Update app.py

app.py

@@ -1,81 +1,87 @@
 import os
 from getpass import getpass
-import gradio as gr
-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
+import streamlit as st
+from dotenv import load_dotenv
+from openai.embeddings_utils import OpenAIEmbeddings
+from openai import OpenAI
+from pinecone import PineconeClient, VectorStore
+from faiss import IndexFlatL2
+from llama_index import VectorStoreIndex, VectorIndexRetriever
 from llama_index.node_parser import SemanticSplitterNodeParser
 from llama_index.embeddings import OpenAIEmbedding
 from llama_index.ingestion import IngestionPipeline
 from llama_index.query_engine import RetrieverQueryEngine
+from llama_index.memory import ConversationBufferMemory
+from llama_index.chains import ConversationalRetrievalChain
+from llama_index.prompts import user_template, bot_template, css
 
-
-
-pinecone_api_key = os.getenv("PINECONE_API_KEY") 
+# Load environment variables
+load_dotenv()
+pinecone_api_key = os.getenv("PINECONE_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")
+index_name = os.getenv("INDEX_NAME")
 
+# Initialize OpenAI and Pinecone clients
+openai.api_key = openai_api_key
+pinecone_client = PineconeClient(api_key=pinecone_api_key)
+pinecone_index = pinecone_client.Index(index_name)
+vector_store = VectorStore(pinecone_index=pinecone_index)
 
-# This will be the model we use both for Node parsing and for vectorization
+# Initialize LlamaIndex components
 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,
-        ],
-    )
-
-
-
-# Initialize connection to Pinecone
-pc = PineconeGRPC(api_key=pinecone_api_key)
-index_name = os.getenv("INDEX_NAME")
-
-# Initialize your index
-pinecone_index = pc.Index(index_name)
-
-# Initialize VectorStore
-vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
-
-pinecone_index.describe_index_stats()
-
-
-# Due to how LlamaIndex works here, if your Open AI API key was
-# not set to an environment variable before, you have to set it at this point
-if not os.getenv('OPENAI_API_KEY'):
-    os.environ['OPENAI_API_KEY'] = openai_api_key
-
-# Instantiate VectorStoreIndex object from our vector_store object
+    ],
+)
 vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
-
-# Grab 5 search results
 retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
-
-
-# Pass in your retriever from above, which is configured to return the top 5 results
 query_engine = RetrieverQueryEngine(retriever=retriever)
 
-# Define the function to handle user input and return the query response
-def query_annual_report(summary_request):
-    llm_query = query_engine.query(summary_request)
-    return llm_query.response
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=query_annual_report,
-    inputs="text",
-    outputs="text",
-    title="Annual Report Summary Query",
-    description="Enter your query to get the summary of the annual report."
-)
-
-# Launch the Gradio app
-iface.launch()
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    llm = OpenAI()
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
+    return conversation_chain
+
+def handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+    
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with Annual Reports", page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    st.header("Chat with Annual Report Documents")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+if __name__ == "__main__":
+    main()