Update app.py

app.py

@@ -13,13 +13,18 @@ def read_pdf(pdf_path):
     return content
 
 # Process and retrieve answers
-def process_invoice(file, hf_token, questions):
+def process_invoice(file, questions):
     try:
         # Read the PDF content directly
         print("Reading PDF content...")
         pdf_content = read_pdf(file.name)
         print(f"PDF Content: {pdf_content[:500]}...")  # Print first 500 characters for verification
 
+        # Get the Hugging Face token from environment variables
+        hf_token = os.getenv("HF_TOKEN")
+        if not hf_token:
+            raise ValueError("Hugging Face token not found in environment variables.")
+
         # Initialize the Hugging Face pipeline
         print("Initializing the Hugging Face pipeline...")
         qa_pipeline = pipeline("question-answering", model="mistralai/Mixtral-8x7B-Instruct-v0.1", use_auth_token=hf_token)
@@ -37,20 +42,19 @@ def process_invoice(file, hf_token, questions):
         return {"error": str(e)}
 
 # Gradio interface
-def gradio_interface(file, hf_token, questions):
-    answers = process_invoice(file, hf_token, questions)
+def gradio_interface(file, questions):
+    answers = process_invoice(file, questions)
     return answers
 
 interface = gr.Interface(
     fn=gradio_interface,
     inputs=[
         gr.File(file_count="single", label="Upload Invoice (PDF)"),
-        gr.Textbox(type="password", label="Enter your Hugging Face Token"),
         gr.Textbox(lines=5, placeholder="Enter your questions separated by commas")
     ],
     outputs="json",
     title="Invoice Data Extraction",
-    description="Upload an invoice PDF, provide your Hugging Face token, and get the extracted data based on your questions."
+    description="Upload an invoice PDF and get the extracted data based on your questions."
 )
 
 if __name__ == "__main__":