Update app.py

app.py

@@ -55,7 +55,7 @@ CHROMA_PDF = './chroma/kkg/pdf'
 CHROMA_WORD = './chroma/kkg/word'
 CHROMA_EXCEL = './chroma/kkg/excel'
 #HuggingFace Model name--------------------------------
-MODEL_NAME_HF  = "t5-small" #"meta-llama/Meta-Llama-3-8B-Instruct" #"mistralai/Mistral-7B-Instruct-v0.3" #"microsoft/Phi-3-mini-4k-instruct" #"HuggingFaceH4/zephyr-7b-alpha" 
+MODEL_NAME_HF  = "dbmdz/bert-base-german-cased" #"meta-llama/Meta-Llama-3-8B-Instruct" #"mistralai/Mistral-7B-Instruct-v0.3" #"microsoft/Phi-3-mini-4k-instruct" #"HuggingFaceH4/zephyr-7b-alpha" 
 
 #HuggingFace Reop ID--------------------------------
 #repo_id = "meta-llama/Llama-2-13b-chat-hf"   
@@ -247,7 +247,7 @@ def generate_text (prompt, chatbot, history, vektordatenbank, retriever, top_p=0
         #3.te Alternative für pipeline
         # Erstelle eine Pipeline mit den gewünschten Parametern
         #llm = pipeline("text-generation", model=MODEL_NAME_HF, config={"temperature": 0.5, "max_length": 1024, "num_return_sequences": 1, "top_k": top_k, "top_p": top_p, "repetition_penalty": repetition_penalty}, trust_remote_code=True)
-        llm = pipeline("text2text-generation", model=MODEL_NAME_HF, trust_remote_code=True)
+        llm = pipeline("summarization", model=MODEL_NAME_HF, trust_remote_code=True)
         result = rag_chain(llm, history_text_und_prompt, retriever)
            
     except Exception as e: