Create app.py

app.py

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+import streamlit as st
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain_community.document_loaders import TextLoader
+
+import os
+os.environ["OPENAI_API_KEY"]=os.getenv('OPENAI_API_KEY')
+# Initialize the embeddings and model
+embd = OpenAIEmbeddings()
+llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+
+# Initialize conversation history
+if "conversation_history" not in st.session_state:
+    st.session_state.conversation_history = []
+
+# Define the Streamlit app
+st.title("Text File Question-Answering with History")
+st.subheader("Upload a text file and ask questions. The app will maintain a conversation history.")
+
+# File upload section
+uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
+
+if uploaded_file:
+    # Load and split the text file
+    text_loader = TextLoader(uploaded_file)
+    document = text_loader.load()
+    
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+    doc_splits = text_splitter.split_documents(document)
+    
+    # Create a vector store
+    vectorstore = Chroma.from_documents(
+        documents=doc_splits,
+        collection_name="conversation_history",
+        embedding=embd,
+    )
+    retriever = vectorstore.as_retriever()
+    
+    # Initialize the QA chain
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=retriever,
+        return_source_documents=True,
+    )
+
+    # Question-answering section
+    query = st.text_input("Ask a question:")
+    
+    if query:
+        result = qa_chain({"query": query})
+        answer = result["result"]
+        st.session_state.conversation_history.append((query, answer))
+        
+        # Display the current answer
+        st.write("**Answer:**", answer)
+        
+        # Display conversation history
+        st.subheader("Conversation History")
+        for idx, (q, a) in enumerate(st.session_state.conversation_history, 1):
+            st.write(f"**Q{idx}:** {q}")
+            st.write(f"**A{idx}:** {a}")
+