Update app.py

app.py

@@ -7,16 +7,17 @@ from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models.gigachat import GigaChat 
 from htmlTemplates import css, bot_template, user_template
 from langchain.llms import HuggingFaceHub, LlamaCpp
 from huggingface_hub import snapshot_download, hf_hub_download
 
 # from prompts import CONDENSE_QUESTION_PROMPT
 
-repo_name = "IlyaGusev/saiga_mistral_7b_gguf"
-model_name = "model-q4_K.gguf"
+#repo_name = "IlyaGusev/saiga_mistral_7b_gguf"
+#model_name = "model-q4_K.gguf"
 
-snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
+#snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
 
 
 def get_pdf_text(pdf_docs):
@@ -51,17 +52,19 @@ def get_vectorstore(text_chunks):
 
 def get_conversation_chain(vectorstore, model_name):
     
-    llm = LlamaCpp(model_path=model_name,
-                   temperature=0.1,
-                   top_k=30,
-                   top_p=0.9,
-                   streaming=True,
-                   n_ctx=2048,
-                   n_parts=1,
-                   echo=True
-                   )
+    #llm = LlamaCpp(model_path=model_name,
+    #               temperature=0.1,
+    #               top_k=30,
+    #               top_p=0.9,
+    #               streaming=True,
+    #               n_ctx=2048,
+    #               n_parts=1,
+    #               echo=True
+    #               )
 
     #llm = ChatOpenAI()
+
+    llm = GigaChat(profanity=False, credentials='9d2e213f-a4a0-431a-a2ac-624fafe3970c')
     
     memory = ConversationBufferMemory(memory_key='chat_history',
                                       input_key='question',