Group Thoughtful: Close issue#7

.github/workflows/docker-action.yml

@@ -0,0 +1,54 @@
+name: Docker-Action
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  build-test:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Step 1: Checkout the code
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      # Step 2: Log in to Docker Hub (optional, if pushing images)
+      # - name: Log in to Docker Hub
+      #   uses: docker/login-action@v2
+      #   with:
+      #     username: ${{ secrets.DOCKER_USERNAME }}
+      #     password: ${{ secrets.DOCKER_PASSWORD }}
+
+      # Step 3: Build the Docker image
+      - name: Build Docker image
+        run: |
+          docker build -t thoughtful-app .
+
+      # Step 4: Run the Docker container
+      - name: Run Docker container
+        run: |
+          docker run -d --name thoughtful-app-container thoughtful-app
+
+      # Step 5: Run tests inside the container
+      # - name: Run tests
+      #   run: |
+      #     docker exec thoughtful-app-container python /app/clients/service/model.py
+      # Step 5: Run tests inside the container
+      - name: Run tests
+        run: |
+          docker exec thoughtful-app-container python /app/tests/test.py
+
+      # Step 6: Clean up
+      - name: Stop and remove container
+        run: |
+          docker stop thoughtful-app-container
+          docker rm thoughtful-app-container
+
+      - name: Remove Docker image
+        run: |
+          docker rmi thoughtful-app

.github/workflows/pylint-action.yml

@@ -0,0 +1,25 @@
+name: Pylint-Action
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pylint
+          pip install -r requirements.txt
+      - name: Analysing the code with pylint
+        run: |
+          echo "::log::Pylint processing"
+          pylint --disable=C,R,W app/*.py

.gitignore

@@ -2,3 +2,21 @@
 .idea
 __pycache__
 .DS_Store
+
+# Ignore virtual environment folder
+env/
+venv/
+
+# Ignore Python cache files
+__pycache__/
+*.pyc
+*.pyo
+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# Ignore VS Code settings
+.vscode/
+
+# Ignore shell Scripts
+*.sh

Dockerfile

@@ -0,0 +1,15 @@
+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run tests by default
+# CMD ["python", "/app/clients/service/model.py"]
+CMD ["sleep","1h"]

README.md

@@ -3,3 +3,102 @@ This will contain the model used for the project that based on the input informa
 The model works off of dummy data of several combinations of clients alongside the interventions chosen for them as well as their success rate at finding a job afterward. The model will be updated by the case workers by inputing new data for clients with their updated outcome information, and it can be updated on a daily, weekly, or monthly basis.
 
 This also has an API file to interact with the front end, and logic in order to process the interventions coming from the front end. This includes functions to clean data, create a matrix of all possible combinations in order to get the ones with the highest increase of success, and output the results in a way the front end can interact with.
+
+## Run the FastAPI project
+
+- Clone the git repo
+
+```bash
+# clone the project from git repo
+git clone https://github.com/JiayangLJY/CommonAssessmentTool-Group-Thoughtful.git
+```
+
+
+
+* Prepare the virtual environment
+
+```bash
+# cd to the project root dir
+cd CommonAssessmentTool-Group-Thoughtful
+
+# create a virtual env for the current project
+python3 -m venv venv
+
+# activate the virtual env
+source venv/bin/activate
+
+# install denpendency packages
+pip install -r requirements.txt
+
+# export python path
+export PYTHONPATH=$(pwd):$PYTHONPATH
+```
+
+> - to deactivate the current virtual env:
+>
+>   ```bash
+>   deactivate
+>   ```
+
+
+
+- Run Fastapi application
+
+```bash
+# cd to the {project}/app dir
+cd app
+
+# run project in dev mode
+fastapi dev main.py 
+```
+
+- Then open the url `http://127.0.0.1:8000/docs` in the browser to view the doc page of the current running project
+
+
+
+## Sprint 1
+
+4 tasks have been assigned to each of the group member: 
+
+1. Configurable machine learning model
+
+- A config file will be included to configure the type and hyperparameter for ML model
+- Create an abstraction layer for training and evaluation
+
+2. Maintenance on form data naming, ordering and converting
+
+- The naming and order of the form data will be uniformly maintained in the schema.py
+- Text conversion will be encapsulated within the data model
+
+3. Unit tests for server-side data processing steps
+
+- Test model selection logic in `/predict` endpoint
+- Test data processing logic of `interpret_and_calculate()`
+- Test frontend validation and form submission
+- Validate the input data
+
+4. Functional test for front-back end interaction
+
+- Update the frontend to support dynamic model selection and hyperparameter configuration
+- Conduct comprehensive integration tests to ensure the new features work correctly across the API and frontend
+
+
+
+## Sprint 2
+
+- Environments of backend and front end projects were successfully set up and both projects are now runnable in our local machines
+- Intergrated test was perfromed on the `clients/predictions` API to make sure the API is working
+- `field_serializer` from Pydantic package was used to convert string type into numerical types
+- We are investigating FastAPI docs and finding that we can use environment variables to configure some parameters in code
+- Primary study showed that we need to retrieve some intermediate variables to suit our code with test
+
+
+
+## Sprint 3
+
+- Different machine learning models, including LinearRegression, GradientBoostingRegressor and SVR were trained and tested to expand the choice in prediction.
+- The task of data type convertion was refactored to be conducted by the `PredictionInput` class.
+- Order of features (columns from the frontend form) was configured in `.env` file.
+- Unit tests were added to make sure the code works during the refactoring work.
+- Intergrated test was perfromed on the `clients/predictions` API to make sure the API is working.
+

app/clients/.env

@@ -0,0 +1,10 @@
+FEATURE_COLS_IN_SEQ=["age", "gender", "work_experience", "canada_workex", "dep_num", "canada_born", "citizen_status", "level_of_schooling", "fluent_english", "reading_english_scale", "speaking_english_scale",  "writing_english_scale", "numeracy_scale", "computer_scale", "transportation_bool", "caregiver_bool", "housing", "income_source", "felony_bool", "attending_school", "currently_employed", "substance_use", "time_unemployed", "need_mental_health_support_bool"]
+#FEATURE_COLS_IN_SEQ=["age", "gender", "canada_workex", "work_experience", "dep_num", "canada_born", "citizen_status", "level_of_schooling", "fluent_english", "reading_english_scale", "speaking_english_scale",  "writing_english_scale", "numeracy_scale", "computer_scale", "transportation_bool", "caregiver_bool", "housing", "income_source", "felony_bool", "attending_school", "currently_employed", "substance_use", "time_unemployed", "need_mental_health_support_bool"]
+
+
+# Options: RandomForestRegressor, LinearRegression, GradientBoostingRegressor, SVR
+MODEL_TYPE=SVR
+MODEL_OUTPUT_NAME=svr_model.pkl
+
+#MODEL_TYPE=RandomForestRegressor
+#MODEL_OUTPUT_NAME=random_forest_model.pkl

app/clients/router.py

@@ -1,15 +1,13 @@
 from fastapi import APIRouter
-from fastapi.responses import HTMLResponse
-
 from app.clients.service.logic import interpret_and_calculate
 from app.clients.schema import PredictionInput
 
+
 router = APIRouter(prefix="/clients", tags=["clients"])
 
+
 @router.post("/predictions")
 async def predict(data: PredictionInput):
     print("HERE")
-    print(data.model_dump())
-    return interpret_and_calculate(data.model_dump())
-
-
+    print(data.model_dump(by_alias=True))
+    return interpret_and_calculate(data.model_dump(by_alias=True))