Update app.py

app.py

@@ -16,7 +16,8 @@ from langchain_community.document_loaders import PyPDFLoader,  UnstructuredWordD
 from langchain_community.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
 #from langchain.document_loaders import GenericLoader
 from langchain.schema import AIMessage, HumanMessage
-from langchain_community.llms import HuggingFaceHub
+#from langchain_community.llms import HuggingFaceHub
+from langchain_huggingface import HuggingFaceEndpoint
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFaceTextGenInference
 #from langchain_community.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
@@ -24,7 +25,7 @@ from langchain.prompts import PromptTemplate
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
 from chromadb.errors import InvalidDimensionException
-from langchain import HuggingFacePipeline
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 from transformers import pipeline
 from utils import *
 from beschreibungen import *
@@ -205,7 +206,7 @@ def generate_text (prompt, chatbot, history, vektordatenbank, retriever, top_p=0
         #oder an Hugging Face --------------------------
         print("HF Anfrage.......................")
         model_kwargs={"temperature": 0.5, "max_length": 512, "num_return_sequences": 1, "top_k": top_k, "top_p": top_p, "repetition_penalty": repetition_penalty}
-        llm = HuggingFaceHub(repo_id=repo_id, model_kwargs=model_kwargs) 
+        llm = HuggingFaceEndpoint(repo_id=repo_id, model_kwargs=model_kwargs) 
         #llm = HuggingFaceChain(model=MODEL_NAME_HF, model_kwargs={"temperature": 0.5, "max_length": 128}) 
         # Erstelle eine Pipeline mit den gewünschten Parametern
         #pipe = pipeline("text-generation", model=MODEL_NAME_HF , model_kwargs=model_kwargs)