import streamlit as st
from rag_pipeline import chunk_text, build_index, retrieve, generate_answer
from evaluator import evaluate

st.set_page_config(
    page_title="RAG Evaluator",
    page_icon="🔍",
    layout="wide",
)

st.markdown("""
<style>
.metric-card {
    background: #f8fafc;
    border: 1px solid #e2e8f0;
    border-radius: 10px;
    padding: 1rem 1.25rem;
    text-align: center;
}
.metric-label {
    font-size: 12px;
    color: #64748b;
    text-transform: uppercase;
    letter-spacing: 0.05em;
    margin-bottom: 0.25rem;
}
.metric-value {
    font-size: 28px;
    font-weight: 600;
}
.score-high { color: #16a34a; }
.score-mid  { color: #d97706; }
.score-low  { color: #dc2626; }
</style>
""", unsafe_allow_html=True)

st.title("🔍 RAG Evaluator")
st.markdown(
    "Paste any document and ask a question. "
    "Get an AI-generated answer with **live quality scores** — "
    "faithfulness, relevance, groundedness, and completeness."
)
st.markdown("---")

with st.sidebar:
    st.header("⚙️ Configuration")
    api_key = st.text_input(
        "OpenAI API Key",
        type="password",
        help="Your key is never stored. Used only for this session.",
    )
    st.markdown("---")
    st.markdown("**About this tool**")
    st.markdown(
        "Built by [Faraz Mubeen Haider](https://linkedin.com/in/farazmubeen) "
        "to demonstrate production RAG evaluation techniques."
    )
    st.markdown("[GitHub](https://github.com/Faraz6180) | "
                "[LinkedIn](https://linkedin.com/in/farazmubeen)")

col1, col2 = st.columns([1, 1], gap="large")

with col1:
    st.subheader("📄 Document")
    document = st.text_area(
        "Paste your document here",
        height=300,
        placeholder="Paste any text document — a report, article, policy, or knowledge base excerpt...",
    )
    st.subheader("❓ Question")
    query = st.text_input(
        "Ask a question about the document",
        placeholder="e.g. What are the main findings of this report?",
    )
    run = st.button("▶ Evaluate", use_container_width=True, type="primary")

with col2:
    st.subheader("📊 Results")

    if run:
        if not api_key:
            st.error("Please enter your OpenAI API key in the sidebar.")
        elif not document.strip():
            st.error("Please paste a document.")
        elif not query.strip():
            st.error("Please enter a question.")
        else:
            with st.spinner("Running RAG pipeline..."):
                chunks = chunk_text(document)
                index, _ = build_index(chunks)
                context_chunks = retrieve(query, chunks, index)
                answer, context = generate_answer(query, context_chunks, api_key)

            with st.spinner("Evaluating answer quality..."):
                scores = evaluate(query, answer, context, api_key)

            st.markdown("**Answer**")
            st.info(answer)
            st.markdown("**Quality Scores**")

            def color_class(val):
                if val >= 0.75:
                    return "score-high"
                elif val >= 0.5:
                    return "score-mid"
                return "score-low"

            metrics = [
                ("Faithfulness", scores["faithfulness"],
                 "Is every claim supported by the document?"),
                ("Relevance", scores["relevance"],
                 "Does the answer address the question?"),
                ("Groundedness", scores["groundedness"],
                 "Does it avoid speculation?"),
                ("Completeness", scores["completeness"],
                 "Does it cover all aspects of the question?"),
            ]

            c1, c2 = st.columns(2)
            for i, (label, val, tip) in enumerate(metrics):
                col = c1 if i % 2 == 0 else c2
                with col:
                    css_class = color_class(val)
                    st.markdown(
                        f"""<div class="metric-card" title="{tip}">
                        <div class="metric-label">{label}</div>
                        <div class="metric-value {css_class}">{val:.2f}</div>
                        </div>""",
                        unsafe_allow_html=True,
                    )
                    st.progress(val)
                    st.caption(tip)

            avg = sum(scores.values()) / len(scores)
            st.markdown("---")
            overall_class = color_class(avg)
            st.markdown(
                f"""<div class="metric-card">
                <div class="metric-label">Overall Quality Score</div>
                <div class="metric-value {overall_class}">{avg:.2f}</div>
                </div>""",
                unsafe_allow_html=True,
            )

            with st.expander("🔍 Retrieved context chunks"):
                for i, chunk in enumerate(context_chunks):
                    st.markdown(f"**Chunk {i+1}:**")
                    st.text(chunk)

    else:
        st.markdown(
            """<div style='padding:2rem;text-align:center;color:#94a3b8;border:1px dashed #e2e8f0;border-radius:10px;'>
            Paste a document and question,<br>then click <b>Evaluate</b> to see results.
            </div>""",
            unsafe_allow_html=True,
        )