Update app.py

app.py

@@ -1,92 +1,67 @@
 import os
-from getpass import getpass
-import gradio as gr
-import random
 import time
-
-pinecone_api_key = os.getenv("PINECONE_API_KEY") or getpass("Enter your Pinecone API Key: ")
-openai_api_key = os.getenv("OPENAI_API_KEY") or getpass("Enter your OpenAI API Key: ")
-
+import streamlit as st
+from getpass import getpass
+from openai import OpenAI
 from llama_index.node_parser import SemanticSplitterNodeParser
 from llama_index.embeddings import OpenAIEmbedding
 from llama_index.ingestion import IngestionPipeline
-
-# This will be the model we use both for Node parsing and for vectorization
-embed_model = OpenAIEmbedding(api_key=openai_api_key)
-
-# Define the initial pipeline
-pipeline = IngestionPipeline(
-    transformations=[
-        SemanticSplitterNodeParser(
-            buffer_size=1,
-            breakpoint_percentile_threshold=95,
-            embed_model=embed_model,
-        ),
-        embed_model,
-    ],
-)
-
 from pinecone.grpc import PineconeGRPC
-from pinecone import ServerlessSpec
-
 from llama_index.vector_stores import PineconeVectorStore
+from llama_index import VectorStoreIndex
+from llama_index.retrievers import VectorIndexRetriever
+from llama_index.query_engine import RetrieverQueryEngine
 
-# Initialize connection to Pinecone
-pc = PineconeGRPC(api_key=pinecone_api_key)
-index_name = "anualreport"
+# Set OpenAI API key from Streamlit secrets
+openai_api_key = st.secrets["OPENAI_API_KEY"]
+pinecone_api_key = st.secrets["PINECONE_API_KEY"]
 
-# Initialize your index
-pinecone_index = pc.Index(index_name)
+# Initialize OpenAI client
+client = OpenAI(api_key=openai_api_key)
 
-# Initialize VectorStore
+# Initialize Pinecone connection
+pc = PineconeGRPC(api_key=pinecone_api_key)
+index_name = "annualreport"
+pinecone_index = pc.Index(index_name)
 vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
 
-pinecone_index.describe_index_stats()
-
-from llama_index import VectorStoreIndex
-from llama_index.retrievers import VectorIndexRetriever
-
-# Set the OpenAI API key if not already set
-if not os.getenv('OPENAI_API_KEY'):
-    os.environ['OPENAI_API_KEY'] = openai_api_key
-
-# Instantiate VectorStoreIndex object from our vector_store object
+# Initialize vector index and retriever
 vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
-
-# Grab 5 search results
 retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
-
-from llama_index.query_engine import RetrieverQueryEngine
-
-# Pass in your retriever from above, which is configured to return the top 5 results
 query_engine = RetrieverQueryEngine(retriever=retriever)
 
-def query_anual_report(query):
+# Set up LlamaIndex embedding model and pipeline
+embed_model = OpenAIEmbedding(api_key=openai_api_key)
+pipeline = IngestionPipeline(
+    transformations=[
+        SemanticSplitterNodeParser(buffer_size=1, breakpoint_percentile_threshold=95, embed_model=embed_model),
+        embed_model,
+    ],
+)
+
+def query_annual_report(query):
     response = query_engine.query(query)
     return response.response
 
-# Define the chat functions
-def user(user_message, history):
-    return "", history + [[user_message, None]]
+# Streamlit app setup
+st.title("ChatGPT-like Clone with Pinecone Integration")
 
-def bot(history):
-    bot_message = query_anual_report(history[-1][0])
-    history[-1][1] = ""
-    for character in bot_message:
-        history[-1][1] += character
-        time.sleep(0.01)  # Reduced sleep time to make response appear faster
-        yield history
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
 
-# Define Gradio Blocks interface
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    clear = gr.Button("Clear")
+# Display chat messages from history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
 
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    clear.click(lambda: None, None, chatbot, queue=False)
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
 
-if __name__ == "__main__":
-    demo.launch()
+    with st.chat_message("assistant"):
+        response = query_annual_report(prompt)
+        st.markdown(response)
+    st.session_state.messages.append({"role": "assistant", "content": response})