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import os
import json
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
import chainlit as cl
# === Load and prepare data ===
with open("combined_data.json", "r") as f:
raw_data = json.load(f)
all_docs = [
Document(page_content=entry["content"], metadata=entry["metadata"])
for entry in raw_data
]
# === Split documents into chunks ===
splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50)
chunked_docs = splitter.split_documents(all_docs)
# === Use your fine-tuned Hugging Face embeddings ===
embedding_model = HuggingFaceEmbeddings(
model_name="AneetaXavier/reformer-pilates-embed-ft-49fc1835-9968-433d-9c45-1538ea91dcc9"
)
# === Set up FAISS vector store ===
vectorstore = FAISS.from_documents(chunked_docs, embedding_model)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
# === Load LLM ===
llm = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
# === Chainlit start event ===
@cl.on_chat_start
async def start():
await cl.Message("🤸 Ready! Ask me anything about Reformer Pilates.").send()
cl.user_session.set("qa_chain", qa_chain)
# === Chainlit message handler ===
@cl.on_message
async def handle_message(message: cl.Message):
chain = cl.user_session.get("qa_chain")
if chain:
try:
response = chain.run(message.content)
except Exception as e:
response = f"⚠️ Error: {str(e)}"
await cl.Message(response).send()
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