Update app.py

app.py

@@ -119,40 +119,41 @@ if disease_or_gard_id:
                                 NER_pipeline, entity_classes,
                                 extract_diseases, GARD_dict, max_length,
                                 classify_model_vars)
-    df.replace(to_replace='None', value="None")
-    st.dataframe(df, height=200)
-    csv = convert_df(df)
-    disease, gardID = name_gardID
-    #if the user input does not have a number in it (i.e. weak proxy for if it is a GARD ID), then preserve the user input as the disease term.
-    if not bool(re.search(r'\d', disease_or_gard_id)):
-        disease = disease_or_gard_id
+    if df:
+        df.replace(to_replace='None', value="None")
+        st.dataframe(df, height=200)
+        csv = convert_df(df)
+        disease, gardID = name_gardID
+        #if the user input does not have a number in it (i.e. weak proxy for if it is a GARD ID), then preserve the user input as the disease term.
+        if not bool(re.search(r'\d', disease_or_gard_id)):
+            disease = disease_or_gard_id
+            
+        st.download_button(
+            label="Download epidemiology results for "+disease+" as CSV",
+            data = csv,
+            file_name=disease+'.csv',
+            mime='text/csv',
+            )
         
-    st.download_button(
-        label="Download epidemiology results for "+disease+" as CSV",
-        data = csv,
-        file_name=disease+'.csv',
-        mime='text/csv',
-        )
-    
-    st.markdown('See the NIH GARD page for ['+disease+'](https://rarediseases.info.nih.gov/diseases/'+str(re.sub('GARD:|0','',gardID))+'/'+str('-'.join(disease.split()))+')')
-    
-    fig = epi_sankey(sankey_data,disease)
-    st.plotly_chart(fig, use_container_width=True)
-    
-    if 'IDS' in list(df.columns):
-        st.markdown('''COLUMNS: \\
-                   - PROB_OF_EPI: Probability that the paper is an epidemiologic study based on its abstract. \\
-                   - IsEpi: If it is an epidemiologic study (If PROB_OF_EPI >0.5) \\
-                   - DIS: Rare disease terms or synonyms identified in the abstract from the GARD Dictionary
-                   - IDS: GARD IDs identified in the abstract from the GARD Dictionary \\
-                   - EPI: Epidemiology Types are the metrics used to estimate disease burden such as "incidence", "prevalence rate", or "occurrence"
-                   - STAT: Epidemiology Rates describe how many people are afflicted by a disease.
-                   - DATE: The dates when the epidemiologic studies were conducted
-                   - LOC: Where the epidemiologic studies were conducted.
-                   - SEX: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population
-                   - ETHN: Ethnicities, races, and nationalities of those represented in the epidemiologic study.
-                ''')
-    else:
-        st.subheader("Categories of Results")
-        st.markdown("    - **PROB_OF_EPI**: Probability that the paper is an epidemiologic study based on its abstract.  \n  - **IsEpi**: If it is an epidemiologic study (If PROB_OF_EPI >0.5)  \n  - **EPI**: Epidemiology Types are the metrics used to estimate disease burden such as 'incidence', 'prevalence rate', or 'occurrence'  \n  - **STAT**: Epidemiology Rates describe how many people are afflicted by a disease.  \n  - **DATE**: The dates when the epidemiologic studies were conducted  \n  - **LOC**: Where the epidemiologic studies were conducted.  \n  - **SEX**: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population  \n  - **ETHN**: Ethnicities, races, and nationalities of those represented in the epidemiologic study.")
-    #st.dataframe(data=None, width=None, height=None)
+        st.markdown('See the NIH GARD page for ['+disease+'](https://rarediseases.info.nih.gov/diseases/'+str(re.sub('GARD:|0','',gardID))+'/'+str('-'.join(disease.split()))+')')
+        
+        fig = epi_sankey(sankey_data,disease)
+        st.plotly_chart(fig, use_container_width=True)
+        
+        if 'IDS' in list(df.columns):
+            st.markdown('''COLUMNS: \\
+                       - PROB_OF_EPI: Probability that the paper is an epidemiologic study based on its abstract. \\
+                       - IsEpi: If it is an epidemiologic study (If PROB_OF_EPI >0.5) \\
+                       - DIS: Rare disease terms or synonyms identified in the abstract from the GARD Dictionary
+                       - IDS: GARD IDs identified in the abstract from the GARD Dictionary \\
+                       - EPI: Epidemiology Types are the metrics used to estimate disease burden such as "incidence", "prevalence rate", or "occurrence"
+                       - STAT: Epidemiology Rates describe how many people are afflicted by a disease.
+                       - DATE: The dates when the epidemiologic studies were conducted
+                       - LOC: Where the epidemiologic studies were conducted.
+                       - SEX: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population
+                       - ETHN: Ethnicities, races, and nationalities of those represented in the epidemiologic study.
+                    ''')
+        else:
+            st.subheader("Categories of Results")
+            st.markdown("    - **PROB_OF_EPI**: Probability that the paper is an epidemiologic study based on its abstract.  \n  - **IsEpi**: If it is an epidemiologic study (If PROB_OF_EPI >0.5)  \n  - **EPI**: Epidemiology Types are the metrics used to estimate disease burden such as 'incidence', 'prevalence rate', or 'occurrence'  \n  - **STAT**: Epidemiology Rates describe how many people are afflicted by a disease.  \n  - **DATE**: The dates when the epidemiologic studies were conducted  \n  - **LOC**: Where the epidemiologic studies were conducted.  \n  - **SEX**: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population  \n  - **ETHN**: Ethnicities, races, and nationalities of those represented in the epidemiologic study.")
+        #st.dataframe(data=None, width=None, height=None)