Update app.py

app.py

@@ -5,23 +5,23 @@ import re
 import gradio as gr
 import os
 import accelerate
-import spaces
+# import spaces
 import subprocess
-from huggingface_hub import hf_hub_download, InferenceClient
-from llama_cpp import Llama
+# from huggingface_hub import hf_hub_download, InferenceClient
+# from llama_cpp import Llama
 
-from huggingface_hub import login
-login(token = os.getenv('HF_TOKEN'))
+# from huggingface_hub import login
+# login(token = os.getenv('HF_TOKEN'))
 
-repo_id = "srijaydeshpande/Deid-Fine-Tuned"
-model_id = "deid_finetuned.Q4_K_M.gguf"
+# repo_id = "srijaydeshpande/Deid-Fine-Tuned"
+# model_id = "deid_finetuned.Q4_K_M.gguf"
 
 
-hf_hub_download(
-    repo_id="srijaydeshpande/Deid-Fine-Tuned",
-    filename="deid_finetuned.Q4_K_M.gguf",
-    local_dir = "./models"
-)
+# hf_hub_download(
+#     repo_id="srijaydeshpande/Deid-Fine-Tuned",
+#     filename="deid_finetuned.Q4_K_M.gguf",
+#     local_dir = "./models"
+# )
 
 def process_document(pdf_path):
     extracted_pages = extract_pages(pdf_path)
@@ -142,7 +142,7 @@ def deidentify_doc(llm_type, pdftext, maxtokens, temperature, top_probability):
             return response
         
 
-@spaces.GPU(duration=80)
+# @spaces.GPU(duration=80)
 def pdf_to_text(files, llm_type, maxtokens=2048, temperature=0, top_probability=0.95):
     files=[files]
     for file in files:
@@ -158,12 +158,12 @@ def pdf_to_text(files, llm_type, maxtokens=2048, temperature=0, top_probability=
             for page_id in page2content:
                 pdftext = page2content[page_id]
                 original_pdf_text += pdftext + '\n'
-                response_generator = deidentify_doc(llm_type, pdftext, maxtokens, temperature, top_probability)
-                print('RESPONSE GENERATOR IS ',response_generator)
+                # response_generator = deidentify_doc(llm_type, pdftext, maxtokens, temperature, top_probability)
+                # print('RESPONSE GENERATOR IS ',response_generator)
                 # for chunk in response_generator:
                 #     accumulated_text += chunk
                 #     yield accumulated_text  # Keep updating output
-                return response_generator # + "\n\n"
+                # return response_generator # + "\n\n"
             print('Extracted Page Content Is ', original_pdf_text)
             print('------------------------------------------------------------')
     # return anonymized_text