Update app.py

app.py

@@ -3,8 +3,8 @@ import gradio as gr
 from langchain.vectorstores import Chroma
 from transformers import RagTokenizer, RagSequenceForGeneration
 from sentence_transformers import SentenceTransformer
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import HuggingFaceLLM
+from langchain import LLMChain, PromptTemplate
+from langchain.llms import HuggingFacePipeline
 
 #Konstanten
 ANTI_BOT_PW = os.getenv("CORRECT_VALIDATE")
@@ -22,9 +22,8 @@ model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", use
 # Verbindung zur Chroma DB und Laden der Dokumente
 chroma_db = Chroma(embedding_model=embedding_model, persist_directory = PATH_WORK + CHROMA_DIR)
 
-# Erstellung eines HuggingFaceLLM Modells
-llm = HuggingFaceLLM(model=model, tokenizer=tokenizer)
-
+# Erstellung eines HuggingFacePipeline LLM Modells
+llm = HuggingFacePipeline(pipeline=model)
 
 
 # Erstellen eines eigenen Retrievers mit Chroma DB und Embeddings
@@ -59,7 +58,9 @@ def get_rag_response(question):
     links = [doc.get('url', 'No URL available') for doc in docs]
 
     # Generieren der Antwort
-    answer = llm(question, docs)
+    prompt_template = PromptTemplate(input_variables=["context", "question"], template="{context}\n\n{question}")
+    prompt = prompt_template(context=" ".join(passages), question=question)
+    answer = llm(prompt)
     
     # Zusammenstellen der Ausgabe
     response = {
@@ -71,16 +72,18 @@ def get_rag_response(question):
 
 	
 	
-def chatbot_response (user_input, chat_history=[]):
-	response = get_rag_response(user_input)
-	answer = response['answer']
-	documents = response['documents']
-	doc_links = "\n\n".join([f"Link: {doc['link']} \nAuszüge der Dokumente: {doc['passage']}" for doc in documents])
-	bot_response = f"{answer} \n\nRelevante Dokumente: \n{doc_links}"
-	
-	chat_history.append((user_inptu, bot_response))
-	
-	return chat_history, chat_history
+# Funktion, die für den Chatbot genutzt wird
+def chatbot_response(user_input, chat_history=[]):
+    response = get_rag_response(user_input)
+    answer = response['answer']
+    documents = response['documents']
+    
+    doc_links = "\n\n".join([f"Link: {doc['link']}\nPassage: {doc['passage']}" for doc in documents])
+    
+    bot_response = f"{answer}\n\nRelevant Documents:\n{doc_links}"
+    
+    chat_history.append((user_input, bot_response))
+    return chat_history, chat_history
 	
 	
 #############################