main.py

import streamlit as st

from search.search_classical import classical_search, classical_retrieve_chunks
from search.search_best_pair import best_pair_search
from llm import generate_response


def pick_mode(label: str) -> str:
    if label.startswith("Semantic"):
        return "semantic"
    if label.startswith("Keyword"):
        return "bm25"
    return "hybrid"


st.set_page_config(page_title="🔍 Multimodal Search The Batch")
st.image("data/the-batch-logo.webp", width=300)
st.title("Multimodal Assistant")

mode = st.selectbox("🔎 Select the search mode:", ["Classical RAG", "Multimodal RAG"])
query = st.text_input("📝 Enter the text query:")

# --- Classical controls ---
classical_retriever = "Semantic (embeddings)"
use_reranker = True

if mode == "Classical RAG":
    classical_retriever = st.radio(
        "🧩 Classical retrieval:",
        ["Semantic (embeddings)", "Keyword (BM25)", "Hybrid (BM25 + Semantic)"],
        horizontal=True
    )
    use_reranker = st.checkbox("✨ Use reranker (cross-encoder)", value=True)

# --- Preview results ---
results = []
if query:
    if mode == "Classical RAG":
        search_mode = pick_mode(classical_retriever)
        results = classical_search(query, k=3, mode=search_mode)
    else:
        results = best_pair_search(query, k=3)

    st.markdown(f"### 📄 Results found: {len(results)}")

    for i, meta in enumerate(results):
        st.markdown(f"### 🔹 Result {i + 1}")
        if meta.get("title"):
            st.markdown(f"**📖 Name:** {meta['title']}")
        if meta.get("date"):
            st.markdown(f"**📅 Date of publication:** {meta['date']}")
        if meta.get("description"):
            st.markdown(f"**📝 Description:** {meta['description']}")
        if meta.get("image_url"):
            st.image(meta["image_url"], use_container_width=True)
        if meta.get("content"):
            st.markdown("**📚 Part of the article:**")
            st.write(meta["content"][:500] + "...")
        if meta.get("source_url"):
            st.markdown(f"[🔗 Read the full article →]({meta['source_url']})")
        st.markdown("---")

# --- Generate answer ---
if query and st.button("🧠 Generate a response to a query"):

    if mode == "Classical RAG":
        search_mode = pick_mode(classical_retriever)

        chunks = classical_retrieve_chunks(
            query=query,
            mode=search_mode,
            fetch_k=50,
            rerank_k=5,
            use_reranker=use_reranker
        )

        docs = []
        for idx, c in enumerate(chunks, start=1):
            meta = c.get("metadata", {})
            docs.append({
                "id": idx,
                "title": meta.get("title", ""),
                "description": meta.get("description", ""),
                "source_url": meta.get("source_url", ""),
                "content": c.get("chunk_text", ""),
                "retriever": c.get("retriever", ""),
                "rerank_score": c.get("rerank_score", None),
            })

        response = generate_response(query, docs)
        st.markdown("### 🤖 Generated Response:")
        st.success(response)

        st.markdown("### 📌 Sources")
        for d in docs:
            st.markdown(f"**[{d['id']}] {d.get('title','')}**")
            if d.get("source_url"):
                st.markdown(d["source_url"])
            st.write((d.get("content") or "")[:450] + "...")
            if d.get("retriever"):
                st.caption(f"retriever: {d['retriever']}")
            if d.get("rerank_score") is not None:
                st.caption(f"rerank_score: {d['rerank_score']:.4f}")
            st.markdown("---")

    else:
        # ✅ Multimodal mode:
        # Preview stays multimodal (best_pair_search),
        # but the ANSWER is generated from TEXT chunks (hybrid) for reliable QA + citations.
        chunks = classical_retrieve_chunks(
            query=query,
            mode="hybrid",
            fetch_k=50,
            rerank_k=5,
            use_reranker=True
        )

        docs = []
        for idx, c in enumerate(chunks, start=1):
            meta = c.get("metadata", {})
            docs.append({
                "id": idx,
                "title": meta.get("title", ""),
                "description": meta.get("description", ""),
                "source_url": meta.get("source_url", ""),
                "content": c.get("chunk_text", ""),
                "retriever": c.get("retriever", ""),
                "rerank_score": c.get("rerank_score", None),
            })

        response = generate_response(query, docs)
        st.markdown("### 🤖 Generated Response:")
        st.success(response)

        st.markdown("### 📌 Sources (text chunks)")
        for d in docs:
            st.markdown(f"**[{d['id']}] {d.get('title','')}**")
            if d.get("source_url"):
                st.markdown(d["source_url"])
            st.write((d.get("content") or "")[:450] + "...")
            if d.get("retriever"):
                st.caption(f"retriever: {d['retriever']}")
            if d.get("rerank_score") is not None:
                st.caption(f"rerank_score: {d['rerank_score']:.4f}")
            st.markdown("---")