frontend.py

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
import plotly.express as px

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

# Load logo
logo_path = "logo.png"
if os.path.exists(logo_path):
    st.image(logo_path, width=180)

# Session State defaults
if "review" not in st.session_state:
    st.session_state.review = ""
if "dark_mode" not in st.session_state:
    st.session_state.dark_mode = False
if "intelligence_mode" not in st.session_state:
    st.session_state.intelligence_mode = True
if "trigger_example_analysis" not in st.session_state:
    st.session_state.trigger_example_analysis = False

# Apply Dark Mode Styling
if st.session_state.dark_mode:
    st.markdown("""
        <style>
        html, body, [class*="st-"] {
            background-color: #121212;
            color: #f5f5f5;
        }
        .stTextInput > div > div > input,
        .stTextArea > div > textarea,
        .stSelectbox div div,
        .stDownloadButton > button,
        .stButton > button {
            background-color: #1e1e1e;
            color: white;
        }
        </style>
    """, unsafe_allow_html=True)

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

    DEFAULT_DEMO_TOKEN = "my-secret-key"
    api_token = st.text_input("🔐 API Token", value=DEFAULT_DEMO_TOKEN, type="password")
    if not api_token or api_token.strip() == "my-secret-key":
        st.warning("🧪 Running in demo mode — for full access, enter a valid API key.")

    backend_url = st.text_input("🌐 Backend URL", value="http://localhost:8000")

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

    industry = st.selectbox("🏭 Industry", [
        "Auto-detect", "Generic", "E-commerce", "Healthcare", "Education", "Travel", "Banking", "Insurance",
        "Gaming", "Food Delivery", "Real Estate", "Fitness", "Entertainment"
    ])

    product_category = st.selectbox("🧩 Product Category", [
        "Auto-detect", "General", "Mobile Devices", "Laptops", "Healthcare Devices", "Banking App",
        "Travel Service", "Educational Tool", "Insurance Portal", "Streaming App", "Wearables",
        "Home Appliances", "Food Apps"
    ])

    use_aspects = st.checkbox("🔬 Enable Aspect Analysis")
    use_smart_summary = st.checkbox("🧠 Smart Summary (Single)")
    use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk")
    verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"])
    voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])

# TTS
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

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

# --- SINGLE REVIEW ---
with tab1:
    st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer")
    st.markdown("<div style='font-size:16px;color:#888;'>Minimum 20–50 words recommended.</div>", unsafe_allow_html=True)

    review = st.text_area("📝 Enter Review", value=st.session_state.review, height=180)

    col1, col2, col3 = st.columns(3)
    with col1:
        analyze = st.button("🔍 Analyze", use_container_width=True, disabled=not api_token)
    with col2:
        if st.button("🎲 Example", use_container_width=True):
            st.session_state.review = (
                "I love this phone! Super fast performance, great battery, and smooth UI. "
                "Camera is awesome too, though the price is a bit high. Overall, very happy."
            )
            st.session_state.trigger_example_analysis = True
            st.rerun()
    with col3:
        if st.button("🧹 Clear", use_container_width=True):
            st.session_state.review = ""
            st.rerun()

    if st.session_state.trigger_example_analysis and st.session_state.review:
        analyze = True
        st.session_state.trigger_example_analysis = False

    if analyze and review:
        if len(review.split()) < 20:
            st.warning("⚠️ Please enter at least 20 words.")
        else:
            with st.spinner("Analyzing..."):
                try:
                    payload = {
                        "text": review,
                        "model": sentiment_model,
                        "industry": industry,
                        "aspects": use_aspects,
                        "follow_up": None,
                        "product_category": product_category,
                        "verbosity": verbosity,
                        "intelligence": st.session_state.intelligence_mode
                    }
                    headers = {"x-api-key": st.session_state.get("api_token", api_token)}
                    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' if use_smart_summary else 'Standard'} | {verbosity} Response")
                        st.markdown(f"**Context:** `{data['industry']}` | `{data['product_category']}` | `Web`")

                        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%})")

                        # --- Follow-Up Section ---
                        st.markdown("### 🔁 Got questions?")
                        st.info("💬 You can ask a follow-up question based on this review summary.")
                        sample_questions = [
                            "What did the user like most?",
                            "Any complaints mentioned?",
                            "Is it positive overall?",
                            "What are the improvement areas?"
                        ]
                        selected_q = st.selectbox("💡 Sample Questions", ["Type your own..."] + sample_questions)
                        custom_q = st.text_input("🔍 Ask a follow-up", value="" if selected_q == "Type your own..." else selected_q)

                        if custom_q:
                            with st.spinner("Thinking..."):
                                payload["follow_up"] = custom_q
                                res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params)
                                if res.status_code == 200:
                                    follow = res.json().get("follow_up")
                                    if follow:
                                        st.subheader("🔁 Follow-Up Answer")
                                        st.warning(follow)
                                else:
                                    st.error(f"❌ Follow-up failed: {res.json().get('detail')}")
                    else:
                        st.error(f"❌ API Error {res.status_code}: {res.json().get('detail', 'Unknown error')}")
                except Exception as e:
                    st.error(f"🚫 Exception occurred: {e}")

# --- BULK CSV ---
with tab2:
    st.title("📚 Bulk CSV Upload")
    st.markdown("""
        Upload a CSV with the following columns:<br>
        <code>review</code> (required), <code>industry</code>, <code>product_category</code>, <code>device</code> (optional)
    """, unsafe_allow_html=True)

    with st.expander("📄 Sample CSV"):
        with open("sample_reviews.csv", "rb") as f:
            st.download_button("⬇️ Download sample CSV", f, file_name="sample_reviews.csv")

    uploaded_file = st.file_uploader("📁 Upload your CSV", type="csv")

    if uploaded_file:
        if not api_token:
            st.error("🔐 Please enter your API token in the sidebar.")
        else:
            try:
                df = pd.read_csv(uploaded_file)
                if "review" not in df.columns:
                    st.error("CSV must contain a `review` column.")
                else:
                    st.success(f"✅ Loaded {len(df)} reviews")
                    for col in ["industry", "product_category", "device"]:
                        if col not in df.columns:
                            df[col] = ["Auto-detect"] * len(df)
                        df[col] = df[col].fillna("Auto-detect").astype(str)

                    df["industry"] = df["industry"].apply(lambda x: "Generic" if x.lower() == "auto-detect" else x)
                    df["product_category"] = df["product_category"].apply(lambda x: "General" if x.lower() == "auto-detect" else x)
                    df["device"] = df["device"].apply(lambda x: "Web" if x.lower() == "auto-detect" else x)

                    if st.button("📊 Analyze Bulk Reviews", use_container_width=True):
                        with st.spinner("Processing..."):
                            try:
                                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(),
                                    "intelligence": st.session_state.intelligence_mode,
                                }
                                res = requests.post(
                                    f"{backend_url}/bulk/?token={st.session_state.get('api_token', api_token)}",
                                    json=payload
                                )
                                if res.status_code == 200:
                                    results = pd.DataFrame(res.json()["results"])
                                    st.dataframe(results)
                                    if "sentiment" in results.columns:
                                        fig = px.pie(results, names="sentiment", title="Sentiment Distribution")
                                        st.plotly_chart(fig)
                                    st.download_button("⬇️ Download Results CSV", results.to_csv(index=False), "results.csv", mime="text/csv")
                                else:
                                    st.error(f"❌ Bulk Error {res.status_code}: {res.json().get('detail', 'Unknown error')}")
                            except Exception as e:
                                st.error(f"🚨 Processing Error: {e}")
            except Exception as e:
                st.error(f"❌ File Read Error: {e}")