model

app.py

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+import pandas as pd
+import streamlit as st
+import numpy as np
+import sklearn
+import joblib
+import plotly.express as px
+
+
+pipeline = joblib.load('toolkit/Pipeline.joblib')
+model = joblib.load('toolkit/model.joblib')
+
+df = pd.read_csv('Dataset/predicted_data_access_consumption_2015_2029.csv')
+
+
+st.set_page_config(
+    page_title='Electricity Access Prediction App',
+    page_icon=':cityscape:'
+)
+
+
+
+# Add a title and subtitle
+st.write("<center><h1>Electricity Access Prediction App ⚡</h1></center>", unsafe_allow_html=True)
+
+# Sidebar navigation
+page = st.sidebar.selectbox("Select Page", ["Predicted Dataset", "Prediction"])
+
+
+
+if page == "Predicted Dataset":
+    # Add a section for displaying the preloaded DataFrame
+    st.subheader("Predicted Dataset")
+
+    # Display the DataFrame
+    st.write("Actual and predicted values of our dataset (2015-2029) from the IMF:")
+    st.dataframe(df) 
+
+    # Add a selectbox to choose a country
+    country = st.selectbox("Select a Country", df['Country'].unique())
+
+    # Filter the DataFrame based on the selected country
+    filtered_df = df[df['Country'] == country]
+
+    # Display the filtered DataFrame
+    st.write(f"Data for {country}:")
+    st.dataframe(filtered_df)
+
+    st.subheader(f"Access to Electricity in {country} Over Time")
+    fig = px.line(filtered_df, x='Year', y='Predicted_Access_to_electricity', title=f'Access to Electricity in {country} Over Time', markers=True)
+    st.plotly_chart(fig)
+
+
+
+
+
+elif page == "Prediction":
+    
+    st.write("This app uses machine learning to predict access to electricity based on certain input parameters. Simply enter the required information and click 'Predict' to get a prediction!")
+    st.subheader("Enter the details to predict access to electricity by % of population")
+
+    st.sidebar.write("""
+    ## Data Input Explanation
+    This app predicts access to electricity based on the following input parameters:
+    - **Year**: The year for which the prediction is being made.
+    - **Income Group**: The income classification of the country (Low income, Lower middle income, Upper middle income, High income).
+    - **Population**: The total population of the country.
+    - **GDP per capita (USD)**: The Gross Domestic Product per capita in current US dollars.
+    - **Inflation (annual %)**: The annual inflation rate.
+    - **Consumption (kWh per capita)**: The electricity consumption per capita in kilowatt-hours.
+    """)
+
+    # Set up the layout
+    col1, col2 = st.columns(2)
+
+    # Create the input fields
+    input_data = {}
+    with col1:
+        input_data['Year'] = st.number_input("Input Year", min_value=1990, max_value=2030, step=1)
+        input_data['IncomeGroup'] = st.radio("Pick an income group", ["Lower middle income", "Upper middle income", "High income", "Low income"])
+        input_data['Population'] = st.slider("Population of the country", min_value=9000, max_value=10000000000)
+
+    with col2:
+        input_data['GDP_per_capita_USD'] = st.slider("Enter GDP per capita (Current $)", min_value=20.0, max_value=250000.0, step=0.1)
+        input_data['Inflation_annual_percent'] = st.slider("Enter Consumer Price Index", min_value=-20.0, max_value=25000.0, step=0.1)
+        input_data['Consumption (kWh per capita)'] = st.slider("Enter Electricity Consumption", min_value=10.0, max_value=55000.0, step=0.1)
+
+    # Define the custom CSS
+    predict_button_css = """
+        <style>
+        .predict-button {
+            background-color: #C4C4C4;
+            color: gray;
+            padding: 0.75rem 2rem;
+            border-radius: 0.5rem;
+            border: none;
+            font-size: 1.1rem;
+            font-weight: bold;
+            text-align: center;
+            margin-top: 2rem;
+        }
+        </style>
+    """
+
+    # Display the custom CSS
+    st.markdown(predict_button_css, unsafe_allow_html=True)
+
+    # Create a button to make a prediction
+    if st.button("Predict", key="predict_button", help="Click to make a prediction."):
+        # Convert the input data to a pandas DataFrame
+        input_df = pd.DataFrame([input_data])
+
+        # Selecting categorical and numerical columns separately
+        numerical = input_df.select_dtypes(include=[np.number]).columns.tolist()
+        categorical = input_df.select_dtypes(exclude=[np.number]).columns.tolist()
+
+        # Fit and transform the data
+        X_processed = pipeline.transform(input_df)
+
+        # Extracting feature names for numerical columns
+        num_feature_names = numerical
+
+        # Extracting feature names for categorical columns after one-hot encoding
+        cat_encoder = pipeline.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
+        cat_feature_names = cat_encoder.get_feature_names_out(categorical)
+
+        # Concatenating numerical and categorical feature names
+        feature_names = num_feature_names + list(cat_feature_names)
+
+        # Convert X_processed to DataFrame
+        final_df = pd.DataFrame(X_processed, columns=feature_names)
+
+        # Make a prediction
+        prediction = model.predict(final_df)[0]
+
+        # Display the prediction
+        st.write(f"The predicted Access to electricity is: {prediction}.")
+        st.table(input_df)

toolkit/model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:58bddbe34894d41a3350643f5ed79fe4f9b1959d65c507f4884d1ca4ae49df50
+size 2552428