Update app.py

app.py

@@ -61,7 +61,7 @@ id2label = {
 
 #     #Get the file path
 
-file = st.file_uploader("File upload", type=["pdf"])
+file = st.file_uploader("File upload", type=['pdf', 'docx', 'txt'])
 
 if uploaded_file is not None: 
 
@@ -71,36 +71,36 @@ if uploaded_file is not None:
         temp.write(bytes_data)
         print(temp.name)
 
-#     # Process file
-#     par_list = get_paragraphs(uploaded_file)
+#    # Process file
+     par_list = get_paragraphs(temp.name)
 
-# ### Make predictions 
-# preds = vg_model(par_list)
+    ### Make predictions 
+    preds = vg_model(par_list)
 
-# # Get label names 
-# preds_list = preds.tolist()
+    # Get label names 
+    preds_list = preds.tolist()
 
-# predictions_names=[]
+    predictions_names=[]
 
-# # loop through each prediction
-# for ele in preds_list:
-#   try:
-#     index_of_one = ele.index(1)
-#   except ValueError:
-#     index_of_one = "NA" 
-#   if index_of_one != "NA": 
-#     name  = id2label[index_of_one]
-#   else: 
-#     name = "NA"
-#     predictions_names.append(name)
+    # loop through each prediction
+    for ele in preds_list:
+       try:
+         index_of_one = ele.index(1)
+       except ValueError:
+    index_of_one = "NA" 
+  if index_of_one != "NA": 
+    name  = id2label[index_of_one]
+  else: 
+    name = "NA"
+    predictions_names.append(name)
 
-# # Combine the paragraphs and labels to a dataframe 
-# df_predictions = pd.DataFrame({'Paragraph': par_list, 'Prediction': predictions_names})
+    # Combine the paragraphs and labels to a dataframe 
+    df_predictions = pd.DataFrame({'Paragraph': par_list, 'Prediction': predictions_names})
 
-# # Drop all "Other" and "NA" predictions
-# filtered_df = df[df['Prediction'].isin(['Other', 'NA'])]
+    # Drop all "Other" and "NA" predictions
+    filtered_df = df[df['Prediction'].isin(['Other', 'NA'])]
 
 
-# #####################################
-# st.write(df)  
+    #####################################
+    st.write(df)