Python for Data Science | Data Science With Python | Data Science Apps with Python and Streamlit
Certainly! Streamlit is a great tool for building interactive web applications for data science and machine learning. Below is a step-by-step guide on how to build your first app using Streamlit to create a machine learning app that takes a photo. This example assumes you have some basic knowledge of Python and data science concepts.
Step 1: Install Streamlit
Make sure you have Python installed on your system. You can install Streamlit using the following command in your terminal or command prompt:
bash
pip install streamlit
Step 2: Set up your environment
Create a new directory for your project and navigate to it:
bash
mkdir streamlit_ml_app
cd streamlit_ml_app
Step 3: Create a Python script (e.g., app.py)
Create a new Python file, for example, app.py, and open it in your favorite text editor. This will be the main script for your Streamlit app.
python
app.py
import streamlit as st
import numpy as np
from PIL import Image
import tensorflow as tf
Load pre-trained model (you can replace this with your own model)
model = tf.keras.applications.MobileNetV2(weights='imagenet')
def predict(image):
Preprocess the image
img_array = tf.image.decode_image(image, channels=3)
img_array = tf.image.resize(img_array, (224, 224))
img_array = tf.expand_dims(img_array, 0)
img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
Make prediction
predictions = model.predict(img_array)
decoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy())[0]
return decoded_predictions
def main():
st.title("Machine Learning Photo App")
uploaded_file = st.file_uploader("Choose a photo...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
Display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image.", use_column_width=True)
Make prediction when a button is clicked
if st.button("Predict"):
predictions = predict(uploaded_file.read())
st.success(f"Prediction: {predictions}")
if _name_ == "__main__":
main()
Step 4: Run your Streamlit app
In your terminal or command prompt, navigate to the directory where your app.py is located and run the following command:
bash
streamlit run app.py
This command will start a local development server, and you can view your app in a web browser by navigating to the provided URL (usually http://localhost:8501).
Step 5: Use your app
You can now upload a photo, click the "Predict" button, and see the machine learning predictions based on the pre-trained MobileNetV2 model.
Feel free to customize the app further based on your specific needs and the machine learning model you want to use.
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