Update app.py

app.py

@@ -110,20 +110,20 @@ def _inference_classifier(text):
 
     return sigmoid(ort_outs[0])
 
-def inference(file_in,file_col_name,input_batch,isurl,use_archive,limit_companies=10):
+def inference(input_batch,isurl,use_archive,limit_companies=10):
     input_batch_content = []
-    if file_in.name is not "":
-        print("[i] Input is file:",file_in.name)
-        dft = pd.read_csv(
-                file_in.name,
-                compression=dict(method='zip')
-              )
-        assert file_col_name in dft.columns, "Indicated col_name not found in file"
-        input_batch_r = dft[file_col_name].values.tolist()
-    else:
-        print("[i] Input is list")
-        assert len(input_batch) > 0, "input_batch array is empty"
-        input_batch_r = input_batch
+#    if file_in.name is not "":
+#        print("[i] Input is file:",file_in.name)
+#        dft = pd.read_csv(
+#                file_in.name,
+#                compression=dict(method='zip')
+#              )
+#        assert file_col_name in dft.columns, "Indicated col_name not found in file"
+#        input_batch_r = dft[file_col_name].values.tolist()
+#    else:
+    print("[i] Input is list")
+    assert len(input_batch) > 0, "input_batch array is empty"
+    input_batch_r = input_batch
  
     print("[i] Input size:",len(input_batch_r))
     
@@ -177,7 +177,7 @@ API input parameters:
 - `limit_companies`: integer. Number of found relevant companies to report.
 
 """
-examples = [["","",[['https://www.bbc.com/news/uk-62732447'],
+examples = [[ [['https://www.bbc.com/news/uk-62732447'],
             ['https://www.bbc.com/news/business-62747401'],
             ['https://www.bbc.com/news/technology-62744858'],
             ['https://www.bbc.com/news/science-environment-62758811'],
@@ -187,9 +187,7 @@ examples = [["","",[['https://www.bbc.com/news/uk-62732447'],
             ['https://www.bbc.com/news/business-62728621'],
             ['https://www.bbc.com/news/science-environment-62680423']],'url',False,5]]
 demo = gr.Interface(fn=inference, 
-                    inputs=[gr.File(label='zipped csv file'),
-                            gr.Textbox(label='If csv, column header name that contains the relevant data:'),
-                            gr.Dataframe(label='input batch', col_count=1, datatype='str', type='array', wrap=True),
+                    inputs=[gr.Dataframe(label='input batch', col_count=1, datatype='str', type='array', wrap=True),
                             gr.Dropdown(label='data type', choices=['text','url'], type='index', value='url'),
                             gr.Checkbox(label='if url parse cached in archive.org'),
                             gr.Slider(minimum=1, maximum=10, step=1, label='Limit NER output', value=5)],