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Instructions and Procedure.md

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STEPS TO DEPLOY A ML MODEL IN Streamlit

1. Create app.py or any name.py file in a folder, containing the following below code as an example w.r.t to a dataset :

Then run the file as python script

CODE ................................................................................................ import streamlit as st import pandas as pd import joblib

Load the trained model

model = joblib.load("C:\imp\1. ISRO\RF_mining_model.pkl")

Title of the web app

st.title("Mining Site Prediction")

Sidebar for user input

st.sidebar.header("Input Features")

def user_input_features(): Distance_from_Earth = st.sidebar.slider("Distance from Earth (M km)", 1.0, 1000.0, 100.0) Iron = st.sidebar.slider("Iron (%)", 0.0, 100.0, 50.0) Nickel = st.sidebar.slider("Nickel (%)", 0.0, 100.0, 50.0) Water_Ice = st.sidebar.slider("Water Ice (%)", 0.0, 100.0, 50.0) Other_Minerals = st.sidebar.slider("Other Minerals (%)", 0.0, 100.0, 50.0) Estimated_Value = st.sidebar.slider("Estimated Value (B USD)", 0.0, 500.0, 100.0) Sustainability_Index = st.sidebar.slider("Sustainability Index", 0.0, 100.0, 0.5) Efficiency_Index = st.sidebar.slider("Efficiency Index", 0.0, 100.0, 0.5)

data = {
    'Distance from Earth (M km)': Distance_from_Earth,
    'Iron (%)': Iron,
    'Nickel (%)': Nickel,
    'Water Ice (%)': Water_Ice,
    'Other Minerals (%)': Other_Minerals,
    'Estimated Value (B USD)': Estimated_Value,
    'Sustainability Index': Sustainability_Index,
    'Efficiency Index': Efficiency_Index
}

features = pd.DataFrame(data, index=[0])
return features

Get user input

input_df = user_input_features()

Display user input

st.subheader('User Input Features') st.write(input_df)

Make prediction

prediction = model.predict(input_df)

Display the prediction result

st.subheader('Prediction Result')

Customize the prediction message

if prediction[0] == 1: st.success("✅ This is a Potential Mining Site.") else: st.error("❌ This is Not a Potential Mining Site.")

Optionally, you can add more details or a description below the result

st.markdown("""

Note: The prediction is based on the model's analysis of key features such as distance from Earth, mineral composition,estimated value(B USD) and sustainability indices.
""", unsafe_allow_html=True)

................................................................................................................................

2. Now, open cmd and give the following command

cd 'path/to the folder/ where ur app.py is present'

AND Not the path of ur app.py file

Run the command streamlit run app.py

'CODE'

C:\Users\debna>cd "C:\imp\1. ISRO"

C:\imp\1. ISRO>streamlit run app.py

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501 Network URL: http://192.168.118.96:8501

Now, lets deploy it permanently as app

  1. create a repository in github

2.Add requirements.txt in First Deployment folder containing all libraries required

3.Add all files of First Deployment folder and not the whole folder at once.

4.Easy, now create new app in Streamlit cloud and deploy it.