init

.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+*.pyd
+*.git
+*myenv

.env

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+MODEL_PATH=linear_regression_model.pkl
+SCALER_PATH=scaler.pkl

.github/workflows/deploy.yml

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+name: Deploy to Heroku
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.9  # Match your app's Python version
+
+    - name: Install Dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Test Application
+      run: |
+        pytest  # Assuming you have tests configured
+
+    - name: Deploy to Heroku
+      env:
+        HEROKU_API_KEY: ${{ secrets.HEROKU_API_KEY }}
+        HEROKU_APP_NAME: ${{ secrets.HEROKU_APP_NAME }}
+      run: |
+        echo "Deploying to Heroku..."
+        heroku container:login
+        heroku container:push web --app $HEROKU_APP_NAME
+        heroku container:release web --app $HEROKU_APP_NAME

Dockerfile

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+# Use an offical python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy  the application code into the container
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8000
+
+# Ensure the application runs on Heroku's dynamic port
+CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port $PORT"]

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+import joblib
+import numpy as np
+import pandas as pd
+import logging
+from fastapi import HTTPException
+from dotenv import load_dotenv
+import os
+import uvicorn
+
+# Load the saved model and scaler
+model = None
+scaler = None
+
+# Load environment variables from .env file in the same directory
+load_dotenv()
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Serve the static files from the static folder
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+
+@app.on_event("startup")
+async def load_model():
+    global model, scaler
+    try:
+   # Use environment variables to load model and scaler paths
+        model_path = os.getenv("MODEL_PATH", "linear_regression_model.pkl")
+        scaler_path = os.getenv("SCALER_PATH", "scaler.pkl")
+
+        model = joblib.load(model_path)
+        scaler = joblib.load(scaler_path)
+        logging.info("Model and scaler have been loaded successfully.")
+    except Exception as e:
+        logging.error(f"Error loading model or scaler: {e}")
+        raise HTTPException(status_code=500, detail="Error loading model")
+
+# Define a request model for the input
+class PredictionRequest(BaseModel):
+    hours_studied: float
+
+# Home endpoint 
+@app.get("/")
+def home():
+    return FileResponse("static/index.html")
+
+# Prediction page endpoint
+@app.get("/predict")
+async def predict_page():
+    return FileResponse("static/predict.html")
+
+# Prediction endpoint
+@app.post("/predict")
+def predict(request: PredictionRequest):
+    if model is None or scaler is None:
+        logging.error("Model is not loaded.")
+        raise HTTPException(status_code=503, detail="Model not loaded, please try again later")
+    
+  # Input validation: Ensure hours_studied is a positive number
+    if request.hours_studied <= 0:
+        logging.warning("Received invalid input: Hours studied should be positive.")
+        raise HTTPException(status_code=400, detail="Hours studied should be a positive number")
+
+    hours = request.hours_studied     # Extract the hours studied from the request
+    data = pd.DataFrame([[hours]], columns=['Hours_Studied'])   # Prepare the data for prediction
+    scaled_data = scaler.transform(data) # Scale the input data
+
+    try:
+        prediction = model.predict(scaled_data)  # Make prediction using the model
+        logging.info(f"Prediction for {hours} hours: {prediction[0]}")
+    except Exception as e:
+        logging.error(f"Error during prediction: {e}")
+        raise HTTPException(status_code=500, detail="Error during prediction")
+    
+    return {"predicted_test_score": prediction[0]} # Return the predicted test score
+
+
+
+if __name__ == "__main__":
+    port = int(os.getenv("PORT", 8000))  # Use Heroku's PORT 
+    uvicorn.run(app, host="0.0.0.0", port=port)

requirements.txt

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+fastapi
+uvicorn
+joblib
+numpy
+pandas
+scikit-learn
+python-dotenv
+pytest
+httpx

static/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Model Prediction Home</title>
+    <link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+    <div class="container" id="home">
+        <h1>Welcome to Meta Brain's Prediction Model</h1>
+        <p>Find out your predicted test score based on your hours of study.</p>
+        <a href="/predict">Go to Prediction Page</a>
+    </div>
+</body>
+</html>

static/predict.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Predict Test Score</title>
+    <link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+    <div class="container" id="predict">
+        <h2>Predict Your Test Score</h2>
+        <form action="/predict" method="post" id="predict-form">
+            <label for="hours_studied">Hours Studied:</label>
+            <input type="number" id="hours_studied" name="hours_studied" required placeholder="Enter hours studied" min="0">
+            <button type="submit">Get Prediction</button>
+        </form>
+        <div id="result" class="result"></div>
+        <footer>
+            <p>&copy; 2024 Meta Brains. All rights reserved.</p>
+        </footer>
+    </div>
+    
+    <script>
+        document.getElementById("predict-form").addEventListener("submit", function(event) {
+            event.preventDefault();
+            const hoursStudied = document.getElementById("hours_studied").value;
+            fetch("/predict", {
+                method: "POST",
+                headers: {
+                    "Content-Type": "application/json"
+                },
+                body: JSON.stringify({ hours_studied: parseFloat(hoursStudied) })
+            })
+            .then(response => response.json())
+            .then(data => {
+                document.getElementById("result").innerText = `Predicted Test Score: ${data.predicted_test_score}`;
+            })
+            .catch(error => {
+                document.getElementById("result").innerText = "Error in prediction. Please try again later.";
+            });
+        });
+    </script>
+</body>
+</html>

static/style.css

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+/* style.css */
+
+body {
+    font-family: 'Arial', sans-serif;
+    background-color: #f4f7fa;
+    margin: 0;
+    padding: 0;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100vh;
+}
+
+.container {
+    width: 80%;
+    max-width: 800px;
+    margin: 20px auto;
+    padding: 20px;
+    background-color: white;
+    border-radius: 8px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+
+h1, h2 {
+    text-align: center;
+    color: #333;
+}
+
+p {
+    text-align: center;
+    color: #555;
+    font-size: 18px;
+}
+
+a {
+    display: block;
+    text-align: center;
+    margin-top: 20px;
+    padding: 10px;
+    background-color: #007bff;
+    color: white;
+    text-decoration: none;
+    border-radius: 5px;
+    font-size: 18px;
+}
+
+a:hover {
+    background-color: #0056b3;
+}
+
+form {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    margin-top: 20px;
+}
+
+label {
+    font-size: 18px;
+    margin-bottom: 10px;
+    color: #333;
+}
+
+input[type="number"] {
+    padding: 10px;
+    width: 100%;
+    max-width: 250px;
+    margin-bottom: 20px;
+    border: 2px solid #ddd;
+    border-radius: 5px;
+    font-size: 16px;
+}
+
+button {
+    padding: 10px 20px;
+    background-color: #28a745;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    font-size: 18px;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #218838;
+}
+
+h2 {
+    margin-top: 20px;
+    color: #333;
+    font-size: 24px;
+}
+
+#predicted_score {
+    font-weight: bold;
+    color: #007bff;
+}

test_api.py

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+import pytest
+from fastapi.testclient import TestClient
+from main import app  
+
+
+@pytest.fixture
+def client():
+    """Create a TestClient instance for making requests."""
+    with TestClient(app) as client:
+        yield client
+
+# Test home route
+def test_home(client):
+    response = client.get("/")
+    assert response.status_code == 200
+    assert "text/html" in response.headers["content-type"]
+    assert "Welcome to Meta Brain's Prediction Model" in response.text  # Check if the static index page content is correct
+
+
+# Test predict page route
+def test_predict_page(client):
+    response = client.get("/predict")
+    assert response.status_code == 200
+    assert "text/html" in response.headers["content-type"]
+    assert "Predict Test Score" in response.text  # Check if predict page content is present
+
+def test_predict_invalid_input(client):
+    response = client.post("/predict", json={"hours_studied": -2.0})
+    assert response.status_code == 400
+    assert "Hours studied should be a positive number" in response.json()["detail"]
+