Update app.py

app.py

@@ -5,9 +5,14 @@ from transformers import pipeline
 import pandas as pd
 from huggingface_hub import login
 
-# Log in to Hugging Face using your API token (if needed for private models)
-# You can generate an API token from https://huggingface.co/settings/tokens
-login(token="your_huggingface_token")
+# Function to login using Hugging Face API token
+def login_with_token(hf_token):
+    """Login to Hugging Face using provided token"""
+    try:
+        login(token=hf_token)
+        return "Logged in successfully!"
+    except Exception as e:
+        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
@@ -68,10 +73,33 @@ def batch_process_resumes(pdf_files):
 # Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("### AI Resume Parser")
+    
+    # User input for Hugging Face token
+    hf_token_input = gr.Textbox(label="Hugging Face Token", placeholder="Enter your Hugging Face API Token here")
+    
+    # File input for resume files
     file_input = gr.File(file_count="multiple", label="Upload Resumes (PDFs)")
+    
+    # Output for results
     output = gr.Textbox(label="Result")
+    
+    # Process button that triggers the login and resume parsing
     process_button = gr.Button("Process Resumes")
     
-    process_button.click(batch_process_resumes, inputs=file_input, outputs=output)
+    # Function call when button is clicked
+    def process_resumes(hf_token, pdf_files):
+        # Attempt to log in with provided token
+        login_message = login_with_token(hf_token)
+        
+        # If login is successful, process resumes
+        if "Error" not in login_message:
+            result_message = batch_process_resumes(pdf_files)
+            return login_message + "\n" + result_message
+        else:
+            return login_message
+
+    # Set up the button click event
+    process_button.click(process_resumes, inputs=[hf_token_input, file_input], outputs=output)
 
+# Launch the Gradio interface
 demo.launch()