❌ <b>Return code:</b> {result.returncode}<br>
+                <b>stdout:</b><br><pre>{result.stdout.strip()}</pre><br>
+                <b>stderr:</b><br><pre style='color:red;'>{result.stderr.strip()}</pre>
+            """,
+            )
+    except Exception as e:
+        return f"Execution failed: {e}"
+
+
+def styled_output(result):
+    if result.strip().lower() == "match":
+        return "<span style='color: green; font-weight: bold;'>✔️ Match</span>"
+    elif result.strip().lower() == "no match":
+        return "<span style='color: red; font-weight: bold;'>❌ No Match</span>"
+    else:
+        return "<span style='color: red; font-weight: bold;'>Error</span>"
+
+
+def extract_emb(image, mode=None):
+    model = timm.create_model(
+        "hf-hub:1aurent/resnet18.tiatoolbox-pcam", pretrained=True, num_classes=0
+    ).eval()
+
+    data_config = timm.data.resolve_model_data_config(model)
+    transforms = timm.data.create_transform(**data_config, is_training=False)
+    features = transforms(image).unsqueeze(0)
+    with torch.no_grad():
+        embs = model(features)
+        embs = F.normalize(embs, dim=1)
+        embs = embs.detach().numpy()
+        embs = embs.squeeze(0)
+        if mode is not None:
+            np.savetxt(
+                f"{mode}-emb.txt",
+                embs.reshape(1, embs.shape[0]),
+                fmt="%.6f",
+                delimiter=",",
+            )
+    return embs
+
+
+def check(security_level, threshold):
+    image_input_auth = Image.open(REF_CANCER_IMG).convert("RGB")
+    _ = extract_emb(image_input_auth, mode="auth")
+    runBinFile("./bin/encProbe.bin", security_level, "encrypt")
+    runBinFile("./bin/recDecision.bin", security_level, "decision", str(threshold))
+    final_output = runBinFile("./bin/decDecision.bin", security_level, "print")
+
+    if final_output.strip().lower() == "match":
+        result = (
+            "<h1><span style='color: green; font-weight: bold;'>Cancerous</span></h1>"
+        )
+    elif final_output.strip().lower() == "no match":
+        result = "<h1><span style='color: green; font-weight: bold;'>Not Cancerous</span></h1>"
+    else:
+        result = "<h1><span style='color: red; font-weight: bold;'>Error!</span></h1>"
+
+    return result
+
+
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.HTML(
+            """
+            <h1 align="center">Suraksh.AI</h1>
+            <p align="center">
+                <a href="https://www.suraksh.ai"> https://www.suraksh.ai</a>
+            </p>       
+            """
+        )
+        with gr.Row():
+            gr.Markdown("## Setup Phase: 🔐 Generate the FHE public and secret keys.")
+        with gr.Row():
+            with gr.Column():
+                securityLevel = gr.Dropdown(
+                    choices=SECURITYLEVELS, label="Choose a security level"
+                )
+            with gr.Column():
+                key_button = gr.Button("Generate the FHE public and secret keys")
+                key_status = gr.Checkbox(
+                    label="FHE Public and Secret keys generated.", value=False
+                )
+                time_output = gr.HTML()
+                key_button.click(
+                    fn=runBinFile,
+                    inputs=[
+                        gr.State("./bin/genKeys.bin"),
+                        securityLevel,
+                        gr.State("genkeys"),
+                    ],
+                    outputs=[key_status, time_output],
+                )
+        with gr.Row():
+            gr.Markdown("### Step 1: Upload or select a reference cell image")
+        with gr.Row():
+            image_input_enroll = gr.Image(type="pil", visible=False)
+            with gr.Column():
+                image_upload_enroll = gr.Image(
+                    label="Upload a reference cell image.",
+                    type="pil",
+                    sources="upload",
+                )
+                image_upload_enroll.change(
+                    fn=display_image,
+                    inputs=image_upload_enroll,
+                    outputs=image_input_enroll,
+                )
+            with gr.Column():
+                example_gallery = gr.Gallery(value=EXAMPLE_IMAGES, columns=3)
+                example_gallery.select(
+                    fn=get_selected_image, inputs=None, outputs=image_input_enroll
+                )
+            with gr.Column():
+                selectedImage = gr.Image(
+                    type="pil", label="Reference cell image", interactive=False
+                )
+                image_input_enroll.change(
+                    fn=lambda img: img, inputs=image_input_enroll, outputs=selectedImage
+                )
+        with gr.Row():
+            gr.Markdown("### Step 2: Generate reference embedding.")
+        with gr.Row():
+            with gr.Column():
+                example_gallery.select(
+                    fn=get_selected_image, inputs=None, outputs=image_input_enroll
+                )
+                key_button = gr.Button("Generate embedding")
+                enroll_emb_text = gr.JSON(label="Reference embedding")
+                mode = gr.State("enroll")
+                key_button.click(
+                    fn=extract_emb,
+                    inputs=[image_input_enroll, mode],
+                    outputs=enroll_emb_text,
+                )
+        with gr.Row():
+            gr.Markdown("### Step 3: 🔒 Encrypt reference embedding using FHE.")
+        with gr.Row():
+            with gr.Column():
+                key_button = gr.Button("Encrypt")
+                key_status = gr.Checkbox(
+                    label="Reference embedding encrypted.", value=False
+                )
+                time_output = gr.HTML()
+                key_button.click(
+                    fn=runBinFile,
+                    inputs=[
+                        gr.State("./bin/encReference.bin"),
+                        securityLevel,
+                        gr.State("encrypt"),
+                    ],
+                    outputs=[key_status, time_output],
+                )
+
+            with gr.Column():
+                key_button = gr.Button("Display")
+                output_text = gr.Text(
+                    label="Encrypted embedding", lines=3, interactive=False
+                )
+                key_button.click(
+                    fn=runBinFile,
+                    inputs=[
+                        gr.State("./bin/encReference.bin"),
+                        securityLevel,
+                        gr.State("print"),
+                    ],
+                    outputs=output_text,
+                )
+
+        with gr.Row():
+            gr.Markdown(
+                "### Step 4: 🔒 Compute biometric recognition decision using the threshold under FHE."
+            )
+        with gr.Row():
+            gr.Markdown("### Set the recognition threshold.")
+        with gr.Row():
+            slider_threshold = gr.Slider(
+                -512 * 5,
+                512 * 5,
+                step=1,
+                value=133,
+                label="Decision threshold",
+                info="The higher the stricter.",
+                interactive=True,
+            )
+            number_threshold = gr.Textbox(visible=False, value="133")
+            slider_threshold.change(
+                fn=lambda x: x, inputs=slider_threshold, outputs=number_threshold
+            )
+        with gr.Row():
+            check_button = gr.Button("Check")
+        with gr.Row():
+            with gr.Column(scale=1):
+                final_output = gr.HTML()
+                check_button.click(
+                    fn=check,
+                    inputs=[securityLevel, slider_threshold],
+                    outputs=final_output,
+                )
+
+            with gr.Column(scale=1):
+                image_output_enroll = gr.Image(label="Reference", sources="upload")
+                image_input_enroll.change(
+                    fn=display_image,
+                    inputs=image_input_enroll,
+                    outputs=image_output_enroll,
+                )
+
+    demo.launch()

bin/decDecision.bin

@@ -0,0 +1,3 @@
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+size 8045152

bin/encProbe.bin

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+oid sha256:3959987099fb521134b6c46c367b2b6f6106bf9bb45eda98108e26710376f078
+size 8030536

bin/encReference.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6705fbd4703ab2bc52d4af5719f5ece369de22d214dda966b124a85803c3a92d
+size 8069184

bin/genKeys.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30c5e81854404a40c06ac9b727a17907536b1490defb055831921022b25e6e7c
+size 8079376

bin/recDecision.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97d6a8be345463779f296ae73c70a082ea90e2c41add0f99f6875d75ff753c75
+size 8057936

lookupTables/Borders_nB_3_dimF_512.txt

@@ -0,0 +1 @@
+-0.050880,-0.029846,-0.014102,-0.000000,0.014102,0.029846,0.050880

lookupTables/MFIP_nB_3_dQ_0.001_dimF_512.txt

@@ -0,0 +1,8 @@
+5,3,2,1,-1,-2,-3,-5
+3,2,1,0,0,-1,-2,-3
+2,1,0,0,0,0,-1,-2
+1,0,0,0,0,0,0,-1
+-1,0,0,0,0,0,0,1
+-2,-1,0,0,0,0,1,2
+-3,-2,-1,0,0,1,2,3
+-5,-3,-2,-1,1,2,3,5

pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "histopathologic-cancer-fhe"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "gradio>=5.25.2",
+    "timm>=1.0.15",
+    "torch>=2.6.0",
+]

requirements.txt

@@ -0,0 +1,217 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml
+aiofiles==24.1.0
+    # via gradio
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.9.0
+    # via
+    #   gradio
+    #   httpx
+    #   starlette
+audioop-lts==0.2.1
+    # via gradio
+certifi==2025.1.31
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.1
+    # via requests
+click==8.1.8
+    # via
+    #   typer
+    #   uvicorn
+fastapi==0.115.12
+    # via gradio
+ffmpy==0.5.0
+    # via gradio
+filelock==3.18.0
+    # via
+    #   huggingface-hub
+    #   torch
+fsspec==2025.3.2
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+gradio==5.25.2
+    # via histopathologic-cancer-fhe (pyproject.toml)
+gradio-client==1.8.0
+    # via gradio
+groovy==0.1.2
+    # via gradio
+h11==0.14.0
+    # via
+    #   httpcore
+    #   uvicorn
+httpcore==1.0.8
+    # via httpx
+httpx==0.28.1
+    # via
+    #   gradio
+    #   gradio-client
+    #   safehttpx
+huggingface-hub==0.30.2
+    # via
+    #   gradio
+    #   gradio-client
+    #   timm
+idna==3.10
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+jinja2==3.1.6
+    # via
+    #   gradio
+    #   torch
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==3.0.2
+    # via
+    #   gradio
+    #   jinja2
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+networkx==3.4.2
+    # via torch
+numpy==2.2.5
+    # via
+    #   gradio
+    #   pandas
+    #   torchvision
+nvidia-cublas-cu12==12.4.5.8
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.4.127
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.4.127
+    # via torch
+nvidia-cuda-runtime-cu12==12.4.127
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.2.1.3
+    # via torch
+nvidia-curand-cu12==10.3.5.147
+    # via torch
+nvidia-cusolver-cu12==11.6.1.9
+    # via torch
+nvidia-cusparse-cu12==12.3.1.170
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.6.2
+    # via torch
+nvidia-nccl-cu12==2.21.5
+    # via torch
+nvidia-nvjitlink-cu12==12.4.127
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvtx-cu12==12.4.127
+    # via torch
+orjson==3.10.16
+    # via gradio
+packaging==25.0
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+pandas==2.2.3
+    # via gradio
+pillow==11.2.1
+    # via
+    #   gradio
+    #   torchvision
+pydantic==2.11.3
+    # via
+    #   fastapi
+    #   gradio
+pydantic-core==2.33.1
+    # via pydantic
+pydub==0.25.1
+    # via gradio
+pygments==2.19.1
+    # via rich
+python-dateutil==2.9.0.post0
+    # via pandas
+python-multipart==0.0.20
+    # via gradio
+pytz==2025.2
+    # via pandas
+pyyaml==6.0.2
+    # via
+    #   gradio
+    #   huggingface-hub
+    #   timm
+requests==2.32.3
+    # via huggingface-hub
+rich==14.0.0
+    # via typer
+ruff==0.11.6
+    # via gradio
+safehttpx==0.1.6
+    # via gradio
+safetensors==0.5.3
+    # via timm
+semantic-version==2.10.0
+    # via gradio
+setuptools==79.0.0
+    # via torch
+shellingham==1.5.4
+    # via typer
+six==1.17.0
+    # via python-dateutil
+sniffio==1.3.1
+    # via anyio
+starlette==0.46.2
+    # via
+    #   fastapi
+    #   gradio
+sympy==1.13.1
+    # via torch
+timm==1.0.15
+    # via histopathologic-cancer-fhe (pyproject.toml)
+tomlkit==0.13.2
+    # via gradio
+torch==2.6.0
+    # via
+    #   histopathologic-cancer-fhe (pyproject.toml)
+    #   timm
+    #   torchvision
+torchvision==0.21.0
+    # via timm
+tqdm==4.67.1
+    # via huggingface-hub
+triton==3.2.0
+    # via torch
+typer==0.15.2
+    # via gradio
+typing-extensions==4.13.2
+    # via
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   torch
+    #   typer
+    #   typing-inspection
+typing-inspection==0.4.0
+    # via pydantic
+tzdata==2025.2
+    # via pandas
+urllib3==2.4.0
+    # via requests
+uvicorn==0.34.2
+    # via gradio
+websockets==15.0.1
+    # via gradio-client