Update app.py

app.py

@@ -4,6 +4,7 @@ import torch
 from transformers import pipeline
 import pandas as pd
 from huggingface_hub import login
+import os
 
 # Function to login using Hugging Face API token
 def login_with_token(hf_token):
@@ -15,7 +16,6 @@ def login_with_token(hf_token):
         return f"Error: {str(e)}"
 
 # Load the model for Named Entity Recognition (NER)
-# You can replace 'dbmdz/bert-large-cased-finetuned-conll03-english' with any other model if needed
 nlp = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", framework="pt")
 
 def extract_text_from_pdf(pdf_file):
@@ -68,9 +68,15 @@ def batch_process_resumes(pdf_files):
     
     # Convert to DataFrame
     df = pd.DataFrame(all_resumes)
+    
+    # Define the file path for the Excel file
+    output_file = "/tmp/parsed_resumes.xlsx"
+    
     # Save to Excel
-    df.to_excel("parsed_resumes.xlsx", index=False)
-    return "Excel file with parsed resumes has been saved as 'parsed_resumes.xlsx'."
+    df.to_excel(output_file, index=False)
+    
+    # Return the path to the file for download
+    return output_file
 
 # Gradio interface
 with gr.Blocks() as demo:
@@ -85,6 +91,9 @@ with gr.Blocks() as demo:
     # Output for results
     output = gr.Textbox(label="Result")
     
+    # File output for the download link
+    download_link = gr.File(label="Download Excel File", file_count=1)
+    
     # Process button that triggers the login and resume parsing
     process_button = gr.Button("Process Resumes")
     
@@ -93,15 +102,15 @@ with gr.Blocks() as demo:
         # Attempt to log in with provided token
         login_message = login_with_token(hf_token)
         
-        # If login is successful, process resumes
+        # If login is successful, process resumes and generate the download link
         if "Error" not in login_message:
-            result_message = batch_process_resumes(pdf_files)
-            return login_message + "\n" + result_message
+            excel_file_path = batch_process_resumes(pdf_files)
+            return login_message + "\nExcel file with parsed resumes is ready for download.", excel_file_path
         else:
-            return login_message
+            return login_message, None
 
     # Set up the button click event
-    process_button.click(process_resumes, inputs=[hf_token_input, file_input], outputs=output)
+    process_button.click(process_resumes, inputs=[hf_token_input, file_input], outputs=[output, download_link])
 
 # Launch the Gradio interface
 demo.launch()