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72 lines
2.3 KiB
72 lines
2.3 KiB
import streamlit as st
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import pandas as pd
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import sys
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sys.path.append('./back/')
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import clustering_csv as cc
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import prediction as p
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def handle_column_multiselect(df, method_name):
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selected_columns = st.multiselect(f"Select the columns you want for {method_name}:", df.columns.tolist(), placeholder="Select dataset columns")
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return selected_columns
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def display_prediction_results(df, targetCol, sourceColumns, method):
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original_col = df[targetCol]
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predicted_col = p.getColumnsForPredictionAndPredict(df, sourceColumns, targetCol, method)
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new_df = pd.DataFrame()
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new_df['Original'] = original_col
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new_df['Predicted'] = predicted_col
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st.dataframe(new_df)
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if 'df' in st.session_state:
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df = st.session_state.df
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st.write("# 🔮 Prediction")
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tab1, tab2 = st.tabs(["Clustering", "Predictions"])
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with tab1:
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st.header("Clustering")
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selected_columns = handle_column_multiselect(df, "clustering")
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tab_names = ["K-means", "DBSCAN"]
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tab11, tab12 = st.tabs(tab_names)
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with tab11:
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if st.button(f"Start {tab_names[0]}"):
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st.pyplot(cc.launch_cluster_knn(df, selected_columns))
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with tab12:
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if st.button(f"Start {tab_names[1]}"):
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st.pyplot(cc.launch_cluster_dbscan(df, selected_columns))
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with tab2:
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st.header("Predictions")
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target_column = st.selectbox(
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"Target column:",
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df.columns.tolist(),
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index=None,
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placeholder="Select target column"
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)
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if target_column != None:
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selected_columns_p = handle_column_multiselect(df, "predictions")
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tab_names = ["Linear Regression", "Random Forest"]
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tab21, tab22 = st.tabs(tab_names)
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with tab21:
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if st.button(f"Start {tab_names[0]}"):
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st.write(target_column)
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st.write(selected_columns_p)
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display_prediction_results(df, target_column, selected_columns_p, tab_names[0])
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with tab22:
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if st.button(f"Start {tab_names[1]}"):
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display_prediction_results(df, target_column, selected_columns_p, tab_names[1])
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else:
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st.write("Please clean your dataset.")
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