Update app.py

app.py

@@ -16,59 +16,68 @@ from utills import load_txt_documents, split_docs, load_uploaded_documents, retr
 from langchain.text_splitter import TokenTextSplitter, RecursiveCharacterTextSplitter
 from langchain_community.document_loaders.directory import DirectoryLoader
 
-script_dir = os.path.dirname(os.path.abspath(__file__))
-data_path = os.path.join(script_dir, "data/")
-model_path = os.path.join(script_dir, 'qwen2-0_5b-instruct-q4_0.gguf')
-store = {}
+def main():
+    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
+    st.header("Chat with multiple PDFs :books:")
 
-model_name = "sentence-transformers/all-mpnet-base-v2"
-model_kwargs = {'device': 'cpu'}
-encode_kwargs = {'normalize_embeddings': True}
+    if "pdf_docs" not in st.session_state:
+        st.session_state.pdf_docs = []
 
-hf = HuggingFaceEmbeddings(
-    model_name=model_name,
-    model_kwargs=model_kwargs,
-    encode_kwargs=encode_kwargs
-)
+    if "conversation_chain" not in st.session_state:
+        st.session_state.conversation_chain = None
 
-def get_vectorstore(text_chunks):
-    model_name = "sentence-transformers/all-mpnet-base-v2"
-    model_kwargs = {'device': 'cpu'}
-    encode_kwargs = {'normalize_embeddings': True}
-    hf = HuggingFaceEmbeddings(
-        model_name=model_name,
-        model_kwargs=model_kwargs,
-        encode_kwargs=encode_kwargs
-    )
+    with st.sidebar:
+        st.subheader("Your documents")
+        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        if pdf_docs:
+            st.session_state.pdf_docs.extend(pdf_docs)
+
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                raw_text = get_pdf_text(st.session_state.pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                vectorstore = get_vectorstore(text_chunks)
+                st.session_state.conversation_chain = get_conversation_chain(vectorstore)
+                st.success("Documents processed and conversation chain created successfully.")
 
-    vectorstore = Chroma.from_documents(documents=text_chunks, embedding=hf, persist_directory="docs/chroma/")
-    return vectorstore
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(st.session_state.conversation_chain, user_question)
 
 def get_pdf_text(pdf_docs):
-    document_loader = DirectoryLoader(pdf_docs)
-    return document_loader.load()
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text    
 
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
+        chunk_size=600, chunk_overlap=50,
+        separators=["\n \n \n", "\n \n", "\n1" , "(?<=\. )", " ", ""],
     )
     chunks = text_splitter.split_text(text)
     return chunks
 
-def create_conversational_rag_chain(vectorstore):
-    
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    model_path = os.path.join(script_dir, 'qwen2-0_5b-instruct-q4_0.gguf')
-    
-    retriever = vectorstore.as_retriever(search_type='mmr', search_kwargs={"k": 7})
+def get_vectorstore(text_chunks):
+    model_name = "sentence-transformers/all-mpnet-base-v2"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': True}
+    embeddings = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+    vectorstore = Chroma.from_texts(
+        texts=text_chunks, embedding=embeddings, persist_directory="docs/chroma/")
+    return vectorstore    
 
+def get_conversation_chain(vectorstore):
     callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
-
+    script_dir = os.path.dirname(os.path.abspath(__file__))
     llm = llamacpp.LlamaCpp(
-        model_path=os.path.join(model_path),
+        model_path = os.path.join(script_dir, 'qwen2-0_5b-instruct-q4_0.gguf'),
         n_gpu_layers=1,
         temperature=0.1,
         top_p=0.9,
@@ -79,14 +88,26 @@ def create_conversational_rag_chain(vectorstore):
         verbose=False,
     )
 
-    contextualize_q_system_prompt = """Given a context, chat history and the latest user question
-    which maybe reference context in the chat history, formulate a standalone question
+    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 7})
+
+    contextualize_q_system_prompt = """Given a context, chat history and the latest user question, 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."""
 
-    ha_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_system_prompt)
+    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)
 
-    qa_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, just say that you don't know. Be as informative as possible, be polite and formal.\n{context}"""
+    qa_system_prompt = """Use the following pieces of retrieved context to answer the question{input}. \
+    Be informative but don't make too long answers, be polite and formal. \
+    If you don't know the answer, say "I don't know the answer." \
+    {context}"""
 
     qa_prompt = ChatPromptTemplate.from_messages(
         [
@@ -95,12 +116,12 @@ def create_conversational_rag_chain(vectorstore):
             ("human", "{input}"),
         ]
     )
-
+    
     question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
 
-    rag_chain = create_retrieval_chain(ha_retriever, question_answer_chain)
+    rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
     msgs = StreamlitChatMessageHistory(key="special_app_key")
-
+    
     conversation_chain = RunnableWithMessageHistory(
         rag_chain,
         lambda session_id: msgs,
@@ -110,59 +131,17 @@ def create_conversational_rag_chain(vectorstore):
     )
     return conversation_chain
 
-def main():
-    """Main function for the Streamlit app."""
-    # Initialize chat history if not already present in session state
-
-    documents = []
-    
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    data_path = os.path.join(script_dir, "data/")
-    for filename in os.listdir(data_path):
-
-    if filename.endswith('.txt'):
-
-        file_path = os.path.join(data_path, filename)
-
-        documents = TextLoader(file_path).load()
-
-        documents.extend(documents)
-    
-    
-    docs = split_docs(documents, 350, 40)
-
-    
-    
-    
-    
-    
-
-    vectorstore = get_vectorstore(docs)
+def handle_userinput(conversation_chain, prompt):
+    msgs = StreamlitChatMessageHistory(key="special_app_key")
+    st.chat_message("human").write(prompt)
+    input_dict = {"input": prompt, "chat_history": msgs.messages}
+    config = {"configurable": {"session_id": 1}}
     
-    msgs = st.session_state.get("chat_history", StreamlitChatMessageHistory(key="special_app_key"))
-    chain_with_history = create_conversational_rag_chain(vectorstore)
-
-    st.title("Conversational RAG Chatbot")
-
-    if prompt := st.chat_input():
-        st.chat_message("human").write(prompt)
-
-        # Prepare the input dictionary with the correct keys
-        input_dict = {"input": prompt, "chat_history": msgs.messages}
-        config = {"configurable": {"session_id": "any"}}
-
-        # Process user input and handle response
-        response = chain_with_history.invoke(input_dict, config)
+    try:
+        response = conversation_chain.invoke(input_dict, config)
         st.chat_message("ai").write(response["answer"])
+    except Exception as e:
+        st.error(f"Error invoking conversation chain: {e}")
 
-        # Display retrieved documents (if any and present in response)
-        if "docs" in response and response["documents"]:
-            for index, doc in enumerate(response["documents"]):
-                with st.expander(f"Document {index + 1}"):
-                    st.write(doc)
-
-    # Update chat history in session state
-    st.session_state["chat_history"] = msgs
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()