Update app.py

app.py

@@ -1,106 +1,97 @@
-from dotenv import load_dotenv
+import os
 import streamlit as st
+from huggingface_hub import HfApi
+
 from langchain_community.document_loaders import UnstructuredPDFLoader
-from langchain_text_splitters.character import CharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+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
-import os
-import nltk
-nltk.download('punkt_tab')
-nltk.download('averaged_perceptron_tagger_eng')
-# Install Poppler and Tesseract in the runtime environment
-os.system("apt-get update && apt-get install -y poppler-utils tesseract-ocr")
-
-secret = os.getenv('Groq_api')
-
-working_dir = os.path.dirname(os.path.abspath(__file__))
-
-def load_documents(file_path):
-    # Specify poppler_path and tesseract_path to ensure compatibility
-    loader = UnstructuredPDFLoader(
-        file_path, 
-        poppler_path="/usr/bin", 
-        tesseract_path="/usr/bin/tesseract"
-    )
+from langchain.chains import RetrievalQA
+
+# Set the working directory
+working_dir = os.path.dirname(os.path.abspath((__file__)))
+
+secret = os.getenv('GROQ_API_KEY')
+
+# Loading the embedding model
+embedding = HuggingFaceEmbeddings()
+
+# Load the llm from Groq
+llm = ChatGroq(
+    model="deepseek-r1-distill-llama-70b",
+    temperature=0
+)
+
+
+def process_document_to_chroma_db(file_name):
+    """Process the document and load it into Chroma DB."""
+    # Load the document using unstructured PDF loader
+    loader = UnstructuredPDFLoader(f"{working_dir}/{file_name}")
     documents = loader.load()
-    return documents
 
-def setup_vectorstore(documents):
-    embeddings = HuggingFaceEmbeddings()
-    text_splitter = CharacterTextSplitter(
-        separator="/n", 
-        chunk_size=1000,
+    # Split the text into chunks
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=2000,
         chunk_overlap=200
     )
-    doc_chunks = text_splitter.split_documents(documents)
-    vectorstores = FAISS.from_documents(doc_chunks, embeddings)
-    return vectorstores
-
-def create_chain(vectorstores):
-    llm = ChatGroq(
-        api_key=secret,
-        model="llama-3.1-8b-instant",
-        temperature=0
+    texts = text_splitter.split_documents(documents)
+
+    # Load the documents into Chroma vectorstore
+    vectordb = Chroma.from_documents(
+        documents=texts,
+        embedding=embedding,
+        persist_directory=f"{working_dir}/doc_vectorstore"
     )
-    retriever = vectorstores.as_retriever()
-    memory = ConversationBufferMemory(
-        llm=llm,
-        output_key="answer",
-        memory_key="chat_history",
-        return_messages=True
+    return 0
+
+
+def answer_question(user_question):
+    """Answer the user's question using the trained model."""
+    # Load the persistent vectordb
+    vectordb = Chroma(
+        persist_directory=f"{working_dir}/doc_vectorstore",
+        embedding_function=embedding
     )
-    chain = ConversationalRetrievalChain.from_llm(
+
+    # Retriever
+    retriever = vectordb.as_retriever()
+
+    # Create a chain to answer user question using DeepSeek-R1
+    qa_chain = RetrievalQA.from_chain_type(
         llm=llm,
+        chain_type="stuff",
         retriever=retriever,
-        memory=memory,
-        verbose=True
     )
-    return chain
+    response = qa_chain.invoke({"query": user_question})
+    answer = response["result"]
 
-# Streamlit page configuration
-st.set_page_config(
-    page_title="Chat with your documents",
-    page_icon="📑",
-    layout="centered"
-)
+    return answer
 
-st.title("📝Chat With your docs 😎")
 
-# Initialize session states
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
+# Streamlit interface
+st.title("🐋 DeepSeek-R1 - Document RAG")
 
-uploaded_file = st.file_uploader(label="Upload your PDF")
+# File uploader widget
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
 
-if uploaded_file:
-    file_path = f"{working_dir}/{uploaded_file.name}"
-    with open(file_path, "wb") as f:
+if uploaded_file is not None:
+    # Define save path and save the uploaded file
+    save_path = os.path.join(working_dir, uploaded_file.name)
+    with open(save_path, "wb") as f:
         f.write(uploaded_file.getbuffer())
 
-    if "vectorstores" not in st.session_state:
-        st.session_state.vectorstores = setup_vectorstore(load_documents(file_path))
-
-    if "conversation_chain" not in st.session_state:
-        st.session_state.conversation_chain = create_chain(st.session_state.vectorstores)
-
-# Display chat history
-for message in st.session_state.chat_history:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
+    # Process the document
+    process_document_to_chroma_db(uploaded_file.name)
+    st.info("Document Processed Successfully")
 
-# User input handling
-user_input = st.chat_input("Ask any questions relevant to uploaded pdf")
+# Text widget to get user input
+user_question = st.text_area("Ask your question about the document")
 
-if user_input:
-    st.session_state.chat_history.append({"role": "user", "content": user_input})
-    with st.chat_message("user"):
-        st.markdown(user_input)
+if st.button("Answer"):
+    # Answer the user's question
+    answer = answer_question(user_question)
 
-    with st.chat_message("assistant"):
-        response = st.session_state.conversation_chain({"question": user_input})
-        assistant_response = response["answer"]
-        st.markdown(assistant_response)
-        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
+    # Display the response
+    st.markdown("### DeepSeek-R1 Response")
+    st.markdown(answer)