Update app.py

app.py

@@ -4,9 +4,22 @@ from langchain.text_splitter import CharacterTextSplitter
 import chromadb
 import chromadb.config
 from chromadb.config import Settings
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+import torch
+import gradio as gr
+import uuid
+from sentence_transformers import SentenceTransformer
+
+model_name = 'google/flan-t5-base'
+model = T5ForConditionalGeneration.from_pretrained(model_name, device_map='auto', offload_folder="offload")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+print('flan read')
 
 
-import gradio as gr
+ST_name = 'sentence-transformers/sentence-t5-base'
+st_model = SentenceTransformer(ST_name)
+print('sentence read')
+
 
 def get_context(query_text):
     query_emb = st_model.encode(query_text)
@@ -54,7 +67,6 @@ def upload_pdf(file):
     ids = [str(uuid.uuid1()) for _ in doc_emb]
 
     client = chromadb.Client()
-    # Create collection. get_collection, get_or_create_collection, delete_collection also available!
     collection = client.create_collection("test_db") 
     
     collection.add(
@@ -66,21 +78,10 @@ def upload_pdf(file):
     return 'hello'
 
 
-from transformers import T5ForConditionalGeneration, AutoTokenizer
-import torch
-
-
-model_name = 'google/flan-t5-base'
 
-model = T5ForConditionalGeneration.from_pretrained(model_name, device_map='auto', offload_folder="offload")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-import uuid
-from sentence_transformers import SentenceTransformer
 
-ST_name = 'sentence-transformers/sentence-t5-base'
 
-st_model = SentenceTransformer(ST_name)