Main streamlit app

app.py

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+import os
+import streamlit as st
+from llama_index.llms.replicate import Replicate
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from llama_index.core.settings import Settings
+from langchain.embeddings.huggingface import HuggingFaceBgeEmbeddings
+
+
+os.environ["REPLICATE_API_TOKEN"] = st.secrets.replicate_key
+
+st.header("Why Can't We All Just Get Along Chatbot")
+
+@st.cache_resource(show_spinner=False)
+def load_data():
+    with st.spinner(text="Loading and indexing Henry and Fry's chapter – hang tight! This should take 1-2 minutes."):
+        llm = Replicate(model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5")
+        Settings.llm = llm
+
+        Settings.embed_model = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en")
+
+        documents = SimpleDirectoryReader("/Users/es26698/Documents/Book_Club/book_chapter/").load_data()
+
+        index = VectorStoreIndex.from_documents(documents,)
+
+        return index
+
+index = load_data()
+
+
+query_engine = index.as_query_engine()
+prompt = st.text_input('Enter your question here:', 'Your question...')
+
+if st.button('Submit Query'):
+	with st.spinner("Generating response... this may take a few minutes..."):
+		resp = query_engine.query(prompt)
+		st.write(resp.response)