update

app.py

@@ -1,9 +1,56 @@
+import os
 import streamlit as st
-from transformers import pipeline
+from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, ServiceContext
+from llama_index.llm_predictor.chatgpt import ChatGPTLLMPredictor
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter some text')
 
-if text:
-    out = pipe(text)
-    st.json(out)
+index_name = "./index.json"
+documents_folder = "./documents"
+
+
+@st.cache_resource
+def initialize_index(index_name, documents_folder):
+    llm_predictor = ChatGPTLLMPredictor()
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
+    if os.path.exists(index_name):
+        index = GPTSimpleVectorIndex.load_from_disk(index_name, service_context=service_context)
+    else:
+        documents = SimpleDirectoryReader(documents_folder).load_data()
+        index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+        index.save_to_disk(index_name)
+
+    return index
+
+
+@st.cache_data(max_entries=200, persist=True)
+def query_index(_index, query_text):
+    response = _index.query(query_text)
+    return str(response)
+
+
+st.title("🦙 Llama Index Demo 🦙")
+st.header("Welcome to the Llama Index Streamlit Demo")
+st.write("Enter a query about Paul Graham's essays. You can check out the original essay [here](https://raw.githubusercontent.com/jerryjliu/llama_index/main/examples/paul_graham_essay/data/paul_graham_essay.txt). Your query will be answered using the essay as context, using embeddings from text-ada-002 and LLM completions from ChatGPT. You can read more about Llama Index and how this works in [our docs!](https://gpt-index.readthedocs.io/en/latest/index.html)")
+
+index = None
+api_key = st.text_input("Enter your OpenAI API key here:", type="password")
+if api_key:
+    os.environ['OPENAI_API_KEY'] = api_key
+    index = initialize_index(index_name, documents_folder)    
+
+
+if index is None:
+    st.warning("Please enter your api key first.")
+
+text = st.text_input("Query text:", value="What did the author do growing up?")
+
+if st.button("Run Query") and text is not None:
+    response = query_index(index, text)
+    st.markdown(response)
+    
+    llm_col, embed_col = st.columns(2)
+    with llm_col:
+        st.markdown(f"LLM Tokens Used: {index.service_context.llm_predictor._last_token_usage}")
+    
+    with embed_col:
+        st.markdown(f"Embedding Tokens Used: {index.service_context.embed_model._last_token_usage}")