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import streamlit as st
from langchain_community.document_loaders import PDFPlumberLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_ollama import OllamaEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_ollama.llms import OllamaLLM
st.markdown("""
<style>
.stApp {
background-color: #0E1117;
color: #FFFFFF;
}
/* Chat Input Styling */
.stChatInput input {
background-color: #1E1E1E !important;
color: #FFFFFF !important;
border: 1px solid #3A3A3A !important;
}
/* User Message Styling */
.stChatMessage[data-testid="stChatMessage"]:nth-child(odd) {
background-color: #1E1E1E !important;
border: 1px solid #3A3A3A !important;
color: #E0E0E0 !important;
border-radius: 10px;
padding: 15px;
margin: 10px 0;
}
/* Assistant Message Styling */
.stChatMessage[data-testid="stChatMessage"]:nth-child(even) {
background-color: #2A2A2A !important;
border: 1px solid #404040 !important;
color: #F0F0F0 !important;
border-radius: 10px;
padding: 15px;
margin: 10px 0;
}
/* Avatar Styling */
.stChatMessage .avatar {
background-color: #00FFAA !important;
color: #000000 !important;
}
/* Text Color Fix */
.stChatMessage p, .stChatMessage div {
color: #FFFFFF !important;
}
.stFileUploader {
background-color: #1E1E1E;
border: 1px solid #3A3A3A;
border-radius: 5px;
padding: 15px;
}
h1, h2, h3 {
color: #00FFAA !important;
}
</style>
""", unsafe_allow_html=True)
PROMPT_TEMPLATE = """
You are an expert research assistant. Use the provided context to answer the query.
If unsure, state that you don't know. Be concise and factual (max 3 sentences).
Query: {user_query}
Context: {document_context}
Answer:
"""
PDF_STORAGE_PATH = ''
EMBEDDING_MODEL = OllamaEmbeddings(model="deepseek-r1:1.5b")
DOCUMENT_VECTOR_DB = InMemoryVectorStore(EMBEDDING_MODEL)
LANGUAGE_MODEL = OllamaLLM(model="deepseek-r1:1.5b")
def save_uploaded_file(uploaded_file):
file_path = PDF_STORAGE_PATH + uploaded_file.name
with open(file_path, "wb") as file:
file.write(uploaded_file.getbuffer())
return file_path
def load_pdf_documents(file_path):
document_loader = PDFPlumberLoader(file_path)
return document_loader.load()
def chunk_documents(raw_documents):
text_processor = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
add_start_index=True
)
return text_processor.split_documents(raw_documents)
def index_documents(document_chunks):
DOCUMENT_VECTOR_DB.add_documents(document_chunks)
def find_related_documents(query):
return DOCUMENT_VECTOR_DB.similarity_search(query)
def generate_answer(user_query, context_documents):
context_text = "\n\n".join([doc.page_content for doc in context_documents])
conversation_prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
response_chain = conversation_prompt | LANGUAGE_MODEL
return response_chain.invoke({"user_query": user_query, "document_context": context_text})
# UI Configuration
st.title("📘 DocuMind AI")
st.markdown("### Your Intelligent Document Assistant")
st.markdown("---")
# File Upload Section
uploaded_pdf = st.file_uploader(
"Upload Research Document (PDF)",
type="pdf",
help="Select a PDF document for analysis",
accept_multiple_files=False
)
if uploaded_pdf:
saved_path = save_uploaded_file(uploaded_pdf)
raw_docs = load_pdf_documents(saved_path)
processed_chunks = chunk_documents(raw_docs)
index_documents(processed_chunks)
st.success("✅ Document processed successfully! Ask your questions below.")
user_input = st.chat_input("Enter your question about the document...")
if user_input:
with st.chat_message("user"):
st.write(user_input)
with st.spinner("Analyzing document..."):
relevant_docs = find_related_documents(user_input)
ai_response = generate_answer(user_input, relevant_docs)
with st.chat_message("assistant", avatar="🤖"):
st.write(ai_response) |