predictive app

app.py

@@ -8,7 +8,7 @@ from src.map_viz import calling_map_viz
 from src.data_ingestion import daily_average
 from src.heatmap import HeatMap
 from src.data_ingestion import remove_previous_view, merge_volumes
-from src.pred_plot import prep_data_pred_plot, data_split, train_model, predicted_figure, get_today, convert_date, gen_fig
+from src.pred_plot import prep_data_pred_plot, data_split, train_model, predicted_figure, get_today, gen_fig, pred_bars
 from datetime import date
 
 def fetch_data():
@@ -97,22 +97,27 @@ def main():
         final_table = prep_data_pred_plot(pred_df)
         x_train, _, y_train, _ = data_split(final_table)
         clf = train_model(x_train, y_train)
-        pred_hour_choice = st.selectbox(
-            "Choose Your Planned Hour",
-            options= hours,
-            key = "pred_hour", index = hours.index("08:00")
-        )
-        pred_view_choice = st.selectbox(
-            "Choose View",
-            options= ['Johor-Tuas','Johor-Woodlands', 'Tuas-Johor', 'Woodlands-Johor'],
-            key = "pred_view"
-        )
-        d = st.date_input(
-            "Choose Your Planned Date",
-            date(today[0],today[1], today[2]))
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            pred_hour_choice = st.selectbox(
+                "Choose Your Planned Hour",
+                options= hours,
+                key = "pred_hour", index = hours.index("08:00")
+            )
+        with col2: 
+            pred_view_choice = st.selectbox(
+                "Choose View",
+                options= ['Johor-Tuas','Johor-Woodlands', 'Tuas-Johor', 'Woodlands-Johor'],
+                key = "pred_view"
+            )
+        with col3: 
+            d = st.date_input(
+                "Choose Your Planned Date",
+                date(today[0],today[1], today[2]))
 
         starter_variables = [x_train, str(d), pred_hour_choice, pred_view_choice]
         st.plotly_chart(predicted_figure(clf, starter_variables, figs))
-
+        st.plotly_chart(pred_bars(d, final_table))
+        
 if __name__ == "__main__":
     main()

src/pred_plot.py

@@ -3,6 +3,8 @@ from sklearn.model_selection import train_test_split
 from sklearn.neural_network import MLPClassifier
 import pandas as pd
 import plotly.graph_objects as go
+import streamlit as st
+from plotly.subplots import make_subplots
 
 def hour_rounder(t):
     if int(t.minute)>= 30:
@@ -32,13 +34,13 @@ def weekend(w):
         return 0
     
 def vehicle_cat(v):
-    if v >= 0 and v < 20:
+    if v >= 0 and v < 2:
         return 0
-    elif v >= 20 and v < 50:
+    elif v >= 2 and v < 4:
         return 1
-    elif v >= 50 and v < 80:
+    elif v >= 4 and v < 6:
         return 2
-    elif v >= 80 and v < 120:
+    elif v >= 6 and v < 8:
         return 3
     else:
         return 4 
@@ -117,8 +119,8 @@ def prep_data_pred_plot(df):
     final_df.loc[:, 'time'] = pd.to_datetime(final_df.loc[:,'time'], format='%H:%M:%S')
     final_df.loc[:,'hour'] = final_df.loc[:,'time'].apply(hour_rounder)
 
-    final_table = final_df.groupby(['view', 'day', 'hour']).sum().reset_index().loc[:,['day', 'hour','view', 'vehicle']]
-
+    final_table = final_df.groupby(['view', 'day', 'hour']).mean().reset_index().loc[:,['day', 'hour','view', 'vehicle']]
+    final_table['vehicle'] = final_table['vehicle'].apply(lambda x: round(x))
     final_table.loc[:,'peak'] = final_table.loc[:,'hour'].apply(peak_hours)
     final_table.loc[:,'peak'] = final_table.loc[:,'peak'].astype('category')
     final_table.loc[:,'weekend'] = final_table.loc[:,'day'].apply(weekend)
@@ -129,37 +131,63 @@ def prep_data_pred_plot(df):
     return final_table
 
 def gen_fig():
+
+    paths = ["M 0.2 0.35 L 0.48 0.52 L 0.52 0.50",
+            "M 0.25 0.75 L 0.475 0.52 L 0.52 0.52",
+            "M 0.5 0.9 L 0.485 0.52 L 0.515 0.52",
+            "M 0.75 0.75 L 0.485 0.52 L 0.52 0.51",
+            "M 0.8 0.35 L 0.48 0.50 L 0.52 0.52"]
+
     figs = []
+    values_ = ["No Traffic on Johor-Singapore Causeway", "Low Traffic on Johor-Singapore Causeway", "Johor-Singapore Causeway Slightly Busy", 
+            "Johor-Singapore Causeway Moderately Busy", "Busiest Time to Travel on Johor-Singapore Causeway"]
 
     for i in range(5):
-        midway = [15, 40, 70, 110, 150]
-        cat = ['No Traffic', 'Minimal Traffic', 'Mild Traffic', 'Moderate Traffic', 'Peak Traffic']
-        
-        figure = go.Figure(go.Indicator(
-                            mode = "gauge",
-                            value = midway[i],
-                            domain = {'x': [0, 1], 'y': [0, 1]},
-                            title = {'text': cat[i], 'font': {'size': 24}},
-                            gauge = {
-                                'axis': {'range': [None, 156], 'tickwidth': 1, 'tickcolor': "darkblue"},
-                                'bar': {'color': "blue"},
-                                'bgcolor': "white",
-                                'borderwidth': 2,
-                                'bordercolor': "gray",
-                                'steps': [
-                                    {'range': [0, 19], 'color': 'darkgreen'},
-                                    {'range': [20, 49], 'color': 'green'},
-                                    {'range': [50, 79], 'color': 'yellow'},
-                                    {'range': [80, 119], 'color': 'orange'},
-                                {'range': [120, 160], 'color': 'red'}],
-                                'threshold': {
-                                    'line': {'color': "red", 'width': 4},
-                                    'thickness': 0.75,
-                                    'value': 490}}))
-
-        figure.update_layout(paper_bgcolor = "lavender", font = {'color': "darkblue", 'family': "Arial"})
-
-        figs.append(figure)
+        plot_bgcolor = "#def"
+        colors = ["#f25829", "#f2a529", "#eff229", "#85e043", "#2bad4e","rgba(0,0,0,0)"] 
+        quadrant_text = ["<b>Heavy</b>", "<b>Moderate</b>", "<b>Mild</b>", "<b>Low</b>", "<b>None</b>",""]
+        n_quadrants = len(colors) - 1
+        figure_1 = go.Figure(
+            data=[
+                go.Pie(
+                    values=[14,14,14,14,14,30],
+                    rotation=130,
+                    hole=0.75,
+                    marker_colors=colors,
+                    marker_line={"width":2, "color":"white"},
+                    textinfo="none",
+                    text=quadrant_text,
+                    hoverinfo="text"
+                ),
+            ],
+            layout=go.Layout(
+                showlegend=False,
+                margin=dict(b=0,t=30,l=10,r=10),
+                width=500,
+                height=350,
+                paper_bgcolor="rgba(0,0,0,0)",
+                annotations=[
+                    go.layout.Annotation(
+                        text=f"<b>{values_[i]}</b>",
+                        x=0.5, xanchor="center", xref="paper",
+                        y= 0.1, yanchor="bottom", yref="paper",
+                        showarrow=False,
+                        font= {"size":15, "color":"#333"}
+                    )
+                ]
+            )
+        )
+        figure_1.update_layout(shapes=[dict(type='path',
+                                            path=paths[i],
+                                        fillcolor="#333"), 
+                                                    go.layout.Shape(
+                        type="circle",
+                        x0=0.48, x1=0.52,
+                        y0=0.48, y1=0.54,
+                        fillcolor="#333",
+                        line_color="#333",
+                    )])
+        figs.append(figure_1)
 
     return figs
     
@@ -200,4 +228,42 @@ def train_model(x_train, y_train):
     clf = MLPClassifier(solver='lbfgs', alpha=3, hidden_layer_sizes=(5,4), random_state=2, max_iter=3000)
     clf.fit(x_train, y_train)
 
-    return clf
+    return clf
+
+def pred_bars(my_date_picker_single, final_table):
+    day_today = convert_date(str(my_date_picker_single))
+    df_filter = final_table[final_table['day']==day_today]
+
+    color_map = {0:"#2bad4e", 1:"#85e043", 2:"#eff229", 3:"#f2a529", 4:"#f25829"}
+
+
+    bar_day = make_subplots(shared_yaxes="all", rows=2, cols=2, start_cell="bottom-left", subplot_titles=("Johor-Tuas", 
+                                                                                "Tuas-Johor", 
+                                                                                "Johor-Woodlands",
+                                                                                    "Johor-Woodlands"))
+    f1 = df_filter[df_filter['view']=='Johor-Tuas']
+    c1 = pd.Series(f1['cat']).map(color_map)
+    bar_day.add_trace(go.Bar(x=f1['hour'], y=f1['vehicle'], name='Johor-Tuas', showlegend=False, marker={'color':c1}),
+                row=1, col=1)
+
+    f2 = df_filter[df_filter['view']=='Tuas-Johor']
+    c2 = pd.Series(f2['cat']).map(color_map)
+    bar_day.add_trace(go.Bar(x=f2['hour'], y=f2['vehicle'], name='Tuas-Johor', showlegend=False, marker={'color':c2}),
+                row=1, col=2)
+    f3 = df_filter[df_filter['view']=='Johor-Woodlands']
+    c3 = pd.Series(f3['cat']).map(color_map)
+    bar_day.add_trace(go.Bar(x=f3['hour'], y=f3['vehicle'], name='Johor-Woodlands', showlegend=False, marker={'color':c3}),
+                row=2, col=1)
+    f4 = df_filter[df_filter['view']=='Woodlands-Johor']
+    c4 = pd.Series(f4['cat']).map(color_map)
+    bar_day.add_trace(go.Bar(x=f4['hour'], y=f4['vehicle'], name='Johor-Woodlands', showlegend=False, marker={'color':c4}),
+                row=2, col=2)
+
+    val_d = date.today().strftime("%d %B, %Y")
+    day_d = date.today().strftime("%A")
+    tex = "Predicted 24 Hour Traffic Trend on: " + day_d + ", " + str(val_d)
+
+
+    bar_day.update_layout(title_text=tex, paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)")
+    bar_day.update_xaxes(tickangle=45)
+    return bar_day