added better app for chat history - trialapp.py

trialapp.py

@@ -0,0 +1,139 @@
+# Importing libraries
+import streamlit as st
+from langchain.chains.history_aware_retriever import create_history_aware_retriever
+from langchain.chains.retrieval import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_groq import ChatGroq
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# API and model settings
+os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN')
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# Streamlit app
+st.title("PDFSense : PDF Question and Answering with Session Chat History")
+st.write("Upload PDFs and ask questions related to the content of the PDFs.")
+llm = ChatGroq(model="Gemma2-9b-It")
+session_id = st.text_input("Session ID", value="common_session")
+
+# Manage chat history
+if 'store' not in st.session_state:
+    st.session_state.store = {}
+
+# Upload files and document loading
+uploaded_files = st.file_uploader("Drop the PDF files here", type="pdf", accept_multiple_files=True)
+
+if uploaded_files:
+    documents = []
+    for uploaded_file in uploaded_files:
+        temppdf = f"./temp.pdf"
+        with open(temppdf, "wb") as file:
+            file.write(uploaded_file.getvalue())
+        docs = PyPDFLoader(temppdf).load()
+        documents.extend(docs)
+
+    # Delete the temp file as we no longer need it
+    if os.path.exists("./temp.pdf"):
+        os.remove("./temp.pdf")
+
+    # Text splitting and embedding, storing in FAISS index
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    splits = text_splitter.split_documents(documents)
+    faiss_index = FAISS.from_documents(splits, embeddings)
+    retriever = faiss_index.as_retriever()
+
+    # Prompts
+    context_system_prompt = (
+        "Given a chat history and the latest user question, "
+        "which might reference context in the chat history, "
+        "formulate a standalone question that can be understood "
+        "without the chat history. Do not answer the question, "
+        "just reformulate it if needed and otherwise return it as it is."
+    )
+    context_prompt = ChatPromptTemplate.from_messages([
+        ("system", context_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}")
+    ])
+
+    history_aware_ret = create_history_aware_retriever(llm, retriever, context_prompt)
+
+    system_prompt = (
+        "You are 'PDFSense', a PDF reading and answering assistant. "
+        "Use the following pieces of retrieved context to answer "
+        "the question. If you don't know the answer, say that you don't know. "
+        "Answer the questions nicely."
+        "\n\n"
+        "{context}"
+    )
+
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}")
+    ])
+
+    # Chain for the chatbot
+    qa_chain = create_stuff_documents_chain(llm, prompt)
+    rag_chain = create_retrieval_chain(history_aware_ret, qa_chain)
+
+    # Session ID storing in chat history
+    def get_session_history(session: str) -> BaseChatMessageHistory:
+        if session_id not in st.session_state.store:
+            st.session_state.store[session_id] = ChatMessageHistory()
+        return st.session_state.store[session_id]
+
+    # RAG with history
+    conversation_rag = RunnableWithMessageHistory(
+        rag_chain,
+        get_session_history,
+        input_messages_key="input",
+        history_messages_key="chat_history",
+        output_messages_key="answer"
+    )
+
+    user_input = st.text_input("Enter your question")
+    if user_input:
+        session_history = get_session_history(session_id)
+        response = conversation_rag.invoke(
+            {"input": user_input},
+            config={
+                "configurable": {"session_id": session_id}
+            },
+        )
+
+        # Display the chat history
+        st.write("### Chat History")
+        for message in session_history.messages:
+            if isinstance(message, dict):  # Handle cases where messages might be dictionaries
+                role = message.get("role", "user")  # Default role is 'user'
+                content = message.get("content", "")
+            else:
+                # For LangChain message objects
+                role = "user" if isinstance(message, ChatMessageHistory) else "assistant"
+                content = message.content
+
+            if role == "user":
+                with st.chat_message("user"):
+                    st.write(content)
+            elif role == "assistant":
+                with st.chat_message("assistant"):
+                    st.write(content)
+            elif role == "system":
+                with st.chat_message("system"):
+                    st.markdown(f"**System Message:** {content}")
+
+        #st.write("Assistant:", response['answer'])