Delete streamlit_app.py

streamlit_app.py

@@ -1,140 +0,0 @@
-import os
-
-# --- Fix for Hugging Face permission issue ---
-os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false"
-os.environ["STREAMLIT_HOME"] = "/tmp"
-os.makedirs("/tmp/.streamlit", exist_ok=True)
-with open("/tmp/.streamlit/config.toml", "w") as f:
-    f.write("[browser]\ngatherUsageStats = false\n[server]\nheadless = true\n")
-    
-#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
-## RAG Q&A Conversation With PDF Including Chat History
-import streamlit as st
-from langchain.chains import create_history_aware_retriever, create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_chroma import Chroma
-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_dotenv()
-
-os.environ['HF_TOKEN'] = os.getenv("HF_TOKEN")
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-
-## Set up Streamlit
-st.title("Conversational RAG With PDF uploads and chat history")
-st.write("Upload PDFs and chat with their content")
-
-## Input the Groq API Key
-api_key = st.text_input("Enter your Groq API key:", type="password")
-
-## Check if Groq API key is provided
-if api_key:
-    llm = ChatGroq(groq_api_key=api_key, model_name="Gemma2-9b-It")
-
-    ## Chat interface
-    session_id = st.text_input("Session ID", value="default_session")
-
-    ## Statefully manage chat history
-    if 'store' not in st.session_state:
-        st.session_state.store = {}
-
-    uploaded_files = st.file_uploader("Choose a PDF file", type="pdf", accept_multiple_files=True)
-
-    ## Process uploaded PDFs
-    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
-
-            loader = PyPDFLoader(temppdf)
-            docs = loader.load()
-            documents.extend(docs)
-
-        # Split and create embeddings for the documents
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
-        splits = text_splitter.split_documents(documents)
-        vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
-        retriever = vectorstore.as_retriever()
-
-        contextualize_q_system_prompt = (
-            "Given a chat history and the 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 is."
-        )
-        contextualize_q_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", contextualize_q_system_prompt),
-                MessagesPlaceholder("chat_history"),
-                ("human", "{input}"),
-            ]
-        )
-
-        history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
-
-        ## Answer question
-        system_prompt = (
-            "You are an assistant for question-answering tasks. "
-            "Use the following pieces of retrieved context to answer "
-            "the question. If you don't know the answer, say that you "
-            "don't know. Use three sentences maximum and keep the "
-            "answer concise."
-            "\n\n"
-            "{context}"
-        )
-        qa_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", system_prompt),
-                MessagesPlaceholder("chat_history"),
-                ("human", "{input}"),
-            ]
-        )
-
-        question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-        rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
-
-        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]
-
-        conversational_rag_chain = RunnableWithMessageHistory(
-            rag_chain,
-            get_session_history,
-            input_messages_key="input",
-            history_messages_key="chat_history",
-            output_messages_key="answer"
-        )
-
-        user_input = st.text_input("Your question:")
-        if user_input:
-            session_history = get_session_history(session_id)
-            response = conversational_rag_chain.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)
-else:
-    st.warning("Please enter the Groq API Key")
-
-
-
-
-