app/streamlit_app.py

import streamlit as st
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from transformers import pipeline
import datetime

embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
db = Chroma(persist_directory="./chroma_db", embedding_function=embedding)
qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")

if "history" not in st.session_state:
    st.session_state.history = []

if "log" not in st.session_state:
    st.session_state.log = []

def get_response(question):
    docs = db.similarity_search(question, k=4)
    context = "\n".join(doc.page_content for doc in docs)
    prompt = f"Answer this question clearly and fully:\nQuestion: {question}\nContext: {context}"
    result = qa_pipeline(prompt, max_length=512, do_sample=False)
    return result[0]['generated_text'].strip()

st.set_page_config(page_title="KizDar Festival Assistant", layout="centered")
st.title("KizDar Festival AI Assistant")
st.caption("Ask about the schedule, DJs, workshops, and more.")

question = st.text_input("Your question:")

if question:
    with st.spinner("Generating response..."):
        answer = get_response(question)
    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    st.session_state.history.append((question, answer))
    st.session_state.log.append(f"{timestamp} | Q: {question} | A: {answer[:80]}...")
    st.markdown("### Answer:")
    st.success(answer)

if st.session_state.history:
    st.markdown("---")
    st.markdown("### Previous Questions:")
    for i, (q, a) in enumerate(reversed(st.session_state.history[-5:]), 1):
        st.markdown(f"**{i}. {q}**")
        st.markdown(f"*{a}*")

with st.expander("📝 View Usage Log"):
    for entry in st.session_state.log[-10:]:
        st.code(entry)