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| # import libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| df = pd.read_csv("rtv.csv", low_memory=False) | |
| # load model from pickle file | |
| with open("rf.pkl", "rb") as f: | |
| rf = pickle.load(f) | |
| st.set_page_config( | |
| page_title = 'Real-Time Data Science Dashboard', | |
| page_icon = '✅', | |
| layout = 'wide' | |
| ) | |
| # create sidebar title | |
| st.title("Raising the :green[Village] :desert:") | |
| st.image("https://ngosjob.b-cdn.net/wp-content/uploads/2023/03/Raising-The-Village.png") | |
| # creating a single-element container. | |
| placeholder = st.empty() | |
| duration = df['duration'].mean() | |
| mean = pd.to_numeric(df['HH Income (UGX)'], errors='coerce').mean() | |
| maxi = pd.to_numeric(df['HH Income (UGX)'], errors='coerce').max() | |
| with placeholder.container(): | |
| # create three columns | |
| kpi1, kpi2, kpi3 = st.columns(3) | |
| # fill in those three columns with respective metrics or KPIs | |
| kpi1.metric(label="Average Income ⏳", value=round(mean)) | |
| kpi2.metric(label="Mean Time(minutes) 💍", value= int(duration)) | |
| kpi3.metric(label="Max income＄", value= f"$ {round(maxi,2)} ") | |
| # create title | |
| st.title("Income Prediction") | |
| # create input fields for each feature | |
| inputs = [] | |
| X_columns = ['Season 2 Agriculture Value (Ugx)', | |
| 'Casual Labour (Ugx)', | |
| 'Season 1 Agriculture Value (Ugx)', | |
| 'Perenial Crops Income (Ugx)', | |
| 'Personal Business & Self Employment (Ugx)', | |
| 'Agriculture Income (UGX) ', | |
| 'Perennial Agriculture Value (Ugx)', | |
| 'HH Income + Consumption + Residues (UGX)'] | |
| for col in X_columns: | |
| value = st.number_input(f"Enter value for {col}", value=0.0) | |
| inputs.append(value) | |
| # create button for prediction | |
| predict = st.button("Predict Income") | |
| if predict: | |
| # convert inputs to numpy array and reshape to match model input shape | |
| inputs = np.array(inputs).reshape(1, -1) | |
| # make prediction using loaded model | |
| y_pred = rf.predict(inputs) | |
| # display prediction result | |
| st.write(f"The predicted income value is {y_pred[0]:.2f}") | |
| st.markdown("============================================================================================================================") | |
| # make prediction and display result if button is clicked and data is uploaded | |
| # display file upload option | |
| st.title("Get predictions of the whole dataset") | |
| st.write("Upload your csv or excel file here:") | |
| st.write("The dashboard supports only csv files at the moment") | |
| uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx"]) | |
| def preproc(df): | |
| df = df[['Season 2 Agriculture Value (Ugx)', | |
| 'Casual Labour (Ugx)', | |
| 'Season 1 Agriculture Value (Ugx)', | |
| 'Perenial Crops Income (Ugx)', | |
| 'Personal Business & Self Employment (Ugx)', | |
| 'Agriculture Income (UGX) ', | |
| 'Perennial Agriculture Value (Ugx)', | |
| 'HH Income + Consumption + Residues (UGX)']] | |
| df.fillna(df.mean(), inplace=True) | |
| return df | |
| # load and display data if uploaded | |
| if uploaded_file is not None: | |
| if uploaded_file.type == "text/csv": | |
| df = pd.read_csv(uploaded_file) | |
| elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": | |
| df = pd.read_excel(uploaded_file) | |
| X_new = preproc(df) | |
| else: | |
| st.write("No file uploaded yet.") | |
| # create button for prediction | |
| predict = st.button("Predict") | |
| # make prediction and display result if button is clicked and data is uploaded | |
| if uploaded_file is not None and predict: | |
| # make prediction using loaded model | |
| y_pred = rf.predict(X_new) | |
| # display prediction result as dataframe | |
| df["prediction"] = y_pred.round(2) | |
| st.dataframe(df[["Surveyor_Name", "prediction"]]) | |
| job_filter = st.selectbox("Select the Quartile", pd.unique(df['Quartile'])) | |
| # dataframe filter | |
| df1 = df[df['Quartile']==job_filter] | |
| st.dataframe(df1) | |