Upload app.py

app.py

@@ -155,43 +155,6 @@ if submitted:
     df2['SubCatName'] = x_1
     df2['Likelihood'] = y_1
 
-    c1, c2, c3 = st.columns([1.5, 0.5, 1])
-
-    with c1:
-        st.header("SubCatName")
-        # plot graph of predictions
-        fig = px.bar(df2, x="Likelihood", y="SubCatName", orientation="h")
-
-        fig.update_layout(
-            # barmode='stack',
-            template='ggplot2',
-            font=dict(
-                family="Arial",
-                size=14,
-                color="black"
-            ),
-            autosize=False,
-            width=500,
-            height=1000,
-            xaxis_title="Likelihood of SubCatName",
-            yaxis_title="SubCatNames",
-            # legend_title="Topics"
-        )
-
-        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
-        fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
-        fig.update_annotations(font_size=14)  # this changes y_axis, x_axis and subplot title font sizes
-
-        # Plot
-        st.plotly_chart(fig, use_container_width=False)
-
-    with c3:
-        st.header("")
-        predicted_1 = st.metric("Predicted SubCatName", sorted_preds_1[0][0])
-        Prediction_confidence_1 = st.metric("Prediction confidence", (str(round(sorted_preds_1[0][1]*100, 1))+"%"))
-
-        #st.success("Great! SubCatName successfully predicted. ", icon="✅")
-
 
     # Second prediction
 
@@ -223,44 +186,6 @@ if submitted:
     df3['ExtraOver'] = x_2
     df3['Likelihood'] = y_2
 
-    d1, d2, d3 = st.columns([1.5, 0.5, 1])
-
-    with d1:
-        st.header("ExtraOver")
-        # plot graph of predictions
-        fig = px.bar(df3, x="Likelihood", y="ExtraOver", orientation="h")
-
-        fig.update_layout(
-            # barmode='stack',
-            template='ggplot2',
-            font=dict(
-                family="Arial",
-                size=14,
-                color="black"
-            ),
-            autosize=False,
-            width=500,
-            height=200,
-            xaxis_title="Likelihood of ExtraOver",
-            yaxis_title="ExtraOver",
-            # legend_title="Topics"
-        )
-
-        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
-        fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
-        fig.update_annotations(font_size=14)  # this changes y_axis, x_axis and subplot title font sizes
-
-        # Plot
-        st.plotly_chart(fig, use_container_width=False)
-
-    with d3:
-        st.header("")
-        predicted_2 = st.metric("Predicted ExtraOver", sorted_preds_2[0][0])
-        Prediction_confidence_2 = st.metric("Prediction confidence", (str(round(sorted_preds_2[0][1]*100, 1))+"%"))
-
-        #st.success("Great! ExtraOver successfully predicted. ", icon="✅")
-
-
 
     # Third prediction
 
@@ -292,9 +217,68 @@ if submitted:
     df4['Conversion_factor'] = x_3
     df4['Likelihood'] = y_3
 
-    e1, e2, e3 = st.columns([1.5, 0.5, 1])
 
-    with e1:
+    st.empty()
+
+    tab1, tab2, tab3, tab4 = st.tabs(["Subcategory", "Extra Over", "Conversion Factor", "Summary"])
+
+    with tab1:
+        st.header("SubCatName")
+        # plot graph of predictions
+        fig = px.bar(df2, x="Likelihood", y="SubCatName", orientation="h")
+
+        fig.update_layout(
+            # barmode='stack',
+            template='ggplot2',
+            font=dict(
+                family="Arial",
+                size=14,
+                color="black"
+            ),
+            autosize=False,
+            width=500,
+            height=1000,
+            xaxis_title="Likelihood of SubCatName",
+            yaxis_title="SubCatNames",
+            # legend_title="Topics"
+        )
+
+        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
+        fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
+        fig.update_annotations(font_size=14)  # this changes y_axis, x_axis and subplot title font sizes
+
+        # Plot
+        st.plotly_chart(fig, use_container_width=False)
+
+    with tab2:
+        st.header("ExtraOver")
+        # plot graph of predictions
+        fig = px.bar(df3, x="Likelihood", y="ExtraOver", orientation="h")
+
+        fig.update_layout(
+            # barmode='stack',
+            template='ggplot2',
+            font=dict(
+                family="Arial",
+                size=14,
+                color="black"
+            ),
+            autosize=False,
+            width=500,
+            height=200,
+            xaxis_title="Likelihood of ExtraOver",
+            yaxis_title="ExtraOver",
+            # legend_title="Topics"
+        )
+
+        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
+        fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))
+        fig.update_annotations(font_size=14)  # this changes y_axis, x_axis and subplot title font sizes
+
+        # Plot
+        st.plotly_chart(fig, use_container_width=False)
+
+    with tab3:
         st.header("Conversion_factor")
         # plot graph of predictions
         fig = px.bar(df4, x="Likelihood", y="Conversion_factor", orientation="h")
@@ -322,10 +306,20 @@ if submitted:
         # Plot
         st.plotly_chart(fig, use_container_width=False)
 
-    with e3:
+    with tab4:
+        # subcatNames
+        st.header("")
+        predicted_1 = st.metric("Predicted SubCatName", sorted_preds_1[0][0])
+        Prediction_confidence_1 = st.metric("Prediction confidence", (str(round(sorted_preds_1[0][1] * 100, 1)) + "%"))
+
+        #ExtraOver
+        st.header("")
+        predicted_2 = st.metric("Predicted ExtraOver", sorted_preds_2[0][0])
+        Prediction_confidence_2 = st.metric("Prediction confidence", (str(round(sorted_preds_2[0][1] * 100, 1)) + "%"))
+
+        # Conversion_factor
         st.header("")
         predicted_3 = st.metric("Predicted ExtraOver", sorted_preds_3[0][0])
-        Prediction_confidence_3 = st.metric("Prediction confidence",
-                                            (str(round(sorted_preds_3[0][1] * 100, 1)) + "%"))
+        Prediction_confidence_3 = st.metric("Prediction confidence", (str(round(sorted_preds_3[0][1] * 100, 1)) + "%"))
 
-        #st.success("Great! Conversion_factor successfully predicted. ", icon="✅")
+        st.success("Great! Predictions successfully predicted. ", icon="✅")