import streamlit as st
import requests
import pandas as pd
from gtts import gTTS
import base64
from io import BytesIO
from PIL import Image
import os

st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")

logo_path = os.path.join("app", "static", "logo.png")
if os.path.exists(logo_path):
    st.image(logo_path, width=160)

# Session state
if "history" not in st.session_state:
    st.session_state.history = []
if "dark_mode" not in st.session_state:
    st.session_state.dark_mode = False

# Sidebar
with st.sidebar:
    st.header("⚙️ Settings")
    st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode)

    sentiment_model = st.selectbox("📊 Sentiment Model", [
        "distilbert-base-uncased-finetuned-sst-2-english",
        "nlptown/bert-base-multilingual-uncased-sentiment"
    ])

    industry = st.selectbox("🏭 Industry Context", [
        "Generic", "E-commerce", "Healthcare", "Education", "Travel", "Banking", "Insurance"
    ])

    product_category = st.selectbox("🧩 Product Category", [
        "General", "Mobile Devices", "Laptops", "Healthcare Devices", "Banking App",
        "Travel Service", "Educational Tool", "Insurance Portal"
    ])

    device_type = st.selectbox("💻 Device Type", [
        "Web", "Android", "iOS", "Desktop", "Smartwatch", "Kiosk"
    ])

    use_aspects = st.checkbox("📈 Enable Aspect-Based Analysis")
    use_smart_summary = st.checkbox("🧠 Use Smart Summary (clustered key points)")
    use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk CSV")

    follow_up = st.text_input("🔁 Follow-up Question")
    voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
    backend_url = st.text_input("🖥️ Backend URL", value="http://0.0.0.0:8000")
    api_token = st.text_input("🔐 API Token", type="password")

# Tabs
tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])

def speak(text, lang='en'):
    tts = gTTS(text, lang=lang)
    mp3 = BytesIO()
    tts.write_to_fp(mp3)
    b64 = base64.b64encode(mp3.getvalue()).decode()
    st.markdown(f'<audio controls><source src="data:audio/mp3;base64,{b64}" type="audio/mp3"></audio>', unsafe_allow_html=True)
    mp3.seek(0)
    return mp3

# Tab: Single Review
with tab1:
    st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer")

    review = st.session_state.get("review", "")
    review = st.text_area("📝 Enter a Review", value=review, height=160)

    col1, col2, col3 = st.columns(3)
    with col1:
        analyze = st.button("🔍 Analyze")
    with col2:
        if st.button("🎲 Example"):
            st.session_state["review"] = "App was smooth, but the transaction failed twice on Android."
            st.rerun()
    with col3:
        if st.button("🧹 Clear"):
            st.session_state["review"] = ""
            st.rerun()

    if analyze and review:
        with st.spinner("Analyzing..."):
            try:
                payload = {
                    "text": review,
                    "model": sentiment_model,
                    "industry": industry,
                    "aspects": use_aspects,
                    "follow_up": follow_up,
                    "product_category": product_category,
                    "device": device_type
                }
                headers = {"X-API-Key": api_token} if api_token else {}
                params = {"smart": "1"} if use_smart_summary else {}
                res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params)
                if res.status_code == 200:
                    data = res.json()
                    st.success("✅ Analysis Complete")
                    st.subheader("📌 Summary")
                    st.info(data["summary"])
                    st.caption(f"🧠 Summary Type: {'Smart Summary' if use_smart_summary else 'Standard Model'}")
                    st.subheader("🔊 Audio")
                    audio = speak(data["summary"], lang=voice_lang)
                    st.download_button("⬇️ Download Summary Audio", audio.read(), "summary.mp3", mime="audio/mp3")
                    st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}")
                    st.info(f"💢 Emotion: {data['emotion']}")
                    if data.get("aspects"):
                        st.subheader("🔬 Aspects")
                        for a in data["aspects"]:
                            st.write(f"🔹 {a['aspect']}: {a['sentiment']} ({a['score']:.2%})")
                    if data.get("follow_up"):
                        st.subheader("🤖 Follow-Up Response")
                        st.warning(data["follow_up"])
                else:
                    st.error(f"❌ API Error: {res.status_code}")
            except Exception as e:
                st.error(f"🚫 {e}")

# Tab: Bulk CSV
with tab2:
    st.title("📚 Bulk CSV Upload")
    uploaded_file = st.file_uploader("Upload CSV with `review` column", type="csv")
    if uploaded_file:
        try:
            df = pd.read_csv(uploaded_file)
            if "review" in df.columns:
                st.success(f"✅ Loaded {len(df)} reviews")

                for col in ["industry", "product_category", "device"]:
                    if col not in df.columns:
                        df[col] = [""] * len(df)
                    df[col] = df[col].fillna("").astype(str)

                if st.button("📊 Analyze Bulk Reviews"):
                    with st.spinner("Processing..."):
                        payload = {
                            "reviews": df["review"].tolist(),
                            "model": sentiment_model,
                            "aspects": use_aspects,
                            "industry": df["industry"].tolist(),
                            "product_category": df["product_category"].tolist(),
                            "device": df["device"].tolist()
                        }
                        headers = {"X-API-Key": api_token} if api_token else {}
                        params = {"smart": "1"} if use_smart_summary_bulk else {}

                        res = requests.post(f"{backend_url}/bulk/", json=payload, headers=headers, params=params)
                        if res.status_code == 200:
                            results = pd.DataFrame(res.json()["results"])
                            results["summary_type"] = "Smart" if use_smart_summary_bulk else "Standard"
                            st.dataframe(results)
                            st.download_button("⬇️ Download Results CSV", results.to_csv(index=False), "bulk_results.csv", mime="text/csv")
                        else:
                            st.error(f"❌ Bulk Analysis Failed: {res.status_code}")
            else:
                st.error("CSV must contain a column named `review`.")
        except Exception as e:
            st.error(f"❌ File Error: {e}")