app.py

import streamlit as st
from langchain.schema import Document
from langchain_core.messages import AIMessage, HumanMessage
from sentence_transformers import SentenceTransformer
from langchain.prompts.chat import ChatPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from aift.multimodal import textqa
from aift import setting
import chromadb

chromadb.api.client.SharedSystemClient.clear_system_cache()
# Set API key for Pathumma
setting.set_api_key('T69FqnYgOdreO5G0nZaM8gHcjo1sifyU')

# App Configuration
st.set_page_config(page_title="Nong Nok", page_icon="🤖")

st.markdown(
    """

    <style>

        @import url('https://fonts.googleapis.com/css2?family=Kanit:wght@700&display=swap');

        

        body {

            margin: 0;

            padding: 0;

        }

        .header-container {

            position: absolute;

            top: 100%;

            left: 50%;

            transform: translate(-50%, -50%);

            text-align: center;

            margin-bottom: 25px;

        }

        .header-title {

            font-size: 4em;

            margin: 0;

            white-space: nowrap;

            font-family: 'Kanit', sans-serif;

            color: white; /* Fallback color */

            -webkit-text-stroke: 2px black; /* Stroke width and color */

            text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); /* Optional shadow for better visibility */

            animation: fadeIn 1s forwards;

        }

        .sub-title {

            position: absolute;

            bottom: -10px;

            right: -20px;

            font-size: 1.5em;

            transform: rotate(-10deg);

            color: #21A2DB;

            white-space: nowrap;    

        }

        @keyframes fadeIn {

            0% {

                color: transparent;

            }

            100% {

                color: white;

            }

        }

    </style>

    <div class="header-container">

        <h1 class="header-title">

            PDPA Chatbot

        </h1>

        <div class="sub-title">( Noknoy-0.5 )</div>

    </div>

    """,
    unsafe_allow_html=True
)

st.markdown(" ")
st.markdown(" ")
st.markdown(" ")
# Custom Embeddings
class CustomEmbeddings:
    def __init__(self, model_name="mrp/simcse-model-m-bert-thai-cased"):
        self.model = SentenceTransformer(model_name)

    def embed_query(self, text):
        return self.model.encode([text])[0].tolist()

    def embed_documents(self, texts):
        return [self.model.encode(text).tolist() for text in texts]

# Pathumma Model Wrapper
class PathummaModel:
    def __init__(self):
        pass

    def generate(self, instruction: str, return_json: bool = False):
        response = textqa.generate(instruction=instruction, return_json=return_json)
        if return_json:
            return response.get("content", "")
        return response

    def __call__(self, input: str):
        return self.generate(input, return_json=False)

# Initialize Pathumma model
model_local = PathummaModel()

# Load PDF file
file_path = "langchain.pdf"
loader = PyPDFLoader(file_path)
docs = loader.load()

# Split text into manageable chunks
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
doc_splits = text_splitter.split_documents(docs)

# Convert documents to Embeddings and store them in Chroma
vectorstore = Chroma.from_documents(
    documents=doc_splits,
    collection_name="rag-chroma",
    embedding=CustomEmbeddings(model_name="mrp/simcse-model-m-bert-thai-cased"),
)
retriever = vectorstore.as_retriever()

# Generate a response using retriever
def get_response(user_query):
    retrieved_docs = retriever.get_relevant_documents(user_query)
    retrieved_context = " ".join([doc.page_content for doc in retrieved_docs])

    after_rag_template = """ตอบคำถามโดยพิจารณาจากบริบทต่อไปนี้เท่านั้น:

    {context}

    คำถาม: {question}

    """
    prompt = after_rag_template.format(context=retrieved_context, question=user_query)
    response = model_local(prompt)
    return response

# Initialize session state
if "chat_history" not in st.session_state:
    st.session_state.chat_history = [
        AIMessage(content='🐦 ยินดีต้อนรับสู่น้องนก แชทบอทที่พร้อมจะให้ข้อมูลคุณเกี่ยวกับพระราชบัญญัติคุ้มครองข้อมูลส่วนบุคคล (PDPA) มีอะไรให้ช่วยไหมครับ?'),
    ]

# Render chat history
for message in st.session_state.chat_history:
    if isinstance(message, AIMessage):
        with st.chat_message("AI"):
            st.write(message.content)
    elif isinstance(message, HumanMessage):
        with st.chat_message("Human"):
            st.write(message.content)

# User input
user_query = st.chat_input("พิมพ์ข้อความที่นี่...")
if user_query is not None and user_query.strip() != "":
    st.session_state.chat_history.append(HumanMessage(content=user_query))

    with st.chat_message("Human"):
        st.markdown(user_query)

    with st.chat_message("AI"):
        placeholder = st.empty()
        placeholder.markdown("กำลังสร้างคำตอบ...")  
        response = get_response(user_query)
        placeholder.markdown(response)  

    st.session_state.chat_history.append(AIMessage(content=response))