best UI added for huggingface space

app.py

@@ -4,11 +4,8 @@ from langchain.chains.history_aware_retriever import create_history_aware_retrie
 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
@@ -17,125 +14,77 @@ 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.markdown('####')
-st.write("Upload PDFs and ask questions related to the content of the PDFs.")
+# Embeddings and LLM initialization
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 llm = ChatGroq(model="Gemma2-9b-It")
-session_id = st.text_input("Session ID", value="common_session")
+st.set_page_config(page_title="PDFSense", page_icon="📄")
+# Streamlit app title
+st.title("📄 PDFSense: PDF Question Answering with Chat History")
+
+# PDF Uploader Section (Keeps it at the top)
+uploaded_files = st.file_uploader("Drop PDF files here", type="pdf", accept_multiple_files=True)
 
-# Manage chat history
-if 'store' not in st.session_state:
-    st.session_state.store = {}
-st.markdown('####')
-# Upload files and document loading
-uploaded_files = st.file_uploader("Drop the PDF files here", type="pdf", accept_multiple_files=True)
-st.markdown('####')
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Hi! I am PDFSense. Upload your PDF and ask me anything related to it."}
+    ]
+
+# Process PDFs if uploaded
 if uploaded_files:
     documents = []
     for uploaded_file in uploaded_files:
-        temppdf = f"./temp.pdf"
+        temppdf = "./temp.pdf"
         with open(temppdf, "wb") as file:
             file.write(uploaded_file.getvalue())
         docs = PyPDFLoader(temppdf).load()
         documents.extend(docs)
+    os.remove("./temp.pdf")  # Clean up temporary file
 
-    # 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 splitting and FAISS index creation
     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."
-    )
+    # History-aware retriever and prompt setup
     context_prompt = ChatPromptTemplate.from_messages([
-        ("system", context_system_prompt),
+        ("system", "Refactor the question using chat history for context."),
         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}"
+        "You are PDFSense, a PDF reading assistant. Use the following context to answer the question: "
+        "{context}. If unsure, respond with 'I don't know.'"
     )
-
     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}
-            },
-        )
-
-        st.write("### Response")
-        st.success(response['answer'])
-        # 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.success(content)
-            elif role == "assistant":
-                with st.chat_message("assistant"):
-                    st.success(content)
-            elif role == "system":
-                with st.chat_message("system"):
-                    st.markdown(f"**System Message:** {content}")
-        #st.write("Assistant:", response['answer'])
+# Display chat history
+for msg in st.session_state["messages"]:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+# User input handling
+if user_input := st.chat_input(placeholder="Ask a question about your uploaded PDF..."):
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+    st.chat_message("user").write(user_input)
+
+    # Run retrieval and answer generation using invoke()
+    with st.chat_message("assistant"):
+        chat_history = [{"role": msg["role"], "content": msg["content"]} for msg in st.session_state["messages"]]
+        result = rag_chain.invoke({"input": user_input, "chat_history": chat_history})
+        
+        # Extract and display only the answer
+        answer = result.get("answer", "I don't know.")
+        st.session_state["messages"].append({"role": "assistant", "content": answer})
+        st.write(answer)

trial.py

@@ -1,117 +0,0 @@
-# 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
-from langchain_core.output_parsers import StrOutputParser
-import os
-from dotenv import load_dotenv
-load_dotenv()
-
-# API and model setting
-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 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 documents 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())
-            file_name=uploaded_file.name
-        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 and storing in chromadb
-    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 latest user question"
-        "which might reference context in the chat history, "
-        "formulate a standalone question which 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 dont 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 question")
-    if user_input:
-        session_history=get_session_history(session_id)
-        response=conversation_rag.invoke(
-            {"input":user_input},
-            config={
-                "configurable":{"session_id":session_id}
-            },
-        )
-        st.write(st.session_state.store)
-        st.write("Assistant:",response['answer'])
-        st.write("Chat History",session_history.messages)