Update app.py

app.py

@@ -1,145 +1,84 @@
+import os
+import json
+
 import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_groq import ChatGroq
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models.gigachat import GigaChat 
-from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub, LlamaCpp
-from huggingface_hub import snapshot_download, hf_hub_download
-
-
-
-repo_name = "IlyaGusev/saiga_mistral_7b_gguf"
-model_name = "model-q4_K.gguf"
-
-#snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
-
-from transformers import pipeline
-
-# Initialize the summarization pipeline
-summarizer = pipeline("summarization")
-
-
-
-def get_pdf_text(pdf_docs):
-    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 = CharacterTextSplitter(separator="\n",
-                                          chunk_size=1000,  # 1000
-                                          chunk_overlap=200,  # 200
-                                          length_function=len
-                                          )
-    chunks = text_splitter.split_text(text)
-
-    return chunks
 
+from vectorize_documents import embeddings
 
-#def get_vectorstore(text_chunks):
-    #embeddings = OpenAIEmbeddings()
-    #embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    #embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
-    #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
-    #vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+working_dir = os.path.dirname(os.path.abspath(__file__))
+config_data = json.load(open(f"{working_dir}/config.json"))
+GROQ_API_KEY = config_data["GROQ_API_KEY"]
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
 
-    #return vectorstore
 
-
-def get_vectorstore(text_chunks, embedding_model_name="intfloat/multilingual-e5-large"):
-    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+def setup_vectorstore():
+    persist_directory = f"{working_dir}/vector_db_dir"
+    embedddings = HuggingFaceEmbeddings()
+    vectorstore = Chroma(persist_directory=persist_directory,
+                         embedding_function=embeddings)
     return vectorstore
 
 
+def chat_chain(vectorstore):
+    llm = ChatGroq(model="llama-3.1-70b-versatile",
+                   temperature=0)
+    retriever = vectorstore.as_retriever()
+    memory = ConversationBufferMemory(
+        llm=llm,
+        output_key="answer",
+        memory_key="chat_history",
+        return_messages=True
+    )
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        verbose=True,
+        return_source_documents=True
+    )
+
+    return chain
+
+
+st.set_page_config(
+    page_title="Multi Doc Chat",
+    page_icon = "📚",
+    layout="centered"
+)
+
+st.title("📚 Multi Documents Chatbot")
 
-def get_conversation_chain(vectorstore, model_name):
-
-
-    llm = GigaChat(profanity=False,
-                   verify_ssl_certs=False
-                  )
-    
-    memory = ConversationBufferMemory(memory_key='chat_history',
-                                      input_key='question',
-                                      output_key='answer',
-                                      return_messages=True)
-    
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
-                                                               retriever=vectorstore.as_retriever(),
-                                                               memory=memory,
-                                                               return_source_documents=True
-                                                               )
-
-    return conversation_chain
-
-
-def summarize_text(text):
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-    return summary[0]['summary_text']
-
-
-def handle_userinput(user_question):
-    response = st.session_state.conversation({'question': user_question})
-
-    st.session_state.chat_history = response['chat_history']
-    st.session_state.retrieved_text = response['source_documents']
-
-    for i, (message, text) in enumerate(zip(st.session_state.chat_history, st.session_state.retrieved_text)):
-        if i % 2 == 0:  # User messages
-            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
-        else:  # Bot messages
-            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
-            if summarize_option and text.page_content:  # Check if summarization is enabled
-                summarized_text = summarize_text(text.page_content)
-                st.write(bot_template.replace("{{MSG}}", summarized_text), unsafe_allow_html=True)
-            else:
-                st.write(bot_template.replace("{{MSG}}", text.page_content), unsafe_allow_html=True)
-
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
 
+if "vectorstore" not in st.session_state:
+    st.session_state.vectorstore = setup_vectorstore()
 
-st.set_page_config(page_title="Chat with multiple PDFs",
-                   page_icon=":books:")
-st.write(css, unsafe_allow_html=True)
+if "conversationsal_chain" not in st.session_state:
+    st.session_state.conversationsal_chain = chat_chain(st.session_state.vectorstore)
 
-if "conversation" not in st.session_state:
-    st.session_state.conversation = None
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = None
 
-st.header("Chat with multiple PDFs :books:")
-user_question = st.text_input("Ask a question about your documents: ")
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
 
-if user_question:
-    handle_userinput(user_question)
+user_input = st.chat_input("Ask AI...")
 
-with st.sidebar:
-    st.subheader("Your documents")
-    embedding_model_name = st.selectbox("Select embedding model", ["intfloat/multilingual-e5-large", "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"])
-    summarize_option = st.sidebar.checkbox("Enable Summarization", value=False)
-    pdf_docs = st.file_uploader(
-        "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-    if st.button("Process"):
-        with st.spinner("Processing"):
-            # get pdf text
-            raw_text = get_pdf_text(pdf_docs)
+if user_input:
+    st.session_state.chat_history.append({"role": "user", "content": user_input})
 
-            # get the text chunks
-            text_chunks = get_text_chunks(raw_text)
+    with st.chat_message("user"):
+        st.markdown(user_input)
 
-            # create vector store
-            vectorstore = get_vectorstore(text_chunks, embedding_model_name)
 
-            # create conversation chain
-            st.session_state.conversation = get_conversation_chain(vectorstore, model_name)
+    with st.chat_message("assistant"):
+        response = st.session_state.conversationsal_chain({"question": user_input})
+        assistant_response = response["answer"]
+        st.markdown(assistant_response)
+        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})