Minimal clone for deployment, see README for full project

.dockerignore

@@ -0,0 +1,52 @@
+# Dockerignore file for excluding unnecessary files from the Docker build context
+# Byte-compiled / cache
+# Python cache folders
+__pycache__/
+# Compiled Python files (.pyc, .pyo, etc.)
+*.py[cod]
+# Temporary files from Jupyter notebooks
+*.ipynb_checkpoints/
+
+# Environment
+# Environment variables files
+.env
+# Catch other .env-like files
+*.env
+
+# OS metadata files
+.DS_Store
+Thumbs.db
+
+# VSCode
+# Editor-specific settings
+.vscode/
+
+# Git
+.git/
+.gitignore
+
+# Logs, temp files
+*.log
+logs/
+core
+
+# Documentation
+Project_Strategy.md
+# Don't add config files
+/configs/
+# Don't add datasets
+/data/
+# Don't add Python notebooks
+/notebooks/
+# Don't add scripts
+/scripts/
+# Don't add modules
+/src/
+# Don't add trained model files
+/models/
+# Exclude final model file
+!/models/final_model/final_model.tflite
+
+# Don't add main repo requirements.txt file
+# The requirements.txt for the Docker image is in app/requirements.txt
+/requirements.txt

.gitattributes

@@ -0,0 +1,7 @@
+# Files required to be stored with Git LFS on Hugging Face Spaces
+# (all files >10MB, or binary files such as images, fonts...)
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.ttf filter=lfs diff=lfs merge=lfs -text
+*.whl filter=lfs diff=lfs merge=lfs -text
+

.gitignore

@@ -0,0 +1,19 @@
+# Byte-compiled / cache
+# Python cache folders
+__pycache__/
+# Compiled Python files (.pyc, .pyo, etc.)
+*.py[cod]
+# Temporary files from Jupyter notebooks
+*.ipynb_checkpoints/
+# Other temp files
+core
+
+# VSCode
+# Editor-specific settings
+.vscode/
+
+# Environment
+# Environment variables files
+.env
+# Catch other .env-like files
+*.env

Dockerfile

@@ -0,0 +1,72 @@
+# Capsule Defect Detection and Segmentation with ConvNeXt+U-Net and FastAPI
+# Use slim Python image for smaller size
+FROM python:3.9.23-slim-bookworm
+
+# Basic ownership labels
+LABEL maintainer="Kev-HL (GitHub)"
+LABEL org.opencontainers.image.source="https://github.com/Kev-HL/capsule-defect-segmentation-api"
+
+# Set working directory
+WORKDIR /app
+
+# Create a non-root user and group (appuser)
+RUN addgroup --system appuser && adduser --system --ingroup appuser appuser
+
+# Update system packages and clean up
+RUN apt-get update && apt-get upgrade -y && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements.txt for API dependencies
+COPY app/requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Install TensorFlow Lite runtime from local wheel file
+# Remove or comment if using a different interpreter (tflite-runtime or ai-edge-litert)
+COPY --chown=appuser:appuser app/tflite_runtime-2.19.0-cp39-cp39-linux_x86_64.whl .
+RUN pip install --no-cache-dir ./tflite_runtime-2.19.0-cp39-cp39-linux_x86_64.whl
+
+# Clean up
+RUN rm tflite_runtime-2.19.0-cp39-cp39-linux_x86_64.whl && \
+    find /usr/local/lib/python3.9/ -type d -name '__pycache__' -prune -exec rm -rf {} + && \
+    rm -rf /usr/share/doc /usr/share/man /usr/share/info /usr/share/locale/*
+
+# Copy app code (FastAPI app)
+COPY --chown=appuser:appuser app/main.py .
+
+# Copy aux code (functions for FastAPI app)
+COPY --chown=appuser:appuser app/aux.py .
+
+# Copy model file
+COPY --chown=appuser:appuser models/final_model/final_model.tflite .
+
+# Copy HTML templates
+RUN mkdir -p templates && chown -R appuser:appuser templates
+COPY --chown=appuser:appuser app/templates/ templates/
+
+# Create static directories for uploads, results and samples
+RUN mkdir -p static/uploads static/results static/samples && chown -R appuser:appuser static
+
+# Copy sample images
+COPY --chown=appuser:appuser app/samples/ static/samples/
+
+# Copy font file (and license) for text rendering on images
+RUN mkdir -p fonts && chown -R appuser:appuser fonts
+COPY --chown=appuser:appuser app/fonts/OpenSans-Bold.ttf fonts
+COPY --chown=appuser:appuser app/fonts/OFL.txt fonts
+
+# Set permissions for static files
+RUN chmod -R 777 static
+
+# Switch to non-root user
+USER appuser
+
+# Expose port (FastAPI default)
+EXPOSE 8000
+
+# Set environment variables
+# Disable buffering for easier logging (immediate output)
+ENV PYTHONUNBUFFERED=1
+
+# Start FastAPI app with uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -0,0 +1,111 @@
+> **Note:** This repo contains only deployment/demo files.  
+> For full source, notebooks, and complete code, see [Capsule Defect Detection and Segmentation with ConvNeXt+U-Net and FastAPI](https://github.com/Kev-HL/capsule-defect-segmentation-api).  
+
+# Capsule Defect Detection and Segmentation with ConvNeXt+U-Net and FastAPI
+
+This project addresses a real-world computer vision challenge: detecting and localizing defects on medicinal capsules via image classification and segmentation.  
+The aim is to deliver a complete pipeline—data preprocessing, model training and evaluation, and deployment, demonstrating practical ML engineering from scratch to API.
+
+---
+
+## Main Repo
+
+This is a minimal clone with only the necessary files from the main repo.  
+For full source, notebooks, and complete code, see [Capsule Defect Detection and Segmentation with ConvNeXt+U-Net and FastAPI](https://github.com/Kev-HL/capsule-defect-segmentation-api).  
+
+---
+
+## Project Overview
+
+End-to-end defect detection and localization using the **Capsule** class from the **MVTec AD dataset**.  
+Key steps include:
+- Data preprocessing, formatting, and augmentation
+- Model design (pre-trained backbone + custom heads)
+- Training, evaluation, and hyperparameter tuning
+- Dockerized FastAPI deployment for inference
+
+*Portfolio project to showcase ML workflow and engineering.*
+
+---
+
+## Key Results
+
+- Evaluation dataset: MVTec AD 'capsule' class, 70/15/15 train/val/test split
+- Quantitative results on test evaluation:
+  - Classification accuracy: **83 %**
+  - Classification defect-only accuracy: **75 %**
+  - Defect presence accuracy: **91 %**
+  - Segmentation quality (mIoU / Dice): **0.79 / 0.73**
+  - Segmentation defect-only quality (mIoU / Dice): **0.70 / 0.55**
+- Model artifacts:
+  - Original model size (.keras / SavedModel): **345 MB**
+  - Raw Converted TFLite size (.tflite): **119 MB**
+  - Optimized Converted TFLite size (.tflite): **31 MB** (Dynamic Range Quantization applied)
+- Container / runtime:
+  - Docker image size: **317 MB**
+  - Runtime used: **tflite-runtime + Uvicorn/FastAPI**
+  - Avg inference latency (inference only, set tensor + invoke): **239 ms**
+  - Avg inference latency (single POST request, measured): **271 ms**
+  - Average memory usage during inference: **321 MB**
+  - Startup time (local): **72 ms**
+- Observations:
+  - The app returns expected visualizations and class labels for the MVTec-style test images.
+  - POST inference latency measured locally, expect increased latency on real use (network delays)
+  - Given the small and highly imbalanced dataset (351 samples, 242 'good' and 109 defective distributed in 5 defect types, ~22 per defect), coupled with the nature of the samples (only distinctive feature is the defect, which in most cases has a small size and varied shape), performance is not as strong as desired, and results lack statistical confidence for a real-case use. Without more data would be difficult to get a reasonable improvement.
+
+---
+
+## Dataset
+
+- *Capsule* class from [MVTec AD dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad)
+- License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+- Dataset folder contains license file  
+- Usage is strictly non-commercial/educational
+
+---
+
+## Tech Stack
+
+- Python
+- TensorFlow
+- Scikit-Learn
+- Numpy / Pandas
+- OpenCV / Pillow
+- Ray Tune (Experiment tracking)
+- OmegaConf (Config management)
+- Docker, FastAPI, Uvicorn (Deployment)
+
+---
+
+## Folder Structure
+
+```
+data/       # Dataset and annotations
+app/        # Inference and deployment code and files
+models/     # Saved trained models and training logs
+```
+
+---
+
+## How to Run
+
+**Build image for deployment:**  
+- Requirements:
+  - `models/final_model/final_model.tflite` (included)
+  - `app/` folder and contents  (included)
+  - `Dockerfile` (included)
+  - `.dockerignore` (included)
+- From the project root, build and run the Docker image:
+```sh
+docker build -t cv-app .
+docker run -p 8000:8000 cv-app
+```
+- Open http://0.0.0.0:8000 in your browser to access the demo UI  
+
+_Note: For the full source code and steps on how to recreate the model, visit the full repo (see "Main Repo" section near the top)_  
+
+---
+
+## Contact
+
+For questions reach out via GitHub (Kev-HL).

app/aux.py

@@ -0,0 +1,215 @@
+"""
+Module with auxiliary functions for the FastAPI defect detection demo app.
+
+Provided functions:
+- preprocess_image: Preprocess input image for model inference.
+- inference: Perform model inference and return class ID, class name, and segmentation mask.
+- encode_mask_to_base64: Encode segmentation mask to base64 string.
+- save_image: Save uploaded image bytes to a file.
+- draw_prediction: Save image with overlayed prediction mask and class name.
+
+Design notes
+
+- This application is a demonstration / portfolio app. For simplicity and safety during demo runs, inference is performed synchronously using a single global TFLite Interpreter instance protected by a threading.Lock to ensure thread-safety.
+- The code intentionally makes a number of fixed assumptions about the model and runtime. If the model or deployment requirements change, the corresponding preprocessing, postprocessing and runtime setup should be updated and tested.
+
+Assumptions:
+
+    File system and assets
+        Font used for drawing labels: ./fonts/OpenSans-Bold.ttf
+        Static files served from: ./static
+        Directories (./static/uploads, ./static/results, ./static/samples) are expected to be present/created by the deployment (Dockerfile or startup); added an exist_ok mkdir as safeguard.
+
+    Upload / input constraints
+        Uploaded images are expected to be valid PNG images (this matches the local MVTec AD dataset used for development).
+        Maximum accepted upload size: 5 MB.
+
+    Runtime / model
+        Uses tflite-runtime Interpreter for model inference (Interpreter from tflite_runtime.interpreter).
+        TFLite model file path: ./final_model.tflite
+        Single Interpreter instance is created at startup and reused for all requests (protected by a threading.Lock).
+
+    Model I/O (these are the exact assumptions used by the code)
+        Expected input tensor: shape (1, 512, 512, 3), dtype float32, pixel value range [0, 255] (model handles internally normalization to [0, 1]).
+        Expected output[0]: segmentation mask of shape (1, 512, 512, 1), dtype float32, values in [0, 1] (probability map).
+        Expected output[1]: class probabilities of shape (1, 6), dtype float32 (softmax-like probabilities).
+"""
+
+# IMPORTS
+
+# Standard library imports
+import base64
+import io
+import logging
+import os
+import threading
+import time
+import uuid
+
+# Third-party imports
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+# CONFIGURATION AND CONSTANTS
+
+# Font path for drawing text on images
+FONT_PATH = './fonts/OpenSans-Bold.ttf'
+
+# Input image size for the model
+INPUT_IMAGE_SIZE = (512, 512)
+
+# Transparency level and color for mask overlay
+MAX_ALPHA = 100 # [0-255]
+MASK_COLOR = (0, 255, 255, 0)  # Cyan RGB (R,G,B,A)
+
+# Dictionary mapping class IDs to class names
+CLASS_MAP = {
+    0: 'good',
+    1: 'crack',
+    2: 'faulty_imprint',
+    3: 'poke',
+    4: 'scratch',
+    5: 'squeeze'
+}
+
+# AUXILIARY FUNCTIONS FOR main.py
+
+# Function to preprocess the image
+def preprocess_image(image_bytes) -> np.ndarray:
+    """
+    Preprocess the input image for model inference.
+    Args:
+        image_bytes: Raw bytes of the input image.
+    Returns:
+        Preprocessed image as a numpy array of shape (1, INPUT_IMAGE_SIZE[0], INPUT_IMAGE_SIZE[1], 3) and dtype float32.
+    """
+    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+    image = image.resize(INPUT_IMAGE_SIZE)
+    img_array = np.array(image, dtype=np.float32)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Function to perform inference on a preprocessed image
+def inference(img, inference_ctx)  -> tuple[int, str, np.ndarray]:
+    """
+    Perform model inference on the preprocessed image.
+    Args:
+        img: Preprocessed image as a numpy array.
+        inference_ctx: Dictionary containing the threading lock and the interpreter and its details.
+    Returns:
+        Tuple containing:
+            - class_id: Predicted class ID (int).
+            - class_name: Predicted class name (str).
+            - mask: Predicted segmentation mask as a numpy array.
+    """
+    # Ensure the interpreter is thread-safe
+    with inference_ctx['interpreter_lock']:
+        
+        # Set the input tensor and invoke the interpreter
+        inference_ctx['interpreter'].set_tensor(inference_ctx['input_details'][0]['index'], img)
+        inference_ctx['interpreter'].invoke()
+        # Get the prediction results
+        pred_mask = inference_ctx['interpreter'].get_tensor(inference_ctx['output_details'][0]['index'])
+        pred_label_probs = inference_ctx['interpreter'].get_tensor(inference_ctx['output_details'][1]['index'])
+
+        # Format the prediction results and get the class name
+        pred_label = np.argmax(pred_label_probs, axis=1)
+        class_id = int(pred_label[0])
+        class_name = CLASS_MAP.get(class_id, 'unknown')
+        mask = pred_mask.squeeze()
+        
+        return class_id, class_name, mask
+
+# Function to encode mask to base64
+def encode_mask_to_base64(mask_array) -> str:
+    """
+    Encode the segmentation mask to a base64 string.
+    Args:
+        mask_array: Segmentation mask as a numpy array.
+    Returns:
+        Base64-encoded string of the mask image.
+    """
+    mask = (mask_array * 255).astype(np.uint8)
+    mask_img = Image.fromarray(mask, mode='L')
+    buffer = io.BytesIO()
+    mask_img.save(buffer, format='PNG')
+    mask64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    return mask64
+
+# Function to save an image for later use
+def save_image(image_bytes) -> tuple[str, str]:
+    """
+    Save the uploaded image bytes to a file.
+    Args:
+        image_bytes: Raw bytes of the input image.
+    Returns:
+        Tuple containing:
+            - filename: Name of the saved file (str).
+            - path: Path to the saved file (str).
+    """
+    filename = f'{uuid.uuid4().hex}.png'
+    os.makedirs('static/uploads', exist_ok=True)
+    path = f'static/uploads/{filename}'
+    with open(path, 'wb') as f:
+        f.write(image_bytes)
+    return filename, path
+
+# Function to save image with overlayed prediction mask and class name
+def draw_prediction(image_path, mask_array, class_name) -> tuple[str, str]:
+    """
+    Save image with overlayed prediction mask and class name.
+    Args:
+        image_path: Path to the original image file.
+        mask_array: Segmentation mask as a numpy array.
+        class_name: Predicted class name (str).
+    Returns:
+        Tuple containing:
+            - filename: Name of the saved file (str).
+            - path: Path to the saved file (str).
+    """
+    # Load the original image and mask
+    orig_img = Image.open(image_path).convert('RGB')
+    mask = (mask_array * 255).astype(np.uint8)
+    mask_img = Image.fromarray(mask, mode='L')
+    if mask_img.size != orig_img.size:
+        mask_img = mask_img.resize(orig_img.size, resample=Image.Resampling.BILINEAR)
+
+    # Overlay the mask on the original image with some transparency
+    alpha_arr = (np.array(mask_img, dtype=np.float32) / 255.0 * float(MAX_ALPHA)).astype(np.uint8)
+    alpha_img = Image.fromarray(alpha_arr, mode='L')
+    overlay = Image.new('RGBA', orig_img.size, MASK_COLOR)
+    overlay.putalpha(alpha_img)
+    overlay_img = Image.alpha_composite(orig_img.convert('RGBA'), overlay).convert('RGB')
+
+    # Draw the class name on the image
+    draw = ImageDraw.Draw(overlay_img)
+    try:
+        font = ImageFont.truetype(FONT_PATH, 35)
+    except:
+        font = ImageFont.load_default()
+    draw.text((40, 40), class_name, fill='red', font=font)
+
+    # Save the visualization image (with bounding box and label)
+    filename = f'{uuid.uuid4().hex}.png'
+    os.makedirs('static/results', exist_ok=True)
+    path = f'static/results/{filename}'
+    overlay_img.save(path)
+
+    return filename, path
+
+# Function to delete files after a delay
+def delete_files_later(files, delay=10) -> None:
+    """
+    Delete files after a specified delay.
+    Args:
+        files: List of file paths to delete.
+        delay: Time in seconds to wait before deleting files (default is 10).
+    """
+    def _del_files():
+        time.sleep(delay)
+        for f in files:
+            try: os.remove(f)
+            except: logging.exception('Error deleting file %s', f)
+    t = threading.Thread(target=_del_files, daemon=True)
+    t.start()
+

app/fonts/OFL.txt

@@ -0,0 +1,88 @@
+Copyright 2020 The Open Sans Project Authors (https://github.com/googlefonts/opensans)
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font
+creation efforts of academic and linguistic communities, and to
+provide a free and open framework in which fonts may be shared and
+improved in partnership with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded,
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply to
+any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software
+components as distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to,
+deleting, or substituting -- in part or in whole -- any of the
+components of the Original Version, by changing formats or by porting
+the Font Software to a new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed,
+modify, redistribute, and sell modified and unmodified copies of the
+Font Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components, in
+Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the
+corresponding Copyright Holder. This restriction only applies to the
+primary font name as presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created using
+the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.

app/fonts/OpenSans-Bold.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27da758f4dcac9a65abe914c13b463b42982b9909bc65713424099f4810bd1e6
+size 147264

app/main.py

@@ -0,0 +1,301 @@
+"""
+FastAPI app for defect detection using a TFLite model.
+
+Provided endpoints
+
+- GET / : Render an HTML form (no results).
+- POST /predict/ : REST API. Predict defect on an uploaded image; returns JSON.
+- POST /upload/ : Upload image, run prediction, and return an HTML page with visualization and results.
+- POST /random-sample/ : Run prediction on a random sample image and return an HTML page with visualization and results.
+
+Design notes
+
+- This application is a demonstration / portfolio app. For simplicity and safety during demo runs, inference is performed synchronously using a single global TFLite Interpreter instance protected by a threading.Lock to ensure thread-safety.
+- The code intentionally makes a number of fixed assumptions about the model and runtime. If the model or deployment requirements change, the corresponding preprocessing, postprocessing and runtime setup should be updated and tested.
+
+Assumptions:
+
+    File system and assets
+        Font used for drawing labels: ./fonts/OpenSans-Bold.ttf
+        Static files served from: ./static
+        Directories (./static/uploads, ./static/results, ./static/samples) are expected to be present/created by the deployment (Dockerfile or startup); added an exist_ok mkdir as safeguard.
+
+    Upload / input constraints
+        Uploaded images are expected to be valid PNG images (this matches the local MVTec AD dataset used for development).
+        Maximum accepted upload size: 5 MB.
+
+    Runtime / model
+        Uses tflite-runtime Interpreter for model inference (Interpreter from tflite_runtime.interpreter).
+        TFLite model file path: ./final_model.tflite
+        Single Interpreter instance is created at startup and reused for all requests (protected by a threading.Lock).
+
+    Model I/O (these are the exact assumptions used by the code)
+        Expected input tensor: shape (1, 512, 512, 3), dtype float32, pixel value range [0, 255] (model handles internally normalization to [0, 1]).
+        Expected output[0]: segmentation mask of shape (1, 512, 512, 1), dtype float32, values in [0, 1] (probability map).
+        Expected output[1]: class probabilities of shape (1, 6), dtype float32 (softmax-like probabilities).
+"""
+
+# IMPORTS
+
+# Standard library imports
+import io
+import logging
+import os
+import random
+import time
+import threading
+
+# Third-party imports
+from fastapi import FastAPI, File, UploadFile, Request, BackgroundTasks
+from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+from PIL import Image, UnidentifiedImageError
+from tflite_runtime.interpreter import Interpreter
+# from ai_edge_litert.interpreter import Interpreter
+
+# Auxiliary imports (Dockerfile sets CWD to /app)
+from aux import preprocess_image, inference, save_image, draw_prediction, encode_mask_to_base64, delete_files_later
+
+# START TIME LOGGING
+import time
+app_start = time.perf_counter()
+
+# CONFIGURATION AND CONSTANTS
+
+# Path to TFLite model file
+MODEL_PATH = './final_model.tflite'
+
+# Number of threads for TFLite interpreter
+NUM_THREADS = 4
+
+# Jinja2 templates directory
+TEMPLATES = Jinja2Templates(directory='templates')
+
+# Max file size for uploads (5 MB)
+MAX_FILE_SIZE = 5 * 1024 * 1024  # 5 MB
+
+
+# MAIN APPLICATION
+
+
+# Set up logging to show INFO level and above messages
+logging.basicConfig(level=logging.INFO)
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Mount static files directory for serving images and other assets
+# App will raise errors if folders do not exist
+# Directory creation is handled by the Dockerfile
+os.makedirs('static', exist_ok=True)
+app.mount('/static', StaticFiles(directory='static'), name='static')
+
+# Load model, set up interpreter and get input/output details
+try:
+    interpreter = Interpreter(model_path=MODEL_PATH, num_threads=NUM_THREADS)
+except:
+    logging.warning(f'num_threads={NUM_THREADS} not supported, falling back to single-threaded interpreter.')
+    interpreter = Interpreter(model_path=MODEL_PATH)
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+logging.info('TF Lite input details: %s \n', input_details)
+logging.info('TF Lite output details: %s \n', output_details)
+
+# Create a threading lock for the interpreter to ensure thread-safety
+interpreter_lock = threading.Lock()
+
+# Inference context to be passed to inference function
+inference_ctx = {
+    'interpreter_lock': interpreter_lock,
+    'interpreter': interpreter,
+    'input_details': input_details,
+    'output_details': output_details,
+}
+
+# Startup time measurement
+@app.on_event('startup')
+async def report_startup_time():
+    startup_time = (time.perf_counter() - app_start) * 1000  # in milliseconds
+    logging.info(f'App startup time: {startup_time:.2f} ms \n')
+
+# Root endpoint to render the HTML form
+@app.get('/', response_class=HTMLResponse)
+async def root(request: Request):
+    # Render the HTML form with empty image URLs and no result
+    return TEMPLATES.TemplateResponse(
+        'index.html',
+        {
+            'request': request,
+            'result': None,
+            'orig_img_url': None,
+            'vis_img_url': None,
+        }
+    )
+
+# Endpoint to handle image prediction (API)
+@app.post('/predict/')
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Check if the uploaded file is a PNG image
+        if file.content_type != 'image/png':
+            return JSONResponse(status_code=400, content={'error': 'Only PNG images are supported.'})
+
+        # Read the image
+        image_bytes = await file.read()
+
+        # Check if the file size exceeds the maximum limit
+        if len(image_bytes) > MAX_FILE_SIZE:
+            return JSONResponse(status_code=400, content={'error': 'File size exceeds the maximum limit of 5 MB.'})
+        
+        # Check if the image is a valid PNG (not just a file with .png extension)
+        try:
+            img_check = Image.open(io.BytesIO(image_bytes))
+            if img_check.format != 'PNG':
+                raise ValueError('Not a PNG')
+        except (UnidentifiedImageError, ValueError):
+            return JSONResponse(status_code=400, content={'error': 'Invalid image file.'})
+
+        # Preprocess the image
+        img = preprocess_image(image_bytes)
+
+        # Run inference on the preprocessed image
+        class_id, class_name, mask = inference(img, inference_ctx)
+
+        # Encode mask to base64
+        mask64 = encode_mask_to_base64(mask)
+
+        # Return the prediction results as JSON
+        return {
+            'class_id': class_id,
+            'class_name': class_name,
+            'mask64_PNG_L': mask64,
+        }
+    except Exception as e:
+        logging.exception(f'Error during prediction: {e}')
+        return JSONResponse(status_code=500, content={'error': 'Model inference failed.'})
+
+# Endpoint to handle image upload and prediction with visualization
+@app.post('/upload/', response_class=HTMLResponse)
+async def upload(
+    request: Request,
+    file: UploadFile = File(...),
+    background_tasks: BackgroundTasks = None
+):
+    try:
+        # Check if the uploaded file is a PNG image
+        if file.content_type != 'image/png':
+            result = {'error': 'Only PNG images are supported.'}
+            return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': result})
+
+        # Read the uploaded image
+        image_bytes = await file.read()
+
+        # Check if the file size exceeds the maximum limit
+        if len(image_bytes) > MAX_FILE_SIZE:
+            return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': {'error': 'File too large (max 5MB).'}})
+
+        # Check if the image is a valid PNG (not just a file with .png extension)
+        try:
+            img_check = Image.open(io.BytesIO(image_bytes))
+            if img_check.format != 'PNG':
+                raise ValueError('Not a PNG')
+        except (UnidentifiedImageError, ValueError):
+            return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': {'error': 'Invalid image file.'}})
+
+        # Save the preprocessed image
+        preproc_filename, preproc_path = save_image(image_bytes)
+    
+        # Preprocess the image
+        img = preprocess_image(image_bytes)
+
+        # Run inference on the preprocessed image
+        class_id, class_name, mask = inference(img, inference_ctx)
+
+        # Overlay mask and draw class name on the preprocessed image for display
+        pred_filename, pred_path = draw_prediction(preproc_path, mask, class_name)
+
+        # Encode mask to base64
+        mask64 = encode_mask_to_base64(mask)
+
+        # Prepare the result to be displayed in the HTML template
+        result = {
+            'class_id': class_id,
+            'class_name': class_name,
+            'mask64_PNG_L': mask64,
+        }
+
+        # Schedule deletion of both images after 10 seconds
+        if background_tasks is not None:
+            background_tasks.add_task(delete_files_later, [preproc_path, pred_path], delay=10)
+
+        # Render the HTML template with the result and image URLs
+        return TEMPLATES.TemplateResponse(
+            'index.html',
+            {
+                'request': request,
+                'result': result,
+                'preproc_img_url': f'/static/uploads/{preproc_filename}',
+                'pred_img_url': f'/static/results/{pred_filename}',
+            }
+        )
+    except Exception as e:
+        logging.exception(f'Error during prediction: {e}')
+        return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': {'error': 'Model inference failed.'}})
+
+# Endpoint to handle random image (from samples) prediction with visualization
+@app.post('/random-sample/', response_class=HTMLResponse)
+async def random_sample(request: Request, background_tasks: BackgroundTasks = None):
+    try:
+        # Check if the samples directory exists and contains PNG files
+        samples_dir = 'static/samples'
+        sample_files = [f for f in os.listdir(samples_dir) if f.lower().endswith('.png')]
+        if not sample_files:
+            result = {'error': 'No sample images available.'}
+            return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': result})
+        
+        # Randomly select a sample image and read it
+        chosen_file = random.choice(sample_files)
+        with open(os.path.join(samples_dir, chosen_file), 'rb') as f:
+            image_bytes = f.read()
+        
+        # Save preprocessed image
+        preproc_filename, preproc_path = save_image(image_bytes)
+        
+        # Preprocess the image
+        img = preprocess_image(image_bytes)
+
+        # Run inference on the preprocessed image
+        class_id, class_name, mask = inference(img, inference_ctx)
+
+        # Overlay mask and draw class name on the preprocessed image for display
+        pred_filename, pred_path = draw_prediction(preproc_path, mask, class_name)
+
+        # Encode mask to base64
+        mask64 = encode_mask_to_base64(mask)
+
+        # Prepare the result to be displayed in the HTML template
+        result = {
+            'class_id': class_id,
+            'class_name': class_name,
+            'mask64_PNG_L': mask64,
+        }
+
+        # Schedule deletion of both images after 10 seconds
+        if background_tasks is not None:
+            background_tasks.add_task(delete_files_later, [preproc_path, pred_path], delay=10)
+
+        # Render the HTML template with the result and image URLs
+        return TEMPLATES.TemplateResponse(
+            'index.html',
+            {
+                'request': request,
+                'result': result,
+                'preproc_img_url': f'/static/uploads/{preproc_filename}',
+                'pred_img_url': f'/static/results/{pred_filename}',
+            }
+        )
+    except Exception as e:
+        logging.exception(f'Error during prediction: {e}')
+        return TEMPLATES.TemplateResponse('index.html', {'request': request, 'result': {'error': 'Model inference failed.'}})

app/requirements.txt

@@ -0,0 +1,33 @@
+# Requirements for the application
+# Python version used in the container: 3.9.23
+fastapi==0.121.3
+jinja2==3.1.6
+numpy==1.26.4
+pillow==11.3.0
+python-multipart==0.0.20
+uvicorn==0.38.0
+
+# Additional requirements for testing
+# Uncomment if needed
+# measure_inference.py requires psutil for memory sampling
+# measure_http.py requires requests for HTTP requests
+# psutil==7.1.3
+# requests==2.32.5
+
+# Model interpreter for the TFLite model is installed in the Dockerfile (manually compiled from LiteRT repo)
+# https://github.com/google-ai-edge/LiteRT
+# Commit used for the provided wheel: cc245c70a9113041467a4add21be6d1553b8d831
+# If replicating the environment without the provided wheel, install one of the following:
+# And remove/comment the interpreter installation line in the Dockerfile
+#
+# - full tensorflow (includes tflite interpreter till TF 2.20)
+#   USAGE: from tensorflow.lite.python.interpreter import Interpreter (for TF 2.20.0, other versions may differ)
+# tensorflow==2.20.0
+#
+# - tflite-runtime if trained/converted with older TF versions, smaller package but not compatible with recent op versions (deprecated package)
+#   USAGE: from tflite_runtime.interpreter import Interpreter
+# tflite-runtime==2.14.0
+#
+# - ai-edge-litert for the latest TFLite interpreter with extended op support, but larger package size
+#   USAGE: from ai_edge_litert.interpreter import Interpreter
+# ai-edge-litert==2.0.3

app/templates/index.html

@@ -0,0 +1,51 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>[DEMO] ML API</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 2em; }
+        form { margin-bottom: 1em; }
+        #result { white-space: pre-wrap; background: #f5f5f5; padding: 1em; border-radius: 5px; }
+    </style>
+</head>
+<body>
+    <h1>[DEMO] Defect Classification & Localization on MVTec AD Capsule Dataset</h1>
+
+    <!-- This form allows users to upload a PNG image for defect detection -->
+    <form action="/upload/" method="post" enctype="multipart/form-data">
+        <label for="file">Upload PNG image:</label>
+        <input type="file" id="file" name="file" accept="image/png" required>
+        <button type="submit">Predict</button>
+    </form>
+
+    <!-- This button allows users to try a random sample image using the following script-->
+    <button id="randomBtn">Try a random sample image</button>
+    <script>
+    document.getElementById("randomBtn").onclick = function() {
+        // Submit POST to /random-sample/ without any file
+        fetch("/random-sample/", {method: "POST"})
+        .then(response => response.text())
+        .then(html => {
+            document.open();
+            document.write(html);
+            document.close();
+        });
+        return false;
+    };
+    </script>
+
+    <!-- Display the result of the prediction -->
+    <!-- First display the prediction and then the json result -->
+
+    {% if preproc_img_url and pred_img_url %}
+        <h3>Preprocessed image (as seen by the model):</h3>
+        <img src="{{ preproc_img_url }}" alt="preprocessed" style="max-width: 400px; border:1px solid #ccc;">
+        <h3>Prediction image:</h3>
+        <img src="{{ pred_img_url }}" alt="prediction" style="max-width: 400px; border:1px solid #ccc;">
+    {% endif %}
+    {% if result %}
+        <h2>Result</h2>
+        <div id="result">{{ result | tojson(indent=2) }}</div>
+    {% endif %}
+</body>
+</html>

app/tflite_runtime-2.19.0-cp39-cp39-linux_x86_64.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6c1d62bd5838cad61d790185bbe25f377d034198719c46326be519f6676739c
+size 3378306

data/capsule/license.txt

@@ -0,0 +1,438 @@
+Attribution-NonCommercial-ShareAlike 4.0 International
+
+=======================================================================
+
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+
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models/final_model/final_model.tflite

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