Update run.py

run.py

@@ -5,18 +5,25 @@
 # Last update: October 15th, 2024
 #############################################################################
 
+import os
+import chromadb
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb.utils import embedding_functions
+import torch # chromaDB
+from transformers import AutoTokenizer, AutoModel # chromaDB
+from huggingface_hub import InferenceClient # Gradio-Interface
+import gradio as gr # Gradio-Interface
+import json # Gradio-Interface
+
 dbPath="/home/af/Schreibtisch/Code/gradio/BERUFENET/db" 
 if(os.path.exists(dbPath)==False): dbPath="/home/user/app/db"
 
 print(dbPath)
 
+
 # Chroma-DB
 #-----------
 
-import chromadb
-from chromadb import Documents, EmbeddingFunction, Embeddings
-import torch # chromaDB
-from transformers import AutoTokenizer, AutoModel # chromaDB
 jina = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
 #jira.save_pretrained("jinaai_jina-embeddings-v2-base-de")   
 device='cuda:0' if torch.cuda.is_available() else 'cpu'
@@ -33,14 +40,12 @@ client = chromadb.PersistentClient(path=path)
 print(client.heartbeat()) 
 print(client.get_version())  
 print(client.list_collections()) 
-from chromadb.utils import embedding_functions
 #default_ef = embedding_functions.DefaultEmbeddingFunction()
 #sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
 #instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
 jina_ef=JinaEmbeddingFunction()
 embeddingFunction=jina_ef
 print(str(client.list_collections()))
-
   
 global collection
 if("name=BerufenetDB1" in str(client.list_collections())): 
@@ -54,10 +59,6 @@ print(collection.count())
 # Gradio-GUI
 #------------
 
-from huggingface_hub import InferenceClient
-import gradio as gr
-import json
-
 myModel="mistralai/Mixtral-8x7B-Instruct-v0.1"
 
 def format_prompt(message, history):