Update app.py

app.py

@@ -129,197 +129,206 @@ if submitted:
         'Others'
     ]
 
-    joined_clean_sents = prep_text(Text_entry)
-
-    # tokenize
-    tokenizer_1 = load_tokenizer_1()
-    tokenized_text_1 = tokenizer_1(joined_clean_sents, return_tensors="pt")
-
-    # predict
-    model_1 = load_model_1()
-    text_logits_1 = model_1(**tokenized_text_1).logits
-    predictions_1 = torch.softmax(text_logits_1, dim=1).tolist()[0]
-    predictions_1 = [round(a, 3) for a in predictions_1]
-
-    # dictionary with label as key and percentage as value
-    pred_dict_1 = (dict(zip(label_list_1, predictions_1)))
-
-    # sort 'pred_dict' by value and index the highest at [0]
-    sorted_preds_1 = sorted(pred_dict_1.items(), key=lambda x: x[1], reverse=True)
-
-    # Make dataframe for plotly bar chart
-    u_1, v_1 = zip(*sorted_preds_1)
-    x_1 = list(u_1)
-    y_1 = list(v_1)
-    df2 = pd.DataFrame()
-    df2['SubCatName'] = x_1
-    df2['Likelihood'] = y_1
-
-
-    # Second prediction
-
-    label_list_2 = ["False", "True"]
-
-    joined_clean_sents = prep_text(Text_entry)
-
-    # tokenize
-    tokenizer_2 = load_tokenizer_2()
-    tokenized_text_2 = tokenizer_2(joined_clean_sents, return_tensors="pt")
-
-    # predict
-    model_2 = load_model_2()
-    text_logits_2 = model_2(**tokenized_text_2).logits
-    predictions_2 = torch.softmax(text_logits_2, dim=1).tolist()[0]
-    predictions_2 = [round(a_, 3) for a_ in predictions_2]
-
-    # dictionary with label as key and percentage as value
-    pred_dict_2 = (dict(zip(label_list_2, predictions_2)))
-
-    # sort 'pred_dict' by value and index the highest at [0]
-    sorted_preds_2 = sorted(pred_dict_2.items(), key=lambda x: x[1], reverse=True)
-
-    # Make dataframe for plotly bar chart
-    u_2, v_2 = zip(*sorted_preds_2)
-    x_2 = list(u_2)
-    y_2 = list(v_2)
-    df3 = pd.DataFrame()
-    df3['ExtraOver'] = x_2
-    df3['Likelihood'] = y_2
-
-
-    # Third prediction
-
-    label_list_3 = ['0.04', '0.045', '0.05', '0.1', '0.15', '0.2', '1.0', '7.0', '166.67', 'Others']
-
-    joined_clean_sents = prep_text(Text_entry)
-
-    # tokenize
-    tokenizer_3 = load_tokenizer_3()
-    tokenized_text_3 = tokenizer_3(joined_clean_sents, return_tensors="pt")
-
-    # predict
-    model_3 = load_model_3()
-    text_logits_3 = model_3(**tokenized_text_3).logits
-    predictions_3 = torch.softmax(text_logits_3, dim=1).tolist()[0]
-    predictions_3 = [round(a_, 3) for a_ in predictions_3]
-
-    # dictionary with label as key and percentage as value
-    pred_dict_3 = (dict(zip(label_list_3, predictions_3)))
-
-    # sort 'pred_dict' by value and index the highest at [0]
-    sorted_preds_3 = sorted(pred_dict_3.items(), key=lambda x: x[1], reverse=True)
-
-    # Make dataframe for plotly bar chart
-    u_3, v_3 = zip(*sorted_preds_3)
-    x_3 = list(u_3)
-    y_3 = list(v_3)
-    df4 = pd.DataFrame()
-    df4['Conversion_factor'] = x_3
-    df4['Likelihood'] = y_3
-
-
-    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=900,
-            height=1000,
-            xaxis_title="Likelihood of SubCatName",
-            yaxis_title="SubCatNames",
-            # legend_title="Topics"
+    if Text_entry == "":
+        st.warning(
+            """This app needs text input to generate predictions. Kindly type or paste text into 
+            the above **"Text Input"** box""",
+            icon="⚠️"
         )
 
-        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")
-
-        fig.update_layout(
-            # barmode='stack',
-            template='ggplot2',
-            font=dict(
-                family="Arial",
-                size=14,
-                color="black"
-            ),
-            autosize=False,
-            width=500,
-            height=500,
-            xaxis_title="Likelihood of Conversion_factor",
-            yaxis_title="Conversion_factor",
-            # 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 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 Conversion_factor", sorted_preds_3[0][0])
-        Prediction_confidence_3 = st.metric("Prediction confidence", (str(round(sorted_preds_3[0][1] * 100, 1)) + "%"))
-
-        st.success("Great! Predictions successfully completed. ", icon="✅")
+    elif Text_entry != "":
+
+        joined_clean_sents = prep_text(Text_entry)
+    
+        # tokenize
+        tokenizer_1 = load_tokenizer_1()
+        tokenized_text_1 = tokenizer_1(joined_clean_sents, return_tensors="pt")
+    
+        # predict
+        model_1 = load_model_1()
+        text_logits_1 = model_1(**tokenized_text_1).logits
+        predictions_1 = torch.softmax(text_logits_1, dim=1).tolist()[0]
+        predictions_1 = [round(a, 3) for a in predictions_1]
+    
+        # dictionary with label as key and percentage as value
+        pred_dict_1 = (dict(zip(label_list_1, predictions_1)))
+    
+        # sort 'pred_dict' by value and index the highest at [0]
+        sorted_preds_1 = sorted(pred_dict_1.items(), key=lambda x: x[1], reverse=True)
+    
+        # Make dataframe for plotly bar chart
+        u_1, v_1 = zip(*sorted_preds_1)
+        x_1 = list(u_1)
+        y_1 = list(v_1)
+        df2 = pd.DataFrame()
+        df2['SubCatName'] = x_1
+        df2['Likelihood'] = y_1
+    
+    
+        # Second prediction
+    
+        label_list_2 = ["False", "True"]
+    
+        joined_clean_sents = prep_text(Text_entry)
+    
+        # tokenize
+        tokenizer_2 = load_tokenizer_2()
+        tokenized_text_2 = tokenizer_2(joined_clean_sents, return_tensors="pt")
+    
+        # predict
+        model_2 = load_model_2()
+        text_logits_2 = model_2(**tokenized_text_2).logits
+        predictions_2 = torch.softmax(text_logits_2, dim=1).tolist()[0]
+        predictions_2 = [round(a_, 3) for a_ in predictions_2]
+    
+        # dictionary with label as key and percentage as value
+        pred_dict_2 = (dict(zip(label_list_2, predictions_2)))
+    
+        # sort 'pred_dict' by value and index the highest at [0]
+        sorted_preds_2 = sorted(pred_dict_2.items(), key=lambda x: x[1], reverse=True)
+    
+        # Make dataframe for plotly bar chart
+        u_2, v_2 = zip(*sorted_preds_2)
+        x_2 = list(u_2)
+        y_2 = list(v_2)
+        df3 = pd.DataFrame()
+        df3['ExtraOver'] = x_2
+        df3['Likelihood'] = y_2
+    
+    
+        # Third prediction
+    
+        label_list_3 = ['0.04', '0.045', '0.05', '0.1', '0.15', '0.2', '1.0', '7.0', '166.67', 'Others']
+    
+        joined_clean_sents = prep_text(Text_entry)
+    
+        # tokenize
+        tokenizer_3 = load_tokenizer_3()
+        tokenized_text_3 = tokenizer_3(joined_clean_sents, return_tensors="pt")
+    
+        # predict
+        model_3 = load_model_3()
+        text_logits_3 = model_3(**tokenized_text_3).logits
+        predictions_3 = torch.softmax(text_logits_3, dim=1).tolist()[0]
+        predictions_3 = [round(a_, 3) for a_ in predictions_3]
+    
+        # dictionary with label as key and percentage as value
+        pred_dict_3 = (dict(zip(label_list_3, predictions_3)))
+    
+        # sort 'pred_dict' by value and index the highest at [0]
+        sorted_preds_3 = sorted(pred_dict_3.items(), key=lambda x: x[1], reverse=True)
+    
+        # Make dataframe for plotly bar chart
+        u_3, v_3 = zip(*sorted_preds_3)
+        x_3 = list(u_3)
+        y_3 = list(v_3)
+        df4 = pd.DataFrame()
+        df4['Conversion_factor'] = x_3
+        df4['Likelihood'] = y_3
+    
+    
+        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=900,
+                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")
+    
+            fig.update_layout(
+                # barmode='stack',
+                template='ggplot2',
+                font=dict(
+                    family="Arial",
+                    size=14,
+                    color="black"
+                ),
+                autosize=False,
+                width=500,
+                height=500,
+                xaxis_title="Likelihood of Conversion_factor",
+                yaxis_title="Conversion_factor",
+                # 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 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 Conversion_factor", sorted_preds_3[0][0])
+            Prediction_confidence_3 = st.metric("Prediction confidence", (str(round(sorted_preds_3[0][1] * 100, 1)) + "%"))
+    
+            st.success("Great! Predictions successfully completed. ", icon="✅")