Update app.py

app.py

@@ -7,7 +7,6 @@ from langchain_community.vectorstores import FAISS
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from transformers import pipeline  # Hugging Face pipeline for using T5 model
-import os
 
 
 # Access Hugging Face API token from Streamlit secrets
@@ -42,10 +41,15 @@ def get_vectorstore(text_chunks):
     return vectorstore
 
 
-# Function to create the conversation chain using T5 from Hugging Face API
+# Function to create the conversation chain using a smaller model
 def get_conversation_chain(vectorstore):
-    # Use HuggingFacePipeline with a Hugging Face T5 model
-    t5_model = pipeline("text2text-generation", model="google/t5-large", tokenizer="google/t5-large")
+    # Use HuggingFacePipeline with a smaller model like `t5-small`
+    t5_model = pipeline(
+        "text2text-generation",
+        model="t5-small",  # Smaller model for low-end systems
+        tokenizer="t5-small",
+        use_auth_token=hf_token  # Automatically fetches the token from Streamlit secrets
+    )
     
     llm = HuggingFacePipeline(pipeline=t5_model)