# app.py

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
import gradio as gr

# Sample FAQ content
faq_text = """
Q: What is your return policy?
A: You can return any item within 30 days of purchase.

Q: How do I track my order?
A: Use the tracking link sent to your email after shipping.

Q: Do you ship internationally?
A: Yes, we ship to over 50 countries worldwide.

Q: What payment methods do you accept?
A: We accept credit cards, PayPal, and Apple Pay.
"""

# Save and load FAQ
with open("faq.txt", "w") as f:
    f.write(faq_text)

loader = TextLoader("faq.txt")
docs = loader.load()

# Chunking
splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
chunks = splitter.split_documents(docs)

# Embedding + FAISS
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = FAISS.from_documents(chunks, embedding)

# RAG pipeline
hf_pipe = pipeline("text2text-generation", model="google/flan-t5-base", max_length=256)
llm = HuggingFacePipeline(pipeline=hf_pipe)

retriever = db.as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

# Gradio UI
def chatbot(query):
    return qa_chain.run(query)

demo = gr.Interface(fn=chatbot, inputs="text", outputs="text", title="FAQ Chatbot (RAG + FAISS)")
demo.launch()