add CORS

readme quick fix
Zaktualizuj 'README.md'
2024-01-21 11:27:01 +01:00 · 2024-01-20 18:36:37 +01:00 · 2024-01-20 18:11:42 +01:00 · 2024-01-20 18:09:34 +01:00 · 2024-01-20 18:09:04 +01:00 · 2024-01-20 18:06:54 +01:00
58 changed files with 433 additions and 4646 deletions
--- a/.idea/.gitignore
+++ b/.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
--- a/.idea/misc.xml
+++ b/.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>
--- a/Frontend/CatApp/.eslintrc.cjs
+++ b/Frontend/CatApp/.eslintrc.cjs
@ -1,18 +0,0 @@
 module.exports = {
  root: true,
  env: { browser: true, es2020: true },
  extends: [
    'eslint:recommended',
    'plugin:@typescript-eslint/recommended',
    'plugin:react-hooks/recommended',
  ],
  ignorePatterns: ['dist', '.eslintrc.cjs'],
  parser: '@typescript-eslint/parser',
  plugins: ['react-refresh'],
  rules: {
    'react-refresh/only-export-components': [
      'warn',
      { allowConstantExport: true },
    ],
  },
 }
--- a/Frontend/CatApp/.gitignore
+++ b/Frontend/CatApp/.gitignore
@ -1,24 +0,0 @@
 # Logs
 logs
 *.log
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
 pnpm-debug.log*
 lerna-debug.log*
 node_modules
 dist
 dist-ssr
 *.local
 # Editor directories and files
 .vscode/*
 !.vscode/extensions.json
 .idea
 .DS_Store
 *.suo
 *.ntvs*
 *.njsproj
 *.sln
 *.sw?
--- a/Frontend/CatApp/index.html
+++ b/Frontend/CatApp/index.html
@ -1,13 +0,0 @@
 <!doctype html>
 <html lang="en">
  <head>
    <meta charset="UTF-8" />
    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>CatApp</title>
  </head>
  <body>
    <div id="root"></div>
    <script type="module" src="/src/main.tsx"></script>
  </body>
 </html>
--- a/Frontend/CatApp/package-lock.json
+++ b/Frontend/CatApp/package-lock.json
--- a/Frontend/CatApp/package.json
+++ b/Frontend/CatApp/package.json
@ -1,31 +0,0 @@
 {
  "name": "catapp",
  "private": true,
  "version": "0.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "build": "tsc && vite build",
    "lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0",
    "preview": "vite preview"
  },
  "dependencies": {
    "react": "^18.2.0",
    "react-dom": "^18.2.0"
  },
  "devDependencies": {
    "@types/react": "^18.2.43",
    "@types/react-dom": "^18.2.17",
    "@typescript-eslint/eslint-plugin": "^6.14.0",
    "@typescript-eslint/parser": "^6.14.0",
    "@vitejs/plugin-react": "^4.2.1",
    "autoprefixer": "^10.4.16",
    "eslint": "^8.55.0",
    "eslint-plugin-react-hooks": "^4.6.0",
    "eslint-plugin-react-refresh": "^0.4.5",
    "postcss": "^8.4.33",
    "tailwindcss": "^3.4.1",
    "typescript": "^5.2.2",
    "vite": "^5.0.8"
  }
 }
--- a/Frontend/CatApp/postcss.config.js
+++ b/Frontend/CatApp/postcss.config.js
@ -1,6 +0,0 @@
 export default {
  plugins: {
    tailwindcss: {},
    autoprefixer: {},
  },
 }
--- a/Frontend/CatApp/src/App.tsx
+++ b/Frontend/CatApp/src/App.tsx
@ -1,13 +0,0 @@
 import Detector from "./components/Detector";
 import Nav from "./components/Nav";
 export default function App() {
  return (
    <>
      <Nav/>
      <Detector/>
    </>
  )
 }
--- a/Frontend/CatApp/src/components/Detector.tsx
+++ b/Frontend/CatApp/src/components/Detector.tsx
@ -1,121 +0,0 @@
 import React, { ChangeEvent, useState } from 'react';
 const Detector = () => {
  const [file, setFile] = useState<File | undefined>();
  const [preview, setPreview] = useState<string>('https://mly5gz70zztl.i.optimole.com/cb:w3s1.35324/w:auto/h:auto/q:mauto/f:best/https://www.lifewithcatman.com/wp-content/uploads/2019/03/maine-coon-cat-photography-robert-sijka-64-57ad8f2c0277c__880.jpg');
  const [results, setResults] = useState<any[]>([]);
  const [loading, setLoading] = useState<boolean>(false);
  const handleFileChange = (e: ChangeEvent<HTMLInputElement>) => {
    if (e.target.files) {
      const selectedFile = e.target.files[0];
      setFile(selectedFile);
      // Generate preview for the selected file
      const reader = new FileReader();
      reader.onloadend = () => {
        setPreview(reader.result as string);
      };
      reader.readAsDataURL(selectedFile);
    }
  };
  const handleSubmit = async () => {
    try {
      if (file) {
        setLoading(true);
        const formData = new FormData();
        formData.append('image', file);
        const response = await fetch('http://127.0.0.1:5000/api/v1/detect-cat', {
          method: 'POST',
          body: formData,
        });
        if (response.ok) {
          const resultFromServer = await response.json();
          // Update results list with the new result
          setResults((prevResults) => [...prevResults, { file, ...resultFromServer }]);
        } else {
          console.error('Błąd serwera:', response.statusText);
        }
      } else {
        console.error('Brak pliku do wysłania.');
        alert('Choose a file before sending')
      }
    } catch (error) {
      console.error('Błąd:', error);
    } finally {
      setLoading(false);
    }
  };
  return (
  <>
    <div className="flex items-center justify-center flex-wrap">
      <div className="w-full">
        <div className="w-5/6 sm:w-[420px] my-6 m-auto border-[1px] border-black p-4 rounded-2xl flex flex-wrap justify-center items-center">
          {preview && (
            <div className="flex justify-center ">
              <img className='rounded-full border-orange-500 border-2' src={preview} alt="Podgląd" style={{ maxWidth: '100%', maxHeight: '200px' }} />
            </div>
          )}
          <input className='' type="file" onChange={handleFileChange} />
          <button
            className='px-4 p-1 lg:mx-2 border-[1px] text-orange-500 border-orange-500 rounded-lg shadow-lg hover:scale-125 hover:text-white hover:bg-orange-500 duration-500 font-bold mt-4 sm:mt-0'
            onClick={handleSubmit}
            disabled={loading}
          >
            IS IT?
          </button>        
        </div>        
      </div>
      {loading && <div className='w-full text-center text-blue-200 uppercase font-semibold'>  Sending file...</div>}
      {results.length > 0 && (
        <div>  
          {results.map((result, index) => (
            <div className='border-[1px] border-gray-500 m-4 rounded-lg p-4' key={index}>
              <h3 className='text-center font-DM text-orange-500 font-semibold'><span>{index + 1}. </span>CAT RESULT</h3>
              <table>
                <thead className='font-DM'>
                  <tr>
                    <th>Image:</th>
                    <th>Results:</th>
                  </tr>
                </thead>
                <tbody>
                  {result.results && Object.keys(result.results).map((filename) => (
                    <tr key={filename}>
                      <td>
                        <img className='rounded-xl m-2 border-orange-500 border-[1px]' src={URL.createObjectURL(result.file)} alt="Podgląd" style={{ maxWidth: '100%', maxHeight: '100px' }} />
                      </td>
                      <td>
                        <p>
                          <span>Result:</span><span className='text-orange-500 font-DM'> {result.results[filename].isCat ? 'This is a cat' : 'Not a cat'}</span> 
                        </p>
                        <ul>
                          {result.results[filename].predictions &&
                            Object.keys(result.results[filename].predictions).map((key) => (
                              <li key={key}>
                                {result.results[filename].predictions[key].score.toFixed(2) * 100}<span>% - </span>
                                {result.results[filename].predictions[key].label}
                              </li>
                            ))}
                        </ul>
                      </td>
                    </tr>
                  ))}
                </tbody>
              </table>
            </div>
          ))}
        </div>
      )}
    </div>
  </>
  );
 };
 export default Detector;
--- a/Frontend/CatApp/src/components/Nav.tsx
+++ b/Frontend/CatApp/src/components/Nav.tsx
@ -1,11 +0,0 @@
 const Nav = () => {
  return (
    <div className='w-full h-32 px-8 md:px-20 shadow-2xl flex items-center justify-center'>
      <img className="h-32 w-32 p-4" src="https://github.com/UniSzef/test/blob/main/logo.png?raw=true"></img>
      <div className="md:text-4xl text-2xl font-DM font-bold">Be sure that your <span className="text-orange-400">cat</span> is real!</div>
    </div>
  )
 }
 export default Nav
--- a/Frontend/CatApp/src/components/Waves.tsx
+++ b/Frontend/CatApp/src/components/Waves.tsx
@ -1,20 +0,0 @@
 import React from 'react'
 const Waves = () => {
  return (
    <>
    <svg className="absolute -z-10" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1440 320">
          <path fill="#edb593" fill-opacity="1" d="M0,288L48,250.7C96,213,192,139,288,117.3C384,96,480,128,576,149.3C672,171,768,181,864,165.3C960,149,1056,107,1152,112C1248,117,1344,171,1392,197.3L1440,224L1440,0L1392,0C1344,0,1248,0,1152,0C1056,0,960,0,864,0C768,0,672,0,576,0C480,0,384,0,288,0C192,0,96,0,48,0L0,0Z"></path>
      </svg>
      <svg className="absolute -z-10 translate-y-[500px]" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1440 320">
          <path fill="#edb593" fill-opacity="1" d="M0,192L40,202.7C80,213,160,235,240,229.3C320,224,400,192,480,160C560,128,640,96,720,101.3C800,107,880,149,960,186.7C1040,224,1120,256,1200,261.3C1280,267,1360,245,1400,234.7L1440,224L1440,320L1400,320C1360,320,1280,320,1200,320C1120,320,1040,320,960,320C880,320,800,320,720,320C640,320,560,320,480,320C400,320,320,320,240,320C160,320,80,320,40,320L0,320Z"></path>
      </svg>
      <div className="h-[400px] w-full -z-10 absolute bg-waves translate-y-[800px]"></div>
      <svg className="absolute -z-10 translate-y-[1200px]" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1440 320">
        <path fill="#edb593" fill-opacity="1" d="M0,288L26.7,261.3C53.3,235,107,181,160,181.3C213.3,181,267,235,320,240C373.3,245,427,203,480,170.7C533.3,139,587,117,640,117.3C693.3,117,747,139,800,154.7C853.3,171,907,181,960,202.7C1013.3,224,1067,256,1120,256C1173.3,256,1227,224,1280,224C1333.3,224,1387,256,1413,272L1440,288L1440,0L1413.3,0C1386.7,0,1333,0,1280,0C1226.7,0,1173,0,1120,0C1066.7,0,1013,0,960,0C906.7,0,853,0,800,0C746.7,0,693,0,640,0C586.7,0,533,0,480,0C426.7,0,373,0,320,0C266.7,0,213,0,160,0C106.7,0,53,0,27,0L0,0Z"></path>
      </svg>
    </>
  )
 }
 export default Waves
--- a/Frontend/CatApp/src/images/logo.png
+++ b/Frontend/CatApp/src/images/logo.png
--- a/Frontend/CatApp/src/index.css
+++ b/Frontend/CatApp/src/index.css
@ -1,5 +0,0 @@
@import url('https://fonts.googleapis.com/css2?family=DM+Serif+Display&display=swap');
@tailwind base;
@tailwind components;
@tailwind utilities;
--- a/Frontend/CatApp/src/main.tsx
+++ b/Frontend/CatApp/src/main.tsx
@ -1,10 +0,0 @@
 import React from 'react'
 import ReactDOM from 'react-dom/client'
 import App from './App.tsx'
 import './index.css'
 ReactDOM.createRoot(document.getElementById('root')!).render(
  <React.StrictMode>
    <App />
  </React.StrictMode>,
 )
--- a/Frontend/CatApp/src/vite-env.d.ts
+++ b/Frontend/CatApp/src/vite-env.d.ts
@ -1 +0,0 @@
 /// <reference types="vite/client" />
--- a/Frontend/CatApp/tailwind.config.js
+++ b/Frontend/CatApp/tailwind.config.js
@ -1,18 +0,0 @@
 /** @type {import('tailwindcss').Config} */
 export default {
  content: [
    "./index.html",
    "./src/**/*.{js,ts,jsx,tsx}",
  ],
  theme: {
    extend: {
      fontFamily: {
        DM: ['DM Serif Display']
      },
      colors: {
        waves: '#edb593',
      },
    },
  },
  plugins: [],
 }
--- a/Frontend/CatApp/tsconfig.json
+++ b/Frontend/CatApp/tsconfig.json
@ -1,25 +0,0 @@
 {
  "compilerOptions": {
    "target": "ES2020",
    "useDefineForClassFields": true,
    "lib": ["ES2020", "DOM", "DOM.Iterable"],
    "module": "ESNext",
    "skipLibCheck": true,
    /* Bundler mode */
    "moduleResolution": "bundler",
    "allowImportingTsExtensions": true,
    "resolveJsonModule": true,
    "isolatedModules": true,
    "noEmit": true,
    "jsx": "react-jsx",
    /* Linting */
    "strict": true,
    "noUnusedLocals": true,
    "noUnusedParameters": true,
    "noFallthroughCasesInSwitch": true
  },
  "include": ["src"],
  "references": [{ "path": "./tsconfig.node.json" }]
 }
--- a/Frontend/CatApp/tsconfig.node.json
+++ b/Frontend/CatApp/tsconfig.node.json
@ -1,10 +0,0 @@
 {
  "compilerOptions": {
    "composite": true,
    "skipLibCheck": true,
    "module": "ESNext",
    "moduleResolution": "bundler",
    "allowSyntheticDefaultImports": true
  },
  "include": ["vite.config.ts"]
 }
--- a/Frontend/CatApp/vite.config.ts
+++ b/Frontend/CatApp/vite.config.ts
@ -1,7 +0,0 @@
 import { defineConfig } from 'vite'
 import react from '@vitejs/plugin-react'
 // https://vitejs.dev/config/
 export default defineConfig({
  plugins: [react()],
 })
--- a/README.md
+++ b/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!
--- a/pycache/cat_detection.cpython-39.pyc
+++ b/pycache/cat_detection.cpython-39.pyc
--- a/cat.png
+++ b/cat.png
--- a/cat_detection.py
+++ b/cat_detection.py
@ -1,48 +1,58 @@
 from io import BytesIO
-import torch
+import numpy as np
 import torch.nn.functional as F
 from PIL import Image
-from torchvision.models.resnet import resnet50, ResNet50_Weights
+from keras.src.applications.resnet import preprocess_input, decode_predictions
-from torchvision.transforms import transforms
+from keras.applications.resnet import ResNet50
 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]),
 ])
-def is_cat(image):
+"""
    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))
-        # Preprocess the image
+        img_array = np.array(img)
-        img_t = preprocess(img)
+        img_array = np.expand_dims(img_array, axis=0)
-        batch_t = torch.unsqueeze(img_t, 0)
+        img_array = preprocess_input(img_array)
-
+        return img_array
        # 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
    except Exception as e:
-        print("Error while processing the image:", 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)
--- a/docker/.dockerignore
+++ b/docker/.dockerignore
@ -0,0 +1,6 @@
 *.md
 /venv
 .git
 __pycache__
 .pytest_cache
 /tests
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@ -0,0 +1,11 @@
 FROM python:3.11
 WORKDIR /app
 COPY . /app
 RUN pip install --no-cache-dir -r requirements.txt
 EXPOSE 5000
 CMD ["python", "main.py"]
--- a/docker/docker-compose.yml
+++ b/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"
--- a/docs.md
+++ b/docs.md
@ -1,9 +0,0 @@
 # Api
 Port -> 5000
 endpoint -> /detect-cat
 Key -> 'Image'
 Value -> {UPLOADED_FILE}
--- a/docs/basics.md
+++ b/docs/basics.md
--- a/docs/docs.md
+++ b/docs/docs.md
@ -0,0 +1,53 @@
 # 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
 ```
--- a/language_label_mapper.py
+++ b/language_label_mapper.py
@ -0,0 +1,30 @@
 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
--- a/main.py
+++ b/main.py
@ -1,38 +1,108 @@
-from flask import Flask, request, jsonify, session
+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
 """ 
    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
 """
 from cat_detection import is_cat
 # Define flask app
 app = Flask(__name__)
 app.secret_key = 'secret_key'
 CORS(app)
 # Available cats
 list_of_labels = [
    'lynx',
    'lion',
    'tiger',
    'cheetah',
    'leopard',
    'jaguar',
    'tabby',
    'Egyptian_cat',
    'cougar',
    'Persian_cat',
    'Siamese_cat',
    'snow_leopard',
    'tiger_cat'
 ]
 # Available languages
 languages = {'pl', 'en'}
-@app.route('/detect-cat', methods=['POST'])
+@app.route('/api/v1/detect-cat', methods=['POST'])
 def upload_file():
-    # 'Key' in body should be named as 'image'. Type should be 'File' and in 'Value' we should upload image from disc.
+    # Validate request
-    file = request.files['image']
+    error_messages = validate(request)
    if file.filename == '':
        return jsonify({'error': "File name is empty. Please name a file."}), 400
    max_class, max_prob = is_cat(file)
-    # Save result in session
+    # If any errors occurred, return 400 (BAD REQUEST)
-    session['result'] = max_class, max_prob
+    if len(error_messages) > 0:
        errors = json.dumps(
            {
                'errors': error_messages
            }
        )
        return Response(errors, status=400, mimetype='application/json')
-    # Tworzenie komunikatu na podstawie wyniku analizy zdjęcia
+    # Get files from request
-    translator = {
+    files = request.files.getlist('image')
-        281: "tabby cat",
+
-        282: "tiger cat",
+    # Get user's language (Value in header 'Accept-Language'). Default value is English
-        283: "persian cat",
+    lang = request.accept_languages.best_match(languages, default='en')
-        284: "siamese cat",
+
-        285: "egyptian cat"
+    # Define JSON structure for results
    results = {
        'lang': lang,
        'results': {},
        'errors': []
    }
    if max_prob is not None:
        result = f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%"
    else:
        result = f"The image is not recognized as a class within the range 281-285 ({max_class})"
-    return jsonify({'result': result}), 200
+    # 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(debug=True)
+    app.run(host='0.0.0.0')
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,9 @@
 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
--- a/resources/en.properties
+++ b/resources/en.properties
@ -0,0 +1,14 @@
 # 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
--- a/resources/pl.properties
+++ b/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
--- a/scripts/build_image.bat
+++ b/scripts/build_image.bat
@ -0,0 +1,24 @@
@echo off
 chcp 65001 >nul
 docker -v >nul 2>&1
 if %errorlevel% neq 0 (
    echo [31mDocker is not installed.[0m
    exit /b 1
 )
 docker info >nul 2>&1
 if %errorlevel% neq 0 (
    echo [31mDocker engine is not running.[0m
    exit /b 1
 )
 cd %~dp0
 echo [32mBuilding docker image...[0m
 docker-compose -f ../docker/docker-compose.yml build
 if %errorlevel% neq 0 (
    echo [31mBuilding docker image failed.[0m
    exit /b 1
 )
 echo [32mThe image was built successfully.[0m
--- a/scripts/build_image.sh
+++ b/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"
--- a/scripts/run_tests.bat
+++ b/scripts/run_tests.bat
@ -0,0 +1,12 @@
@echo off
 echo [32mRunning unit tests.[0m
 cd %~dp0
 pytest ../tests
 if %ERRORLEVEL% equ 0 (
    echo [32mTests passed successfully.[0m
 ) else (
 Tests failed.
    echo [31mTests failed.[0m
 )
--- a/scripts/start.bat
+++ b/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 [31mStarting docker container failed.[0m
    exit /b 1
 )
--- a/scripts/start.sh
+++ b/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
--- a/test.py
+++ b/test.py
@ -1,9 +0,0 @@
 import requests
 url = 'http://127.0.0.1:5000/detect-cat'
 files = {'image': (open('cat1.jpg', 'rb'))}
 response = requests.post(url, files=files)
 print(response.text)
--- a/tests/init.py
+++ b/tests/init.py
--- a/tests/img/Egyptian_cat/Egyptian-Mau-Bronze-750.jpg
+++ b/tests/img/Egyptian_cat/Egyptian-Mau-Bronze-750.jpg
--- a/tests/img/Persian_cat/PER_Fluffy_Fancy_Antony_(5468896948).jpg
+++ b/tests/img/Persian_cat/PER_Fluffy_Fancy_Antony_(5468896948).jpg
--- a/tests/img/Siamese_cat/siamese-cat-cover.jpg
+++ b/tests/img/Siamese_cat/siamese-cat-cover.jpg
--- a/tests/img/cheetah/TheCheethcat.jpg
+++ b/tests/img/cheetah/TheCheethcat.jpg
--- a/tests/img/cougar/Cougar_at_Cougar_Mountain_Zoological_Park_2.jpg
+++ b/tests/img/cougar/Cougar_at_Cougar_Mountain_Zoological_Park_2.jpg
--- a/tests/img/jaguar/Junior-Jaguar-Belize-Zoo.jpg
+++ b/tests/img/jaguar/Junior-Jaguar-Belize-Zoo.jpg
--- a/tests/img/leopard/African_leopard_male_(cropped).jpg
+++ b/tests/img/leopard/African_leopard_male_(cropped).jpg
--- a/tests/img/lion/Lion_waiting_in_Namibia.jpg
+++ b/tests/img/lion/Lion_waiting_in_Namibia.jpg
--- a/tests/img/lynx/552661_poster.jpg
+++ b/tests/img/lynx/552661_poster.jpg
--- a/tests/img/not
+++ b/tests/img/not
--- a/tests/img/snow_lopard/Snow-Leopard.jpg
+++ b/tests/img/snow_lopard/Snow-Leopard.jpg
--- a/tests/img/tabby/Cat_November_2010-1a.jpg
+++ b/tests/img/tabby/Cat_November_2010-1a.jpg
--- a/tests/img/tiger/1000_F_88747131_OIL6X2l7Nur139dUHTdaRK51NwqW02DE.jpg
+++ b/tests/img/tiger/1000_F_88747131_OIL6X2l7Nur139dUHTdaRK51NwqW02DE.jpg
--- a/tests/img/tiger_cat/cat1.jpg
+++ b/tests/img/tiger_cat/cat1.jpg
--- a/tests/test_1.py
+++ b/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
--- a/validator.py
+++ b/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
Author	SHA1	Message	Date
Michael	7fe54f35d7	add CORS	2024-01-21 11:27:01 +01:00
Michael	bcf0d04de6	readme quick fix	2024-01-20 18:36:37 +01:00
Michał Łuczak	c1f7cd5d92	Zaktualizuj 'README.md'	2024-01-20 18:11:42 +01:00
Michał Łuczak	7ac72b91cd	Merge pull request 'Bugfixes' (#6 ) from flask-ML into dev Reviewed-on: #6	2024-01-20 18:09:34 +01:00
Michael	030d5e432e	readme guides	2024-01-20 18:09:04 +01:00
Michael	80706e2c16	quick bugfix	2024-01-20 18:06:54 +01:00
Michał Łuczak	290772dfa2	Merge pull request 'flask-ML' (#5 ) from flask-ML into dev Reviewed-on: #5	2024-01-20 17:51:32 +01:00
Michał Łuczak	e886e8af7e	Usuń '.idea/.gitignore'	2024-01-20 17:51:08 +01:00
Michał Łuczak	5f7e15dbea	Usuń '.idea/misc.xml'	2024-01-20 17:51:00 +01:00
Michael	557e16dfd6	delete polish comments	2024-01-20 17:49:04 +01:00
Michael	e0046ef98a	revert	2024-01-20 17:48:16 +01:00
Michael	9f3bbb0d87	add docker, tests, scripts, test images and bugfixes	2024-01-20 17:45:24 +01:00
Michał Łuczak	a00c20f3b1	Merge pull request 'API improvements' (#4 ) from flask-ML into dev Reviewed-on: #4	2024-01-18 23:34:15 +01:00
Michael	294fb339ed	bugfixes	2024-01-17 19:46:35 +01:00
Michael	695592b7b6	requirements.txt	2024-01-14 15:05:47 +01:00
Michael	663ef6d80d	API improvements	2024-01-14 14:53:54 +01:00