add CORS

Reviewed-on: #6

.idea/.gitignore

@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/misc.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="MarkdownSettingsMigration">
-    <option name="stateVersion" value="1" />
-  </component>
-</project>

README.md

@@ -0,0 +1,11 @@
+Cat detection
+
+Quick guide:
+1. install docker
+2. build image with `./scripts/build_image.bat`
+3. run container with `./scripts/start.bat`
+
+Quick tests guide:
+1. run unit tests `./scripts/run_tests.bat`
+
+Have fun!

basics.ipynb

@@ -1,39 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2024-01-04T16:38:33.550511800Z",
-     "start_time": "2024-01-04T16:38:33.542353Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cat_detection.py

@@ -0,0 +1,58 @@
+from io import BytesIO
+
+import numpy as np
+from PIL import Image
+from keras.src.applications.resnet import preprocess_input, decode_predictions
+from keras.applications.resnet import ResNet50
+
+
+"""
+    Recognition file.
+    Model is ResNet50. Pretrained model to image recognition.
+    If model recognize cat then returns response with first ten CAT predictions.
+    If first prediction is not a cat then returns False.
+    If prediction is not a cat (is not within list_of_labels) then skips this prediction.
+    Format of response:
+    {
+        'label': {label}
+        'score': {score}
+    }
+"""
+
+
+model = ResNet50(weights='imagenet')
+
+
+# PRIVATE Preprocess image method
+def _preprocess_image(image):
+    try:
+        img = Image.open(BytesIO(image.read()))
+        img = img.resize((224, 224))
+        img_array = np.array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = preprocess_input(img_array)
+        return img_array
+    except Exception as e:
+        print(f"Error preprocessing image: {e}")
+        return None
+
+
+# Generate response
+def _generate_response(decoded_predictions, list_of_labels):
+    results = {}
+    for i, (imagenet_id, label, score) in enumerate(decoded_predictions):
+        if i == 0 and label not in list_of_labels:
+            return None
+        if score < 0.01:
+            break
+        if label in list_of_labels:
+            results[len(results) + 1] = {"label": label, "score": round(float(score), 2)}
+    return results
+
+
+# Cat detection
+def detect_cat(image_file, list_of_labels):
+    img_array = _preprocess_image(image_file)
+    prediction = model.predict(img_array)
+    decoded_predictions = decode_predictions(prediction, top=10)[0]
+    return _generate_response(decoded_predictions, list_of_labels)

docker/.dockerignore

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+*.md
+/venv
+.git
+__pycache__
+.pytest_cache
+/tests

docker/Dockerfile

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+FROM python:3.11
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+EXPOSE 5000
+
+CMD ["python", "main.py"]

docker/docker-compose.yml

@@ -0,0 +1,10 @@
+version: '3.3'
+
+services:
+  cat-detection:
+    image: cat-detection
+    build:
+      context: ../
+      dockerfile: ./docker/Dockerfile
+    ports:
+      - "5000:5000"

docs/basics.md

@@ -0,0 +1,6 @@
+#inzynieriaOprogramowania #semestr3 
+
+# Faza 1 - ogólna architektura projektu
+Aplikacja oparta na micro-frameworku Flask, w której użytkownik przesyła zdjęcie na serwer i w odpowiedzi dostaje na ile % na zdjęciu znajduje się kot (poszczególne przedziały będą miały inną informację typu 100%-95%: "to *prawie* na pewno jest kot" itd.).
+
+Wyniki oceny będą przechowywane w sesji - będą zapisane do momentu wyłączenia przeglądarki, przez co nie ma potrzeby stawiania bazy danych.

docs/docs.md

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+# Api
+
+Port -> 5000
+
+endpoint -> api/v1/detect-cat
+
+Key -> 'Image'
+
+Value -> {UPLOADED_FILE}
+
+Flask Rest API application to cat recognition.
+If request is valid then send response with results of recognition.
+If key named 'Image' in body does not occur then returns 400 (BAD REQUEST).
+Otherwise, returns 200 with results of recognition.
+Format of response:
+```json
+{
+    "lang": "{users_lang}",
+    "results": {
+        "{filename}": {
+            "isCat": "{is_cat}",
+            "results": {
+                "1": "{result}",
+                "2": "{result}",
+                "3": "{result}",
+                "4": "{result}",
+                "5": "{result}",
+                "6": "{result}",
+                "7": "{result}",
+                "8": "{result}",
+                "9": "{result}",
+                "10": "{result}"
+            }
+        }
+    },
+    "errors": [
+        "{error_message}",
+        "{error_message}"
+    ]
+}
+```
+Format of result:
+```json
+    {
+        "label": "{label}",
+        "score": "{score}"
+    }
+```
+
+Example response:
+```json
+
+```

language_label_mapper.py

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+import os
+
+from jproperties import Properties
+
+"""
+    Translator method.
+    If everything fine then returns translated labels.
+    Else throws an Exception and returns untranslated labels.
+"""
+
+
+def translate(to_translate, lang):
+    try:
+        config = Properties()
+        script_directory = os.path.dirname(os.path.abspath(__file__))
+        resources_path = os.path.join(script_directory, "./resources")
+
+        # Load properties file for given lang
+        with open(os.path.join(resources_path, f"./{lang}.properties"), 'rb') as config_file:
+            config.load(config_file, encoding='UTF-8')
+
+        # Translate labels for given to_translate dictionary
+        for index, label_info in to_translate.items():
+            label = label_info.get("label")
+            to_translate[index]["label"] = config.get(label).data
+        return to_translate, []
+    except Exception as e:
+        error_message = f"Error translating labels: {e}"
+        print(error_message)
+        return to_translate, error_message

main.py

@@ -1,59 +1,108 @@
-from PIL import Image
-import torch
-import torch.nn.functional as F
-from torchvision.models.resnet import resnet50, ResNet50_Weights
-from torchvision.transforms import transforms
+from flask import Flask, request, Response, json
+from cat_detection import detect_cat
+from language_label_mapper import translate
+from validator import validate
+from flask_cors import CORS
 
-# Load the pre-trained model
-model = resnet50(weights=ResNet50_Weights.DEFAULT)
-
-model.eval()
-
-# Define the image transformations
-preprocess = transforms.Compose([
-    transforms.Resize(256),
-    transforms.CenterCrop(224),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
+""" 
+    Flask Rest API application to cat recognition.
+    If request is valid then send response with results of recognition.
+    If key named 'Image' in body does not occurred then returns 400 (BAD REQUEST).
+    Otherwise returns 200 with results of recognition.
+    Format of response:
+        {
+            "lang": {users_lang},
+            "results": {
+                {filename}: {
+                    "isCat": {is_cat},
+                    "results": {
+                        "1": {result}
+                        "2": {result}
+                        "3": {result}
+                        ...
+                        "10" {result}
+                    }
+                },
+                ...
+            },
+            errors[
+                {error_message},
+                {error_message},
+                ...
+            ]
+        }
+    To see result format -> cat_detection.py
+"""
 
 
-def is_cat(image_path):
-    # Open the image
-    img = Image.open(image_path)
+# Define flask app
+app = Flask(__name__)
+app.secret_key = 'secret_key'
+CORS(app)
 
-    # Preprocess the image
-    img_t = preprocess(img)
-    batch_t = torch.unsqueeze(img_t, 0)
+# Available cats
+list_of_labels = [
+    'lynx',
+    'lion',
+    'tiger',
+    'cheetah',
+    'leopard',
+    'jaguar',
+    'tabby',
+    'Egyptian_cat',
+    'cougar',
+    'Persian_cat',
+    'Siamese_cat',
+    'snow_leopard',
+    'tiger_cat'
+]
 
-    # Make the prediction
-    out = model(batch_t)
-
-    # Apply softmax to get probabilities
-    probabilities = F.softmax(out, dim=1)
-
-    # Get the maximum predicted class and its probability
-    max_prob, max_class = torch.max(probabilities, dim=1)
-    max_prob = max_prob.item()
-    max_class = max_class.item()
-
-    # Check if the maximum predicted class is within the range 281-285
-    if 281 <= max_class <= 285:
-        return max_class, max_prob
-    else:
-        return max_class, None
+# Available languages
+languages = {'pl', 'en'}
 
 
-image_path = 'wolf.jpg'
-max_class, max_prob = is_cat(image_path)
-translator = {
-    281: "tabby cat",
-    282: "tiger cat",
-    283: "persian cat",
-    284: "siamese cat",
-    285: "egyptian cat"
-}
-if max_prob is not None:
-    print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
-else:
-    print(f"The image is not recognized as a class within the range 281-285 ({max_class})")
+@app.route('/api/v1/detect-cat', methods=['POST'])
+def upload_file():
+    # Validate request
+    error_messages = validate(request)
+
+    # If any errors occurred, return 400 (BAD REQUEST)
+    if len(error_messages) > 0:
+        errors = json.dumps(
+            {
+                'errors': error_messages
+            }
+        )
+        return Response(errors, status=400, mimetype='application/json')
+
+    # Get files from request
+    files = request.files.getlist('image')
+
+    # Get user's language (Value in header 'Accept-Language'). Default value is English
+    lang = request.accept_languages.best_match(languages, default='en')
+
+    # Define JSON structure for results
+    results = {
+        'lang': lang,
+        'results': {},
+        'errors': []
+    }
+
+    # Generate results
+    for file in files:
+        predictions = detect_cat(file, list_of_labels)
+        if predictions is not None:
+            predictions, error_messages = translate(predictions, lang)
+        results['results'][file.filename] = {
+            'isCat': False if not predictions else True,
+            **({'predictions': predictions} if predictions is not None else {})
+        }
+        if len(error_messages) > 1:
+            results['errors'].append(error_messages)
+
+    # Send response with 200 (Success)
+    return Response(json.dumps(results), status=200, mimetype='application/json')
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0')

requirements.txt

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+flask==3.0.0
+numpy==1.26.3
+pillow==10.2.0
+keras==2.15.0
+jproperties==2.1.1
+tensorflow==2.15.0
+werkzeug==3.0.1
+pytest==7.4.4
+flask-cors==4.0.0

resources/en.properties

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+# EN
+lynx=lynx
+lion=lion
+tiger=tiger
+cheetah=cheetah
+leopard=leopard
+jaguar=jaguar
+tabby=tabby
+Egyptian_cat=Egyptian cat
+cougar=cougar
+Persian_cat=Persian cat
+Siamese_cat=Siamese cat
+snow_leopard=snow leopard
+tiger_cat=tiger cat

resources/pl.properties

@@ -0,0 +1,14 @@
+# PL
+lynx=ryś
+lion=lew
+tiger=tygrys
+cheetah=gepard
+leopard=lampart
+jaguar=jaguar
+tabby=kot pręgowany
+Egyptian_cat=kot egipski
+cougar=puma
+Persian_cat=kot perski
+Siamese_cat=kot syjamski
+snow_leopard=lampart śnieżny
+tiger_cat=kot tygrysi

scripts/build_image.bat

@@ -0,0 +1,24 @@
+@echo off
+chcp 65001 >nul
+
+docker -v >nul 2>&1
+if %errorlevel% neq 0 (
+    echo Docker is not installed.
+    exit /b 1
+)
+
+docker info >nul 2>&1
+if %errorlevel% neq 0 (
+    echo Docker engine is not running.
+    exit /b 1
+)
+
+cd %~dp0
+echo Building docker image...
+docker-compose -f ../docker/docker-compose.yml build
+if %errorlevel% neq 0 (
+    echo Building docker image failed.
+    exit /b 1
+)
+
+echo The image was built successfully.

scripts/build_image.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+export LC_ALL=C.UTF-8
+export LANG=C.UTF-8
+
+docker -v > /dev/null 2>&1
+if [ $? -ne 0 ]; then
+    echo -e "\033[31mDocker is not installed.\033[0m"
+    exit 1
+fi
+
+docker info > /dev/null 2>&1
+if [ $? -ne 0 ]; then
+    echo -e "\033[31mDocker engine is not running.\033[0m"
+    exit 1
+fi
+
+cd ../docker
+echo -e "\033[32mBuilding docker image...\033[0m"
+docker-compose build cat-detection
+
+if [ $? -ne 0 ]; then
+    echo -e "\033[31mBuilding docker image failed.\033[0m"
+    exit 1
+fi
+
+echo -e "\033[32mThe image was built successfully.\033[0m"

scripts/run_tests.bat

@@ -0,0 +1,12 @@
+@echo off
+echo Running unit tests.
+
+cd %~dp0
+pytest ../tests
+
+if %ERRORLEVEL% equ 0 (
+    echo Tests passed successfully.
+) else (
+Tests failed.
+    echo Tests failed.
+)

scripts/start.bat

@@ -0,0 +1,9 @@
+@echo off
+chcp 65001 >nul
+
+cd %~dp0
+docker compose -f ../docker/docker-compose.yml up cat-detection -d
+if %errorlevel% neq 0 (
+    echo Starting docker container failed.
+    exit /b 1
+)

scripts/start.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+export LC_ALL=C.UTF-8
+export LANG=C.UTF-8
+
+docker-compose -f ../docker/docker-compose.yml up cat-detection -d
+
+if [ $? -ne 0 ]; then
+    echo -e "\033[31mStarting docker container failed.\033[0m"
+    exit 1
+fi

tests/test_1.py

@@ -0,0 +1,19 @@
+import os
+
+from werkzeug.datastructures import FileStorage
+
+from main import app
+
+
+def test_upload_file():
+    with app.test_client() as test_client:
+        script_directory = os.path.dirname(os.path.abspath(__file__))
+        image_path = os.path.join(script_directory, "./img/tiger_cat/cat1.jpg")
+        image = FileStorage(
+            stream=open(image_path, "rb"),
+            filename="cat1.jpg",
+            content_type="image/jpeg",
+        )
+
+        response = test_client.post('/api/v1/detect-cat', data={'image': image}, content_type='multipart/form-data')
+        assert response.status_code == 200

validator.py

@@ -0,0 +1,31 @@
+"""
+    Validation method.
+    If everything fine then returns empty list.
+    Else returns list of error messages.
+"""
+
+# Allowed extensions
+allowed_extensions = {'jpg', 'jpeg', 'png'}
+
+
+def validate(request):
+    errors = []
+    try:
+        images = request.files.getlist('image')
+
+        # Case 1 - > request has no 'Image' Key in body
+        if images is None:
+            raise KeyError("'Image' key not found in request.")
+
+        # Case 2 - > if some of the images has no filename
+        if not images or all(img.filename == '' for img in images):
+            raise ValueError("Value of 'Image' key is empty.")
+
+        # Case 3 -> if some of the images has wrong extension
+        for img in images:
+            if not img.filename.lower().endswith(('.png', '.jpg', '.jpeg')):
+                raise ValueError(f"Given file '{img.filename}' has no allowed extension. "
+                                 f"Allowed extensions: {allowed_extensions}.")
+    except Exception as e:
+        errors.append(e.args[0])
+    return errors