First deploy to HF Spaces

Dockerfile

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+
+
+# Use Python base image
+FROM --platform=linux/amd64 python:latest
+
+# Set working directory in the container
+WORKDIR /app
+
+# Copy requirements.txt to container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the FastAPI app files to the container
+COPY . /app
+
+# Expose port 80 for FastAPI app
+EXPOSE 7860
+
+# Command to start the FastAPI app
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE

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+MIT License
+
+Copyright (c) 2024 rishabh.io
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,11 +1,193 @@
----
-title: IrisModel
-emoji: 📊
-colorFrom: green
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# packaging-ml-model
+Learn how to package a machine learning model into a container
+
+
+
+# Steps to use the repository
+
+1. Clone the repository 
+2. Create a virtual environment ( to isolate the dependencies ) 
+3. Install the requirements with the following command: 
+
+```
+
+pip install -r requirements.txt 
+```
+
+# Build the model file 
+1. Execute the following command to build the model 
+
+```
+python model.py 
+
+```
+
+- This will build the model and serialize it into a file called 
+as model.joblib, this is what we'll load into memory when we 
+build our inference API via fastAPI 
+
+# Build a fastAPI based app
+
+- The source code for this is available in the app.py file 
+- You can check whether it's working by executing the following 
+    command: 
+
+```
+uvicorn main:app --reload
+
+```
+
+# Generate a Docker file 
+
+Generate Dockerfile in the same directory as the app and add the 
+following contents to it: 
+
+```
+
+# Use Python base image
+FROM python:3.9-slim
+
+# Set working directory in the container
+WORKDIR /app
+
+# Copy requirements.txt to container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the FastAPI app files to the container
+COPY app /app
+
+# Expose port 80 for FastAPI app
+EXPOSE 80
+
+# Command to start the FastAPI app
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
+
+```
+
+# Build the Docker Image from the instructions given in the Dockerfile 
+
+```
+docker build -t packaged-model-001 .
+```
+
+# Build the container out of the image 
+```
+docker run -p 8000:80 packaged-model-001
+```
+
+
+# Verify whether the container is running 
+
+- Use postman to call the post end-point available at localhost:8000/predict 
+
+```
+
+{
+    "sepal_length": 2,
+    "sepal_width": 3.0,
+    "petal_length": 4.0,
+    "petal_width": 1.5
+}
+
+```
+
+
+# push the image to docker registry 
+
+1. Login to Docker 
+
+```
+
+docker login
+
+```
+
+- The above command should show text like the following: 
+
+```
+Log in with your Docker ID or email address to push and pull images from Docker Hub. If you don't have a Docker ID, head over to https://hub.docker.com/ to create one.
+You can log in with your password or a Personal Access Token (PAT). Using a limited-scope PAT grants better security and is required for organizations using SSO. Learn more at https://docs.docker.com/go/access-tokens/
+
+Username: 
+```
+
+- Use the PAT as the password 
+** Note: You can also use your password but the use of PAT with minimal access is recommended. 
+
+
+# Create a repository on DockerHub 
+
+```
+1. Go to Dockerhub ( search on Google ) 
+
+2. Create an Account if not already existing
+
+3. Create a new Repository 
+
+```
+
+
+# Tag your local image 
+
+```
+
+docker tag packaged-model-001:latest riio/packaged-model-001:latest
+
+```
+
+
+## Push the image to DockerHub 
+
+```
+docker push riio/packaged-model-001:latest
+```
+
+
+## Sample Output: 
+
+```
+
+(venv) username@machine packaging-ml-model % docker push riio/packaged-model-001:latest
+The push refers to repository [docker.io/riio/packaged-model-001]
+fd749012a9d2: Pushed 
+963141bae3f4: Pushing  253.5MB
+4f83a3ffc58c: Pushed 
+7b34bc82ecfd: Pushed 
+b958f60e4e67: Pushed 
+f02ce41627b1: Pushed 
+eeac00a5e55e: Pushed 
+34e7752745be: Pushed 
+8560597d922c: Pushing  100.2MB
+```
+
+
+## Congratulations.
+
+- You just packaged your machine learning model and made it available to the world with the power of containers. 
+
+## How anybody can use your packaged model? 
+
+It's simple
+
+```
+docker pull riio/packaged-model-001:latest 
+
+```
+
+
+## How to run the container ? 
+
+```
+
+docker run -p 8000:80 riio/packaged-model-001:latest
+```
+
+
+
+# Link to access the Container: 
+
+
+- https://model-deployment-005.purplecliff-0cc0d310.centralindia.azurecontainerapps.io/predict

main.py

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+
+
+
+# app/main.py
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+from joblib import load
+import numpy as np
+from fastapi.responses import HTMLResponse
+
+
+# Define FastAPI app
+app = FastAPI()
+
+# Load the trained model
+model = load("model.joblib")
+
+# Define request body schema using Pydantic BaseModel
+class Item(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+# Define endpoint to make predictions
+@app.post("/predict")
+async def predict(item: Item):
+    # Convert input to array
+    input_data = [item.sepal_length, item.sepal_width, item.petal_length, item.petal_width]
+    input_array = np.array([input_data])
+
+    # Make prediction
+    prediction = model.predict(input_array)[0]
+
+    # Map prediction to class label
+    class_label = {0: "setosa", 1: "versicolor", 2: "virginica"}
+    predicted_class = class_label[prediction]
+
+    # Return prediction
+    return {"predicted_class": predicted_class}
+
+
+@app.get('/app', response_class=HTMLResponse)
+async def html():
+    content = open('static/index.html', 'r') 
+    return content.read() 

model.py

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+
+# app/model.py
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from joblib import dump
+
+# Load the Iris dataset
+iris = load_iris()
+X, y = iris.data, iris.target
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a Random Forest classifier
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate the model
+accuracy = model.score(X_test, y_test)
+print("Model accuracy:", accuracy)
+
+# Save the trained model as a joblib file
+dump(model, "model.joblib")

requirements.txt

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+annotated-types==0.6.0
+anyio==4.3.0
+click==8.1.7
+fastapi==0.110.3
+h11==0.14.0
+idna==3.7
+joblib==1.4.0
+numpy==1.26.4
+pydantic==2.7.1
+pydantic_core==2.18.2
+scikit-learn==1.4.2
+scipy==1.13.0
+sniffio==1.3.1
+starlette==0.37.2
+threadpoolctl==3.5.0
+typing_extensions==4.11.0
+uvicorn==0.29.0

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>Flower Predictor</title>
+<style>
+    body {
+        font-family: Arial, sans-serif;
+        margin: 0;
+        padding: 0;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        height: 100vh;
+        background-color: #f2f2f2;
+    }
+    
+    form {
+        background-color: #fff;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    }
+    
+    label {
+        display: block;
+        margin-bottom: 10px;
+    }
+    
+    input[type="text"] {
+        width: 100%;
+        padding: 10px;
+        margin-bottom: 20px;
+        border: 1px solid #ccc;
+        border-radius: 5px;
+        box-sizing: border-box;
+    }
+    
+    input[type="submit"] {
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 20px;
+        border: none;
+        border-radius: 5px;
+        cursor: pointer;
+    }
+    
+    input[type="submit"]:hover {
+        background-color: #45a049;
+    }
+</style>
+</head>
+<body>
+<div>
+    <h1>Flower Predictor</h1>
+    <p>Enter the flower measurements to predict the class of the flower.</p>
+    <p> The predicted class is: <span id="predicted_class"></span> </p>
+</div>
+<form id="flowerForm">
+    <label for="petal_length">Petal Length:</label>
+    <input type="text" id="petal_length" name="petal_length" required>
+
+    <label for="sepal_length">Sepal Length:</label>
+    <input type="text" id="sepal_length" name="sepal_length" required>
+
+    <label for="petal_width">Petal Width:</label>
+    <input type="text" id="petal_width" name="petal_width" required>
+
+    <label for="sepal_width">Sepal Width:</label>
+    <input type="text" id="sepal_width" name="sepal_width" required>
+
+    <input type="submit" value="Predict">
+</form>
+
+<script>
+    document.getElementById("flowerForm").addEventListener("submit", function(event) {
+        event.preventDefault(); // Prevent default form submission
+
+        // Prepare data for API call
+        const formData = new FormData(event.target);
+        const requestData = {};
+        formData.forEach((value, key) => {
+            requestData[key] = value;
+        });
+
+        // Make API call
+        fetch("/predict", {
+            method: "POST",
+            headers: {
+                "Content-Type": "application/json"
+            },
+            body: JSON.stringify(requestData)
+        })
+        .then(response => {
+            if (!response.ok) {
+                throw new Error("Network response was not ok");
+            }
+            return response.json();
+        })
+        .then(data => {
+            // Handle API response here
+            console.log(data);
+            document.getElementById("predicted_class").innerText = data.predicted_class;
+            // alert("Prediction: " + data.predicted_class);
+        })
+        .catch(error => {
+            console.error("Error:", error);
+            alert("An error occurred. Please try again.");
+        });
+    });
+</script>
+
+</body>
+</html>