app.py

"""
app.py
======
Streamlit chat UI for the Morningstar Financial Intelligence RAG Pipeline.

Deployed on Hugging Face Spaces (free CPU tier).
Vector store: Qdrant Cloud (free tier).
LLM: Google Gemini 1.5 Flash (free tier, 1M tokens/min).

Run locally:
    streamlit run app.py
"""

import os
import streamlit as st
from dotenv import load_dotenv

load_dotenv()

# ── Page config ────────────────────────────────────────────────────────────────
st.set_page_config(
    page_title = "Financial Intelligence Assistant",
    page_icon  = "📊",
    layout     = "wide",
)

# ── Load pipeline once (cached across sessions) ────────────────────────────────
@st.cache_resource(show_spinner="Loading pipeline...")
def init_pipeline():
    from src.retriever  import FinancialRetriever
    from src.rag_chain  import get_llm
    from src.guardrails import RAGGuardrails

    retriever  = FinancialRetriever(rerank=True)
    llm        = get_llm()
    guardrails = RAGGuardrails(run_grounding=False)  # NLI skipped for latency
    return retriever, llm, guardrails

retriever, llm, guardrails = init_pipeline()

# ── Sidebar ────────────────────────────────────────────────────────────────────
with st.sidebar:
    st.title("About")
    st.markdown(
        "This assistant answers questions about **Apple Inc.** using:\n"
        "- SEC filings: 10-K, 10-Q, 8-K\n"
        "- Morningstar equity research reports\n\n"
        "**Retrieval:** Dense ANN + cross-encoder reranking  \n"
        "**LLM:** Google Gemini 2.5 Flash  \n"
        "**Vector store:** Qdrant Cloud\n\n"
        "_For informational purposes only. Not investment advice._"
    )
    st.divider()
    st.caption("Sample questions:")
    examples = [
        "What was Apple's total net sales in FY2024?",
        "What were Apple's main risk factors in the 2024 10-K?",
        "How did Apple's Services segment perform in FY2024?",
        "What is Apple's gross margin trend over the last 3 years?",
        "What did Apple say about AI in their latest filings?",
    ]
    for ex in examples:
        if st.button(ex, use_container_width=True):
            st.session_state["prefill"] = ex

# ── Main UI ────────────────────────────────────────────────────────────────────
st.title("Financial Intelligence Assistant")
st.caption(
    "Apple SEC Filings (10-K, 10-Q, 8-K) + Morningstar Research | "
    "Powered by Google Gemini 1.5 Flash + Qdrant"
)

# ── Session state ──────────────────────────────────────────────────────────────
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat history
for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])
        if msg.get("sources"):
            with st.expander("Sources", expanded=False):
                for src in msg["sources"]:
                    st.caption(src)

# Handle prefilled example question from sidebar
prefill = st.session_state.pop("prefill", None)

# ── Chat input ─────────────────────────────────────────────────────────────────
query = st.chat_input("Ask about Apple financials...") or prefill

if query:
    # Show user message
    st.session_state.messages.append({"role": "user", "content": query})
    with st.chat_message("user"):
        st.markdown(query)

    with st.chat_message("assistant"):
        # Layer 1: input guardrail
        ok, reason = guardrails.gate_input(query)
        if not ok:
            st.warning(reason)
            st.session_state.messages.append({
                "role": "assistant", "content": reason
            })
            st.stop()

        # Retrieve chunks
        with st.spinner("Searching documents..."):
            chunks = retriever.retrieve(query, n_results=8)

        # Layer 2: retrieval guardrail
        ok, reason = guardrails.gate_retrieval(chunks)
        if not ok:
            st.warning(reason)
            st.session_state.messages.append({
                "role": "assistant", "content": reason
            })
            st.stop()

        # Build context
        context = retriever.build_context(chunks, max_chars=6000)

        # Build prompt
        from src.rag_chain import RAG_PROMPT_TEMPLATE
        from langchain_core.prompts import PromptTemplate
        from langchain_core.output_parsers import StrOutputParser

        prompt_template = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
        chain           = prompt_template | llm | StrOutputParser()

        # Stream response token by token
        full_answer = ""
        placeholder = st.empty()

        for chunk_token in chain.stream({"query": query, "context": context}):
            full_answer += chunk_token
            placeholder.markdown(full_answer + "▌")
        placeholder.markdown(full_answer)

        # Layer 3: output guardrail (basic checks, no NLI for latency)
        try:
            final_answer, warnings = guardrails.gate_output(
                full_answer, chunks
            )
        except ValueError as e:
            st.error(str(e))
            st.session_state.messages.append({
                "role": "assistant", "content": str(e)
            })
            st.stop()

        for w in warnings:
            st.caption(f"Note: {w}")

        # Build source attribution list
        sources = []
        for i, c in enumerate(chunks[:5], 1):
            m = c["metadata"]
            doc_type    = m.get("doc_type", "")
            fiscal_year = m.get("fiscal_year", "")
            filed       = m.get("filing_date", "")
            heading     = m.get("heading_path", "") or m.get("section_title", "")
            score       = c.get("score", 0)

            label = f"[{i}] {doc_type}"
            if fiscal_year:
                label += f" FY{fiscal_year}"
            if filed:
                label += f" (filed {filed})"
            if heading:
                label += f" | {heading[:60]}"
            label += f" | score: {score:.3f}"
            sources.append(label)

        if sources:
            with st.expander("Sources", expanded=False):
                for src in sources:
                    st.caption(src)

        # Save to session state
        st.session_state.messages.append({
            "role"    : "assistant",
            "content" : final_answer,
            "sources" : sources,
        })