Update app.py

app.py

@@ -16,7 +16,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 #from transformers import AutoModelForCausalLM
 
 
-access_token = os.getenv("HUGGINGFACE_API_KEY")
+#access_token = os.getenv("HUGGINGFACE_API_KEY")
 
 
 
@@ -66,7 +66,7 @@ def text_splitter(text):
 
 #GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 #COHERE_API_KEY = os.getenv("COHERE_API_KEY")
-HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+#HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
 
 def get_conversational_chain(retriever):
     prompt_template = """
@@ -82,10 +82,10 @@ def get_conversational_chain(retriever):
     Answer:
     """
     #model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, google_api_key=GOOGLE_API_KEY)
-    repo_id ='google/gemma-1.1-2b-it'
+    llm ='google/gemma-1.1-2b-it'
     #repo_id='meta-llama/Meta-Llama-3-70B'
     #repo_id = 'mistralai/Mixtral-8x7B-Instruct-v0.1'
-    llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.3,token = access_token)
+    #llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.3,token = access_token)
     #tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
     #llm = AutoModelForCausalLM.from_pretrained("google/gemma-1.1-2b-it")
     #llm = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct", trust_remote_code=True, token=access_token)