Update app.py

app.py

@@ -1,21 +1,4 @@
-import os
-from faiss import write_index
-import gradio as gr
-import numpy as np
-import torch 
-from tqdm import tqdm
-from torch.utils.data import DataLoader, Dataset
-from datasets import load_dataset
-import pandas as pd
-import faiss
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoModel
-from transformers import TextIteratorStreamer
-from threading import Thread
-
-torch.set_num_threads(2)
-HF_TOKEN = os.environ.get("SECRET_TOKEN")      
-
-
+    
 # OBTENER EL DATASET________________________________________________________________________________
 def get_medical_flashcards_dataset():
     """
@@ -337,7 +320,23 @@ def answer_query(query_text, index, documents, llm_model, llm_tokenizer, embeddi
 
 
 if __name__ == '__main__':
-
+    import os
+    from faiss import write_index
+    import gradio as gr
+    import numpy as np
+    import torch 
+    from tqdm import tqdm
+    from torch.utils.data import DataLoader, Dataset
+    from datasets import load_dataset
+    import pandas as pd
+    import faiss
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoModel
+    from transformers import TextIteratorStreamer
+    from threading import Thread
+    
+    torch.set_num_threads(2)
+    HF_TOKEN = os.environ.get("SECRET_TOKEN")  
+    
     class CFG:
         embedding_model = 'TimKond/S-PubMedBert-MedQuAD'
         batch_size = 128
@@ -382,3 +381,5 @@ if __name__ == '__main__':
                     description = "Gemma 2b Medical Chatbot is a chatbot that can help you with your medical queries. It is not a replacement for a doctor. Please consult a doctor for any medical advice.",
                     )
     demo.launch()
+
+