modernize code , Updated README and requirements.txt with Streamlit a…

README.md

@@ -1,42 +1,73 @@
-# Model Deployment as API | The Iris Dataset
+# **Model Deployment as API | The Iris Dataset**
+### **Streamlit App Interface**
+![Streamlit App](imgs/img1.png)
+
+![Streamlit App](imgs/img2.png)
+
 
 Deploying a Machine Learning Model as a REST API with Flask
 
 ![Iris](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Machine+Learning+R/iris-machinelearning.png "Iris")
 
+---
+
+## **Data Set Information**
+
+This is perhaps the best-known database in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other two; the latter are NOT linearly separable from each other.
+
+**Predicted attribute**: Class of iris plant.
+
+---
+
+## **Attribute Information**
+
+1. Sepal length in cm  
+2. Sepal width in cm  
+3. Petal length in cm  
+4. Petal width in cm  
+5. Class:  
+   - Iris Setosa  
+   - Iris Versicolour  
+   - Iris Virginica  
+
+---
+
+## **Features**
 
-## Data Set Information
+- **Flask API**:
+  - Endpoint `/predict` accepts feature arrays and returns predictions.
+  - Enabled **CORS** to allow cross-origin requests from the Streamlit app.
+- **Streamlit Frontend**:
+  - Interactive UI for users to input feature values (Sepal Length, Sepal Width, Petal Length, Petal Width).
+  - Displays prediction results dynamically.
+  - Includes a visually appealing background and project-specific design.
+- Pre-trained model based on the Iris dataset.
 
-This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. 
+---
 
-Predicted attribute: class of iris plant. 
+## **Steps**
 
-## Attribute Information
+1. Build and train the machine learning model in a Jupyter Notebook (file: `_model/Iris_model.ipynb`).
+2. Save the model in a (pickle) file (file: `api/iris_model.pkl`).
+3. Create an API application that uses the pre-trained model to generate predictions (file: `api/api.py`).
+4. Add a **Streamlit frontend** for user interaction (file: `streamlit_app.py`).
+5. Encapsulate the application in a Docker container (file: `api/Dockerfile`).
+6. Deploy the application to a cloud server.
 
-1. sepal length in cm 
-2. sepal width in cm 
-3. petal length in cm 
-4. petal width in cm 
-5. class: 
--- Iris Setosa 
--- Iris Versicolour 
--- Iris Virginica
+---
 
-## Steps
-1. Build and train the machine learning model in a Jupyter Notebook (file: _model/Iris_model.ipynb_),
-2. save the model in a (pickle) file (file: _api/iris_model.pkl_)
-3. create an API application that uses the pre-trained model to generate predictions (file: _api/api.py_),
-3. encapsulate the application in a Docker container (file: _api/Dockerfile_),
-4. deploy the application to a cloud server.
+## **Technical Requirements**
 
-## Technical Requirements
 + Python 3.4+,
 + Docker,
-+ The required Python libraries used can be installed from the included _requirements.txt_ file:
++ The required Python libraries can be installed from the included `requirements.txt` file.
 
+---
+
+## **Running the Application Locally**
+
+### **Directly**
 
-## Running the application locally
-### Directly
 ```bash
 # Clone the project
 git clone https://github.com/AchilleasKn/flask_api_python.git
@@ -50,26 +81,24 @@ apt install python3-pip
 # Install the requirements
 pip3 install -r requirements.txt
 
-# Run the script in Python
+# Run the Flask API
 python3 api.py
 ```
 
-### On Docker
+### **On Docker**
 
 ###### Available images:
-- achilleaskn/flask_api_python:latest
-
-This image is based on the python:3.6-jessie official image
+- `achilleaskn/flask_api_python:latest`
+  - This image is based on the `python:3.6-jessie` official image.
 
-[![](https://images.microbadger.com/badges/image/achilleaskn/flask_api_python.svg)](https://microbadger.com/images/achilleaskn/flask_api_python "Get your own image badge on microbadger.com")
+  [![](https://images.microbadger.com/badges/image/achilleaskn/flask_api_python.svg)](https://microbadger.com/images/achilleaskn/flask_api_python "Get your own image badge on microbadger.com")
 
-- achilleaskn/flask_api_python:alpine.latest
+- `achilleaskn/flask_api_python:alpine.latest`
+  - This image is based on the Alpine Linux image, which is a lightweight version of Linux.
 
-This image is based on Alpine Linux image which is a lightweight version of Linux
+  [![](https://images.microbadger.com/badges/image/achilleaskn/flask_api_python:alpine.latest.svg)](https://microbadger.com/images/achilleaskn/flask_api_python:alpine.latest "Get your own image badge on microbadger.com")
 
-[![](https://images.microbadger.com/badges/image/achilleaskn/flask_api_python:alpine.latest.svg)](https://microbadger.com/images/achilleaskn/flask_api_python:alpine.latest "Get your own image badge on microbadger.com")
-
-##### From scratch
+##### From Scratch
 
 ```bash
 # Clone the project
@@ -78,93 +107,158 @@ git clone https://github.com/AchilleasKn/flask_api_python.git
 # Change Directory
 cd flask_api_python/api
 
-# Build the docker image
+# Build the Docker image
 docker build -t flask_api .
 
-# For the alpine version run the following
-#docker build -f Dockerfile.alpine -t flask_api .
+# For the Alpine version, run the following:
+# docker build -f Dockerfile.alpine -t flask_api .
 
-# Run the flask_api image and expose the 5000 port 
+# Run the Flask API image and expose port 5000
 docker run -d -p 5000:5000 flask_api
 
 # To see the running containers
-docker ps 
+docker ps
 
-# To see the logs of our running container
+# To see the logs of the running container
 docker logs <Container ID>
 ```
 
 ##### With Docker Pull
+
 ```bash
-# Pull the docker image
+# Pull the Docker image
 docker pull achilleaskn/flask_api_python:latest
 
-# For the alpine version run the following
-#docker pull achilleaskn/flask_api_python:alpine.latest
+# For the Alpine version, run the following:
+# docker pull achilleaskn/flask_api_python:alpine.latest
 
-# Run the flask_api image and expose the 5000 port 
+# Run the Flask API image and expose port 5000
 docker run -d -p 5000:5000 achilleaskn/flask_api_python:latest
 
-# For the alpine version run the following
-#docker run -d -p 5000:5000 achilleaskn/flask_api_python:alpine.latest
+# For the Alpine version, run the following:
+# docker run -d -p 5000:5000 achilleaskn/flask_api_python:alpine.latest
 
 # To see the running containers
-docker ps 
+docker ps
 
-# To see the logs of our running container
+# To see the logs of the running container
 docker logs <Container ID>
 ```
 
-### Testing the application
-Once it is running, the API can be queried using HTTP POST requests.
-I recommend using [postman](https://www.getpostman.com/) for testing.
+---
 
-URL: `http://0.0.0.0:5000/predict`
+## **Testing the Application**
 
-- Sample query for "Setosa" type:
-```json
-{
-	"feature_array":[4.9, 2.9, 1.2, 0.3]
-}
-```
+Once it is running, the API can be queried using HTTP POST requests. I recommend using [Postman](https://www.getpostman.com/) for testing.
 
-The response should look like this:
-```json
-{
-    "prediction": [
-        0
-    ]
-}
-```
+**URL**: `http://0.0.0.0:5000/predict`
 
-- Sample query for "Versicolour" type:
-```json
-{
-	"feature_array":[6.4, 3.2, 4.5, 1.5]
-} 
-```
+- **Sample query for "Setosa" type**:
+  ```json
+  {
+      "feature_array": [4.9, 2.9, 1.2, 0.3]
+  }
+  ```
 
-The response should look like this:
-```json
-{
-    "prediction": [
-        1
-    ]
-}
-```
+  **Response**:
+  ```json
+  {
+      "prediction": [0]
+  }
+  ```
 
-- Sample query for "Virginica" type:
-```json
-{
-	"feature_array":[6.2, 3.1, 5.3, 2.4]
-} 
-```
+- **Sample query for "Versicolour" type**:
+  ```json
+  {
+      "feature_array": [6.4, 3.2, 4.5, 1.5]
+  }
+  ```
 
-The response should look like this:
-```json
-{
-    "prediction": [
-        2
-    ]
-}
-```
+  **Response**:
+  ```json
+  {
+      "prediction": [1]
+  }
+  ```
+
+- **Sample query for "Virginica" type**:
+  ```json
+  {
+      "feature_array": [6.2, 3.1, 5.3, 2.4]
+  }
+  ```
+
+  **Response**:
+  ```json
+  {
+      "prediction": [2]
+  }
+  ```
+
+---
+
+## **Using the Streamlit Frontend**
+
+The Streamlit app provides an interactive UI for users to input feature values and view predictions.
+
+### **How to Run the Streamlit App**
+
+1. Ensure the Flask API is running locally or deployed.
+2. Start the Streamlit app:
+   ```bash
+   streamlit run streamlit_app.py
+   ```
+3. The app will open in your default browser at:
+   ```
+   http://localhost:8501
+   ```
+
+### **Streamlit App Features**
+- Input fields for Sepal Length, Sepal Width, Petal Length, and Petal Width.
+- A "Predict" button to send the input to the Flask API and display the result.
+- Displays the predicted Iris species along with an image of the corresponding flower.
+
+---
+
+
+
+
+
+## **Deployment**
+
+### **Deploying the Flask API**
+You can deploy the Flask API to platforms like **Heroku**, **AWS**, or **Render**:
+1. Create a `Procfile`:
+   ```
+   web: gunicorn api:app
+   ```
+2. Deploy to Heroku:
+   ```bash
+   heroku create your-app-name
+   git push heroku main
+   ```
+
+### **Deploying the Streamlit App**
+Deploy the Streamlit app to [Streamlit Cloud](https://streamlit.io/cloud):
+1. Sign up for Streamlit Cloud.
+2. Connect your GitHub repository.
+3. Select `streamlit_app.py` as the entry point.
+4. Deploy the app.
+
+---
+
+## **Contributing**
+
+If you'd like to contribute, follow these steps:
+1. Fork the repository.
+2. Create a new branch for your changes:
+   ```bash
+   git checkout -b feature/your-feature-name
+   ```
+3. Push your changes and submit a pull request.
+
+---
+
+## **License**
+
+This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.