import streamlit as st
import pandas as pd
import numpy as np
import os
import hashlib
from datetime import datetime
from groq import Groq
import pdfplumber
import plotly.graph_objects as go
import folium
from streamlit_folium import st_folium
from streamlit_geolocation import streamlit_geolocation

# --- 1. CORE SYSTEM CONFIG ---
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
if not GROQ_API_KEY:
    st.error("⚠️ API Key Missing! Please check your environment variables.")
    st.stop()

st.set_page_config(page_title="IntelliCare Portal | Hassan Naseer", layout="wide", page_icon="🏥")

# --- 2. PREMIUM CSS ---
st.markdown("""
<style>
    [data-testid="stSidebar"] { background: #1e3a8a !important; }
    [data-testid="stSidebar"] * { color: #ffffff !important; }
    [data-testid="stSidebar"] .stRadio > div { display: none; }
    
    div.stButton > button, div.stDownloadButton > button {
        background-color: #2563eb !important; color: white !important;
        border: 2px solid #3b82f6 !important; border-radius: 12px !important;
        transition: 0.3s; font-weight: bold !important; width: 100% !important;
        margin-bottom: 10px !important;
    }
    div.stButton > button:hover { background-color: #1e40af !important; border-color: #60a5fa !important; box-shadow: 0 0 10px #3b82f6 !important; }
    
    .main .block-container { padding-bottom: 180px; }
    .metric-card { background: white; border-radius: 12px; padding: 20px; border-left: 5px solid #2563eb; box-shadow: 0 4px 15px rgba(0,0,0,0.05); color: #1e293b; margin-bottom: 15px; }
    .user-msg { background: #3b82f6; color: white; padding: 15px; border-radius: 15px 15px 2px 15px; margin-bottom: 10px; margin-left: 15%; }
    .ai-msg { background: #f1f5f9; color: #1e293b; padding: 15px; border-radius: 15px 15px 15px 2px; margin-bottom: 10px; margin-right: 15%; border: 1px solid #e2e8f0; }
</style>
""", unsafe_allow_html=True)

# --- 3. DATABASE HANDLERS ---
USER_DB, CALL_LOG_DB, CALL_SIGNAL_DB, FEEDBACK_DB, RX_DB = "users_secure.csv", "call_history.csv", "active_calls.csv", "feedback.csv", "prescriptions.csv"

def hash_pass(pwd): return hashlib.sha256(str.encode(pwd)).hexdigest()
def load_db(file, cols):
    if os.path.exists(file): return pd.read_csv(file)
    return pd.DataFrame(columns=cols)

# Session Initialization
if "logged_in" not in st.session_state: st.session_state.logged_in = False
if "msgs" not in st.session_state: st.session_state.msgs = []
if "active_doc" not in st.session_state: st.session_state.active_doc = ""
if "audio_key" not in st.session_state: st.session_state.audio_key = 0
if "nav" not in st.session_state: st.session_state.nav = "Home"

# --- 4. AUTHENTICATION ---
if not st.session_state.logged_in:
    st.markdown("<h1 style='text-align: center; color: #1e3a8a;'>🏥 IntelliCare Portal</h1>", unsafe_allow_html=True)
    c2 = st.columns([1, 2, 1])[1]
    with c2:
        tab1, tab2 = st.tabs(["🔐 Login", "📝 Create Account"])
        with tab1:
            u, p = st.text_input("Username"), st.text_input("Password", type="password")
            if st.button("Sign In"):
                db = load_db(USER_DB, ["username", "password", "role"])
                match = db[(db['username'] == u) & (db['password'] == hash_pass(p))]
                if not match.empty:
                    st.session_state.logged_in, st.session_state.username, st.session_state.role = True, u, match.iloc[0]['role']
                    st.session_state.nav = "📊 Analytics" if st.session_state.role == "Doctor" else "💬 AI Chat"
                    st.rerun()
        with tab2:
            nu, np = st.text_input("New ID"), st.text_input("New Pass", type="password")
            nr = st.selectbox("Role", ["Patient", "Doctor"])
            if st.button("Register Account"):
                df = load_db(USER_DB, ["username", "password", "role"])
                pd.concat([df, pd.DataFrame([{"username": nu, "password": hash_pass(np), "role": nr}])]).to_csv(USER_DB, index=False)
                st.success("Registration Successful!")
    st.stop()

# --- 5. SIDEBAR ---
with st.sidebar:
    st.markdown(f"### 👤 {st.session_state.username} ({st.session_state.role})")
    lang = st.radio("Language", ["English", "Urdu"], label_visibility="collapsed")
    st.divider()
    if st.session_state.role == "Patient":
        if st.button("💬 AI Chat"): st.session_state.nav = "💬 AI Chat"
        if st.button("💊 My Prescriptions"): st.session_state.nav = "💊 Prescriptions"
        if st.button("🧪 Health Lab"): st.session_state.nav = "🧪 Health Lab"
        if st.button("📍 Nearby Clinics"): st.session_state.nav = "📍 Clinics"
        if st.button("📞 Video Call"): st.session_state.nav = "📞 Video"
        if st.button("⭐ Give Feedback"): st.session_state.nav = "⭐ Feedback"
    else:
        if st.button("📊 Clinical Analytics"): st.session_state.nav = "📊 Analytics"
        if st.button("🖥️ Consultation Desk"): st.session_state.nav = "🖥️ Desk"
        if st.button("✍️ Issue Prescription"): st.session_state.nav = "✍️ Prescription"
        if st.button("📂 AI Summarizer"): st.session_state.nav = "📂 Summarizer"
    
    st.divider()
    if st.button("🗑️ Clear Chat"): 
        st.session_state.msgs, st.session_state.active_doc = [], ""
        st.rerun()
    if st.button("🚪 Logout"): st.session_state.logged_in = False; st.rerun()

# --- 6. DOCTOR PORTAL (LIVE ANALYTICS) ---
if st.session_state.nav == "📊 Analytics":
    st.markdown("### 📊 Live Physician Practice Analytics")
    u_db = load_db(USER_DB, ["username", "role"])
    f_db = load_db(FEEDBACK_DB, ["Doctor", "Rating"])
    total_patients = len(u_db[u_db['role'] == 'Patient'])
    my_rating = f_db[f_db['Doctor'] == st.session_state.username]['Rating'].mean() if not f_db.empty else 5.0

    c1, c2, c3 = st.columns(3)
    with c1: st.markdown(f'<div class="metric-card"><b>Registered Patients</b><br><h2>{total_patients}</h2></div>', unsafe_allow_html=True)
    with c2: st.markdown(f'<div class="metric-card"><b>Average Rating</b><br><h2>{my_rating:.1f}/5</h2></div>', unsafe_allow_html=True)
    with c3: st.markdown('<div class="metric-card"><b>Server Status</b><br><h2>Online</h2></div>', unsafe_allow_html=True)

    g1, g2 = st.columns(2)
    with g1:
        st.plotly_chart(go.Figure(go.Indicator(mode="gauge+number", value=total_patients, gauge={'axis': {'range': [0, 100]}, 'bar': {'color': "#1e3a8a"}}, title={'text': "Total Patient Registry"})), use_container_width=True)
    with g2:
        st.plotly_chart(go.Figure(data=[go.Pie(labels=['Patients', 'Doctors'], values=[total_patients, len(u_db[u_db['role'] == 'Doctor'])], hole=.4)]), use_container_width=True)

elif st.session_state.nav == "🖥️ Desk":
    st.markdown("### 🖥️ Consultation Desk")
    sig = load_db(CALL_SIGNAL_DB, ["Caller", "Receiver", "RoomID", "Status"])
    active = sig[(sig['Receiver'] == st.session_state.username) & (sig['Status'] == 'Active')]
    if not active.empty:
        st.info(f"🔔 Incoming Request: **{active.iloc[0]['Caller']}**")
        if st.button("✅ Join Consultation"): st.session_state.d_room = active.iloc[0]['RoomID']
    if "d_room" in st.session_state:
        st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.d_room}" width="100%" height="650px" allow="camera; microphone; fullscreen; display-capture"></iframe>', height=700)

elif st.session_state.nav == "📂 Summarizer":
    st.markdown("### 📂 AI Clinical Summarizer")
    up_s = st.file_uploader("Upload Report (PDF)", type=['pdf'])
    if up_s:
        with pdfplumber.open(up_s) as f:
            text_s = " ".join([p.extract_text() for p in f.pages if p.extract_text()])
            if st.button("🚀 Process Clinical Summary"):
                res = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": "You are a physician. Summarize this report for a doctor's review."}, {"role": "user", "content": text_s}])
                st.info(res.choices[0].message.content)

# --- 7. PATIENT CONTENT & TOOLS ---
elif st.session_state.nav == "💬 AI Chat":
    up_p = st.file_uploader("Upload Report", type=['pdf'])
    if up_p:
        with pdfplumber.open(up_p) as f:
            st.session_state.active_doc = " ".join([p.extract_text() for p in f.pages if p.extract_text()])
    for m in st.session_state.msgs:
        st.markdown(f'<div class="{"user-msg" if m["role"]=="user" else "ai-msg"}">{m["content"]}</div>', unsafe_allow_html=True)
    v = st.audio_input("🎤", key=f"v_{st.session_state.audio_key}")
    q = st.chat_input("Ask health query...")
    if q or v:
        final_q = q if q else Groq(api_key=GROQ_API_KEY).audio.transcriptions.create(file=("a.wav", v.getvalue()), model="whisper-large-v3", response_format="text")
        st.session_state.msgs.append({"role": "user", "content": final_q})
        ans = Groq(api_key=GROQ_API_KEY).chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": f"Medical AI. Context: {st.session_state.active_doc}"}] + st.session_state.msgs)
        st.session_state.msgs.append({"role": "assistant", "content": ans.choices[0].message.content})
        st.session_state.audio_key += 1
        st.rerun()

elif st.session_state.nav == "🧪 Health Lab":
    st.markdown("### 🧪 Personal Health Lab")
    tool = st.selectbox("Tool", ["⚖️ BMI Analyzer", "🩸 Glucose Tracker"])
    if tool == "⚖️ BMI Analyzer":
        
        w, h = st.number_input("Weight (kg)", 70), st.number_input("Height (cm)", 175)
        bmi = round(w / ((h/100)**2), 1)
        st.plotly_chart(go.Figure(go.Indicator(mode="gauge+number", value=bmi, title={'text': "Current BMI"})), use_container_width=True)

elif st.session_state.nav == "📞 Video":
    u_db = load_db(USER_DB, ["username", "role"])
    docs = u_db[u_db['role'] == "Doctor"]['username'].tolist()
    target = st.selectbox("Select Specialist", docs)
    if st.button("Start Call"):
        room = f"IntelliCare-{st.session_state.username}-{target}"
        pd.DataFrame([{"Caller": st.session_state.username, "Receiver": target, "RoomID": room, "Status": "Active"}]).to_csv(CALL_SIGNAL_DB, index=False)
        st.session_state.p_room = room
    if "p_room" in st.session_state:
        st.components.v1.html(f'<iframe src="https://meet.jit.si/{st.session_state.p_room}" width="100%" height="650px" allow="camera; microphone; fullscreen; display-capture"></iframe>', height=700)

elif st.session_state.nav == "✍️ Prescription":
    u_db = load_db(USER_DB, ["username", "role"])
    patients = u_db[u_db['role'] == "Patient"]['username'].tolist()
    with st.form("rx"):
        target = st.selectbox("Select Patient", patients)
        meds = st.text_area("Prescription Notes")
        if st.form_submit_button("Send"):
            pd.concat([load_db(RX_DB, ["Date", "Doctor", "Patient", "Notes"]), pd.DataFrame([{"Date": datetime.now().strftime("%Y-%m-%d"), "Doctor": st.session_state.username, "Patient": target, "Notes": meds}])]).to_csv(RX_DB, index=False)
            st.success("Sent!")