Upload app.py

app.py

@@ -63,7 +63,7 @@ iface1 = gr.Interface(
 
     This app has two analysis modules summarised below:
     - Single-Text-Prediction - Analyses text pasted in a text box and return IEQ prediction.
-    - Multi-Text-Prediction - Analyses multiple rows of texts in an uploaded CSV file and returns a downloadable CSV file with IEQ prediction for each row of text.
+    - Multi-Text-Prediction - Analyses multiple rows of texts in an uploaded CSV or Excell file and returns a downloadable CSV file with IEQ prediction for each row of text.
 
     This app runs on a free server and may therefore not be suitable for analysing large CSV files. 
     If you need assistance with analysing large CSV, do get in touch using the contact information in the Contact section.
@@ -72,7 +72,7 @@ iface1 = gr.Interface(
     <p>We would be happy to receive your feedback regarding this app. If you would also like to collaborate with us to explore some use cases for the model 
     powering this app, we are happy to hear from you.</p>
 
-    Dr Abdul-Manan Sadick - s.sadick@deakin.edu.au
+    Dr Abdul-Manan Sadick - s.sadick@deakin.edu.au\n
     Dr Giorgia Chinazzo - giorgia.chinazzo@northwestern.edu
     ''')
 
@@ -189,8 +189,13 @@ def predict_from_csv(file, column_name, progress=gr.Progress()):
         fig (plotly.graph_objs.Figure): A histogram showing the frequency of each IEQ label.
         output_csv (gr.File): A downloadable CSV file containing the predictions.
     """
-    # Read the CSV file
-    df_docs = pd.read_csv(file)
+    # Read the CSV or Excel file
+    if file.endswith('.csv'):
+        df_docs = pd.read_csv(file)
+    elif file.endswith('.xls') or file.endswith('.xlsx'):
+        df_docs = pd.read_excel(file)
+    else:
+        raise gr.Error("Invalid file type. Please upload a CSV or Excel file.")
 
     # Check if the specified column exists
     if column_name not in df_docs.columns:
@@ -349,7 +354,7 @@ def predict_from_csv(file, column_name, progress=gr.Progress()):
 
 
 # Define the input component
-file_input = gr.File(label="Upload CSV file here", show_label=True, file_types=[".csv"])
+file_input = gr.File(label="Upload CSV or Excel file here", show_label=True, file_types=[".csv", ".xls", ".xlsx"])
 column_name_input = gr.Textbox(label="Enter the column name containing the text to be analyzed", show_label=True)
 
 # Create the Gradio interface
@@ -357,12 +362,12 @@ iface3 = gr.Interface(fn=predict_from_csv,
                       inputs=[file_input, column_name_input],
                       outputs=[gr.Plot(label='Frequency of IEQs', show_label=True),
                                gr.File(label='Download output CSV', show_label=True)],
-                      title="Multi-text Prediction (CVS)",
+                      title="Multi-text Prediction",
                       description='**NOTE:** Please enter the column name containing the text to be analyzed.')
 
 # Create a tabbed interface
 demo = gr.TabbedInterface(interface_list=[iface1, iface2, iface3],
-                          tab_names=["General-App-Info", "Single-Text-Prediction", "Multi-Text-Prediction (CSV)"],
+                          tab_names=["General-App-Info", "Single-Text-Prediction", "Multi-Text-Prediction"],
                           title="Indoor Environmetal Quality (IEQ) Text Classifier App",
                           theme='soft'
                           )