Creator name anzeigen, challenge löschen button

.env

@@ -0,0 +1,7 @@
+DB_ENGINE=django.db.backends.postgresql
+DB_NAME=example
+DB_USER=postgres
+DB_PASSWORD=example_password
+DB_HOST=db
+DB_PORT=5432
+

.gitignore

@@ -1,6 +1,9 @@
 __pycache__/
-worker/venv/
+venv/
 node_modules
 settings.json
 node_modules
-package-lock.json
+package-lock.json
+todo
+media
+*.log

README.md

@@ -5,17 +5,24 @@ This web application provides users with ML models and datasets, and evaluates t
 # Requirements:
 
 The only requirement needed for this project is docker, and enough local storage (~10 Gb).
+We recommend Docker Desktop for managing the images and containers. (Link: https://www.docker.com/products/docker-desktop/)
 
 # How to run the platform:
 
 ### building images
 
-inside the folder containing the repository, please run the following command:
+Inside the folder containing the repository, please run the following command:
 
-`docker compose up`
+`docker compose build`
 
 The first time you run it, it might take a few minutes to finish installing all dependencies and building the images.
 
+If you want to start all containers and the services attached to them (note: also starts a worker container):
+
+`docker compose up`
+
+Important: If you use Docker Desktop you need to have the app open for this to work.
+
 ### running Django migrations
 
 When connecting to the database for the first time (e.g. after the docker volumes are created for the
@@ -25,9 +32,9 @@ After running `docker ps`, copy the name of the container with image `{root_fold
 If your root folder is called "evalXai", and you used docker compose to instantiate all containers, you can use the
 same container names we used in the default commands below.
 
-`docker exec -it evalxai-backend-1 python manage.py makemigrations`
+`docker exec -it exact-backend-1 python manage.py makemigrations`
 
-`docker exec -it evalxai-backend-1 python manage.py migrate`
+`docker exec -it exact-backend-1 python manage.py migrate`
 
 Alternatively, if you have the docker desktop app, you can follow these steps:
 

admin.py

@@ -1,7 +1,5 @@
 from django.contrib import admin
 from .platform.backend.api.models import *
 
-admin.site.register(Mlmodel)
-admin.site.register(Dataset)
-admin.site.register(Xaimethod)
 admin.site.register(Score)
+admin.site.register(Challenge)

compose.yaml

@@ -9,26 +9,35 @@
 # https://github.com/docker/awesome-compose
 services:
   backend:
+    image: exact-backend
     build:
       context: ./platform/backend
     ports:
       - 8000:8000
     user: root
     volumes:
       - /var/run/docker.sock:/var/run/docker.sock
+      # - ./platform/backend/mysite/media:/app/mysite/media  
+      - ./media:/app/mysite/media
     depends_on:
-      - worker
       - db
+    env_file:
+      - .env
 
   frontend:
+    image: exact-frontend
     build:
       context: ./platform/frontend
     ports:
       - 3000:3000
 
-  worker:
-    build:
-      context: ./worker
+  # worker:
+  #   image: exact-worker
+  #   build:
+  #     context: ./worker
+  #   depends_on:
+  #     - backend
+
   # Additional configuration for the worker service
 
   # The commented out section below is an example of how to define a PostgreSQL

documentation/backend.md

@@ -63,22 +63,22 @@
 
 ### *XAI Detail*
 
-- **URL** `/api/xai/<int:challenge_id>/`
+- **URL** `api/xai/<str:challenge_id>/`
 - **Method** POST
 - **Description** 
   - Endpoint to for XAI script upload
   - Endpoint triggers creation of a worker (runs and evaluates xai method) container 
 - **Parameters**
-  - `challenge_id`: Number
+  - `challenge_id`: String
 
 
 ### *Score Detail*
 
-- **URL** `/api/score/<int:challenge_id>/`
+- **URL** `/api/score/<str:challenge_id>/`
 - **Methods** GET, POST
 - **Description** Endpoint to retrieve or update scores for a challenge.
 - **Parameters**
-  - `challenge_id`: Number
+  - `challenge_id`: String
 - **Response** 
   - GET:
     - `score`: Number
@@ -87,58 +87,148 @@
 
 ### *Dataset Detail*
 
-- **URL** `/api/dataset/<int:challenge_id>/`
+- **URL** `/api/dataset/<str:challenge_id>/`
 - **Method** GET
-- **Description** Endpoint to download dataset associated with a challenge
+- **Description** Endpoint to download dataset associated with a challenge. Redirects to the url containing the file. 
 - **Parameters**
-  - `challenge_id`: Number
+  - `challenge_id`: String
 - **Response** 
-  - Dataset file in binary format
+  - Location: The URL of the dataset file associated with the challenge ID.
 
-### *AI Detail*
+### *ML Model Detail*
 
-- **URL** `/api/mlmodel/<int:challenge_id>/`
+- **URL** `/api/mlmodel/<str:challenge_id>/`
 - **Method** GET
-- **Description** Endpoint to download trained machine learning model associated with a challenge
+- **Description** Endpoint to download trained machine learning model associated with a challenge. Redirects to the url containing the file. 
 - **Parameters**
-  - `challenge_id`: Number
+  - `challenge_id`: String
 - **Response** 
-  - Trained machine learning model file in binary format
+  - Location: The URL of the Mlmodel file associated with the challenge ID.
 
 ### *XAI Template*
 
-- **URL** `/api/xai_template/<int:challenge_id>/`
+- **URL** `/api/xaimethod/<str:challenge_id>/`
 - **Method** GET
-- **Description** Endpoint to download XAI template file associated with a challenge.
+- **Description** Endpoint to download XAI Method file associated with a challenge. Redirects to the url containing the file. 
 - **Parameters**
-  - `challenge_id`: Number
+  - `challenge_id`: String
 - **Response** 
-  - XAI template file in binary format
+  - Location: The URL of the XAI Method template file associated with the challenge ID.
+
+## Create Challenge
+
+- **URL:** `/api/challenge/create/`
+- **Method:** POST
+- **Description:** Endpoint for creating a new challenge. It takes a title, description and 3 files - xai method template, dataset and a machine learning model. The uploaded files are saved to Amazon S3 and their URLs are stored in the Postgresql database, where the challenge_id connects all the tables. 
+- **Parameters:**
+  - `title`: String (required) - The title of the challenge.
+  - `description`: String (required) - Description of the challenge.
+  - `xai_method`: File (required) - File containing the XAI method.
+  - `dataset`: File (required) - File containing the dataset.
+  - `mlmodel`: File (required) - File containing the ML model.
+- **Response:**
+  - Success (201): `{ "message": "Challenge created successfully" }`
+  - Error (400): `{ "errors": { "<field_name>": ["<error_message>"] } }`
+- **Sample Request:**
+  ```json
+  {
+    "title": "Challenge Title",
+    "description": "Description of the challenge",
+    "xai_method": <XAI_Method_File>,
+    "dataset": <Dataset_File>,
+    "mlmodel": <ML_Model_File>
+  }
+
+**Notes:**
+  - CSRF protection is temporarily disabled for testing.
+
+## Challenge Creation Form View
+
+- **URL:** `/challenge/form`
+- **Method:** POST
+- **Description:** Endpoint for submitting a challenge form. It does the same thing as /api/challenge/create/ but provides a visual interface in the backend for sending the POST request. It takes a title, description and 3 files - xai method template, dataset and machine learning model. The uploaded files are saved to Amazon S3 and their URLs are stored in the Postgresql database, where the challenge_id connects all the tables. 
+- **Parameters:**
+  - `title`: String (required) - The title of the challenge.
+  - `description`: String (required) - Description of the challenge.
+  - `xai_method`: File (required) - File containing the XAI method.
+  - `dataset`: File (required) - File containing the dataset.
+  - `mlmodel`: File (required) - File containing the ML model.
+- **Response:**
+  - Redirects to the success page upon successful submission.
+- **Sample Request:**
+  ```json
+  {
+    "title": "Challenge Title",
+    "description": "Description of the challenge",
+    "xai_method": <XAI_Method_File>,
+    "dataset": <Dataset_File>,
+    "mlmodel": <ML_Model_File>
+  }
+  ```
+- **Notes:**
+  - CSRF protection is temporarily disabled for testing.
+
+## Get Challenge
+
+- **URL:** `/challenge/<str:challenge_id>/`
+- **Method:** GET
+- **Description:** Endpoint for retrieving challenge details by ID of the challenge. Responds with a json with all the important information about the challenge.
+- **Parameters:**
+  - `challenge_id`: String (required) - ID of the challenge to retrieve.
+- **Response:**
+  - JSON object containing challenge details:
+    - `challenge_id`: String - ID of the challenge.
+    - `title`: String - Title of the challenge.
+    - `description`: String - Description of the challenge.
+    - `created_at`: String (DateTime) - Date and time when the challenge was created.
+    - `xai_method_url`: String (Optional) - URL of the XAI method associated with the challenge.
+    - `dataset_url`: String (Optional) - URL of the dataset associated with the challenge.
+    - `mlmodel_url`: String (Optional) - URL of the ML model associated with the challenge.
+- **Sample Response:**
+  ```json
+  {
+    "challenge_id": "123abc",
+    "title": "Challenge Title",
+    "description": "Description of the challenge",
+    "created_at": "2024-05-30T12:00:00Z",
+    "xai_method_url": "https://example.com/xai_method",
+    "dataset_url": "https://example.com/dataset",
+    "mlmodel_url": "https://example.com/mlmodel"
+  }
+  ```
+- **Notes:**
+  - If the challenge with the provided ID is not found, an error message is returned.
 
 ## *Data Model*
 
 ### *XAI Method Model*
-- **Description** Model representing the XAI  method associated with a challenge
-- **Fields**
-  - `challenge_id`: IntegerField (default: 1)
-  - `xai_method`: JSONField (not final type, TODO)
-
-
-
-### Score Model
-- **Description** Model representing the score associated with a challenge 
-- **Fields**
-  - `challenge_id`: IntegerField (default: 1)
+- **Description:** Model representing the XAI method associated with a challenge.
+- **Fields:**
+  - `challenge_id`: CharField (max_length=100, unique=True)
+  - `xai_method_url`: URLField (default='')
+
+### *Score Model*
+- **Description:** Model representing the score associated with a challenge.
+- **Fields:**
+  - `challenge_id`: CharField (max_length=100, unique=True)
   - `score`: FloatField
 
-### Dataset Model
-- **Description** Model representing the dataset associated with a challenge
-- **Fields**
-  - `challenge_id`: IntegerField (default: 1)
-  - `dataset`: JSONField (not final type)
-
-### ML Model
-- **Description** Model representing the machine learning model associated with a challenge
-- **Fields**
-  - `challenge_id`: IntegerField (default: 1)
-  - `model`: JSONField (not final type)
+### *Dataset Model*
+- **Description:** Model representing the dataset associated with a challenge.
+- **Fields:**
+  - `challenge_id`: CharField (max_length=100, unique=True)
+  - `dataset_url`: URLField (default='')
+
+### *ML Model*
+- **Description:** Model representing the machine learning model associated with a challenge.
+- **Fields:**
+  - `challenge_id`: CharField (max_length=100, unique=True)
+  - `model_url`: URLField (default='')
+
+### *Challenge Model*
+- **Description:** Model representing a challenge.
+- **Fields:**
+  - `challenge_id`: CharField (max_length=100, unique=True)
+  - `title`: CharField (max_length=100)
+  - `description`: TextField
+  - `created_at`: DateTimeField (auto_now_add=True)