# File: dashboard.py
# Purpose: Streamlit recruiter dashboard with manual fetch report button

import requests
import streamlit as st

API_BASE = "http://localhost:8000"

st.set_page_config(page_title="AI Interview Caller", layout="wide")
st.title("AI Interview Screening Caller")
st.caption("Powered by Bland.ai · Groq · ElevenLabs")

# ── Sidebar: Initiate New Call ────────────────────────────────────────────────
with st.sidebar:
    st.header("New Interview Call")
    candidate_name = st.text_input("Candidate Name")
    candidate_phone = st.text_input("Phone (E.164 e.g. +919876543210)")
    candidate_email = st.text_input("Email (optional)")
    resume_text = st.text_area("Paste Resume Text", height=200)

    if st.button("Initiate Call", type="primary"):
        if not candidate_name or not candidate_phone:
            st.error("Name and phone are required.")
        else:
            payload = {
                "candidate_name": candidate_name,
                "candidate_phone": candidate_phone,
                "candidate_email": candidate_email,
                "resume_text": resume_text,
            }
            with st.spinner("Initiating call via Bland.ai..."):
                resp = requests.post(f"{API_BASE}/api/initiate-call", json=payload)
            if resp.status_code == 200:
                data = resp.json()
                st.success(f"Call initiated! Session ID: {data['session_id']}")
                st.info("After the call ends, click 'Fetch Report' to get the evaluation.")
            else:
                st.error(f"Error: {resp.text}")

# ── Main: Session List ────────────────────────────────────────────────────────
st.header("Recent Interviews")

col_refresh, _ = st.columns([1, 5])
if col_refresh.button("Refresh List"):
    st.rerun()

resp = requests.get(f"{API_BASE}/api/sessions")
if resp.status_code != 200:
    st.warning("Could not reach the API server.")
    st.stop()

sessions = resp.json()
if not sessions:
    st.info("No interviews yet. Use the sidebar to start one.")
    st.stop()

# Column headers
h1, h2, h3, h4, h5, h6 = st.columns([2, 1, 1, 2, 1, 1])
h1.markdown("**Candidate**")
h2.markdown("**Status**")
h3.markdown("**Score**")
h4.markdown("**Recommendation**")
h5.markdown("**Action**")
h6.markdown("**Report**")
st.divider()

for s in sessions:
    with st.container():
        col1, col2, col3, col4, col5, col6 = st.columns([2, 1, 1, 2, 1, 1])
        col1.write(f"**{s['candidate']}**")
        status_color = {"completed": "green", "active": "orange", "initiated": "blue"}.get(s["status"], "gray")
        col2.markdown(f":{status_color}[{s['status']}]")
        col3.write(f"{s['overall_score']:.1f}/10" if s["overall_score"] else "—")
        col4.write(s["recommendation"] or "—")

        if col5.button("Fetch Report", key=f"fetch_{s['id']}"):
            with st.spinner("Fetching report from Bland.ai..."):
                fetch_resp = requests.post(f"{API_BASE}/api/session/{s['id']}/fetch-report")
            if fetch_resp.status_code == 200:
                st.success(fetch_resp.json().get("message", "Done"))
                st.rerun()
            else:
                st.error(fetch_resp.text)

        if col6.button("View", key=f"view_{s['id']}"):
            st.session_state["selected_session"] = s["id"]

        st.divider()

# ── Report Viewer ─────────────────────────────────────────────────────────────
if "selected_session" in st.session_state:
    sid = st.session_state["selected_session"]
    report_resp = requests.get(f"{API_BASE}/api/session/{sid}/report")
    if report_resp.status_code == 200:
        data = report_resp.json()
        st.subheader(f"Report — {data['candidate']}")

        if data["status"] != "completed":
            st.warning(f"Status: {data['status']} — Click 'Fetch Report' after the call ends.")
        else:
            st.markdown("### Full Analysis is Below")
            col1, col2, col3, col4 = st.columns(4)
            col1.metric("Communication", f"{data['communication_score']:.1f}/10")
            col2.metric("Technical", f"{data['technical_score']:.1f}/10")
            col3.metric("Confidence", f"{data['confidence_score']:.1f}/10")
            col4.metric("Overall", f"{data['overall_score']:.1f}/10")

            st.markdown(f"**Recommendation:** {data['recommendation']}")
            st.markdown(f"**Skills:** {', '.join(data['skills'] or [])}")

            with st.expander("Full Report"):
                st.text(data["report"])

            trans_resp = requests.get(f"{API_BASE}/api/session/{sid}/transcript")
            if trans_resp.status_code == 200:
                turns = trans_resp.json()
                if turns:
                    with st.expander("View Transcript"):
                        for t in turns:
                            if t["speaker"] == "AI":
                                st.markdown(f"**AI:** {t['text']}")
                            else:
                                st.markdown(f"**Candidate:** {t['text']}")