frontend.py

import streamlit as st
import requests
import pandas as pd
from gtts import gTTS
import base64
from io import BytesIO
import os
import plotly.express as px

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

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

# Session state setup
defaults = {
    "review": "",
    "dark_mode": False,
    "intelligence_mode": True,
    "trigger_example_analysis": False,
    "last_response": None,
    "followup_answer": None
}
for k, v in defaults.items():
    if k not in st.session_state:
        st.session_state[k] = v

# 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
with st.sidebar:
    st.header("⚙️ PM Config")
    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)

    api_token = st.text_input("🔐 API Token", value="my-secret-key", type="password")
    if not api_token or api_token.strip() == "my-secret-key":
        st.warning("🧪 Demo Mode — Not all features active.")

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

    sentiment_model = st.selectbox("📊 Sentiment Model", [
        "Auto-detect", 
        "distilbert-base-uncased-finetuned-sst-2-english",
        "nlptown/bert-base-multilingual-uncased-sentiment"
    ])
    industry = st.selectbox("🏭 Industry", ["Auto-detect", "Generic", "E-commerce", "Healthcare", "Education"])
    product_category = st.selectbox("🧩 Product Category", ["Auto-detect", "General", "Mobile Devices", "Laptops"])
    use_aspects = st.checkbox("🔍 Detect Pain Points")
    use_explain_bulk = st.checkbox("🧠 Generate PM Insight (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(["🧠 Analyze Review", "📚 Bulk Reviews"])

# === SINGLE REVIEW ===
with tab1:
    st.title("📊 ChurnSight AI — Product Feedback Assistant")
    st.markdown("Analyze feedback to detect churn risk, extract pain points, and support product decisions.")

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

    col1, col2, col3 = st.columns(3)
    with col1:
        analyze = st.button("🔍 Analyze")
    with col2:
        if st.button("🎲 Example"):
            st.session_state.review = (
                "The app crashes every time I try to checkout. It's so slow and unresponsive. "
                "Customer support never replied. I'm switching to another brand."
            )
            st.session_state.trigger_example_analysis = True
            st.rerun()
    with col3:
        if st.button("🧹 Clear"):
            for key in ["review", "last_response", "followup_answer"]:
                st.session_state[key] = ""
            st.rerun()

    if (analyze or st.session_state.trigger_example_analysis) and st.session_state.review:
        st.session_state.trigger_example_analysis = False
        st.session_state.followup_answer = None
        with st.spinner("Analyzing feedback..."):
            try:
                model = None if sentiment_model == "Auto-detect" else sentiment_model
                payload = {
                    "text": st.session_state.review,
                    "model": model or "distilbert-base-uncased-finetuned-sst-2-english",
                    "industry": industry,
                    "product_category": product_category,
                    "verbosity": verbosity,
                    "aspects": use_aspects,
                    "intelligence": st.session_state.intelligence_mode
                }
                headers = {"x-api-key": api_token}
                res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers)
                if res.status_code == 200:
                    st.session_state.last_response = res.json()
                else:
                    st.error(f"API error: {res.status_code} - {res.json().get('detail')}")
            except Exception as e:
                st.error(f"🚫 Exception: {e}")

    data = st.session_state.last_response
    if data:
        st.subheader("📌 PM Insight Summary")
        st.info(data["summary"])
        st.caption("🔎 Summary Model: facebook/bart-large-cnn | " + verbosity + " response")
        st.markdown(f"**Industry:** `{data['industry']}` | **Category:** `{data['product_category']}` | **Device:** Web")

        st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}")
        st.info(f"💢 Emotion: {data['emotion']}")
        if "churn_risk" in data:
            risk = data["churn_risk"]
            color = "🔴" if risk == "High Risk" else "🟢"
            st.metric("🚨 Churn Risk", f"{color} {risk}")

        if "pain_points" in data and data["pain_points"]:
            st.error("🔍 Pain Points: " + ", ".join(data["pain_points"]))

        st.subheader("🔊 Audio Summary")
        audio = speak(data["summary"], lang=voice_lang)
        st.download_button("⬇️ Download Audio", audio.read(), "summary.mp3")

        st.markdown("### 🔁 Ask a Follow-Up")
        
        # 💡 Smarter Follow-Up Suggestion Logic
        sentiment = data["sentiment"]["label"].lower()
        churn = data.get("churn_risk", "")
        pain = data.get("pain_points", [])

        if sentiment == "positive" and churn == "Low Risk":
            sample_questions = [
                "What features impressed the user?",
                "Would they recommend the product?",
                "What delighted the customer most?"
            ]
        elif sentiment == "negative" or churn == "High Risk" or pain:
            sample_questions = [
                "What made the user upset?",
                "Any feature complaints?",
                "Is this user likely to churn?"
            ]
        else:
            sample_questions = [
                "What are the key takeaways?",
                "How can this review guide product changes?",
                "Is there any concern raised?"
            ]


        selected_q = st.selectbox("💡 Suggested Questions", ["Type your own..."] + sample_questions)
        custom_q = selected_q if selected_q != "Type your own..." else st.text_input("🔍 Follow-up Question")

        if custom_q:
            with st.spinner("Thinking..."):
                try:
                    follow_payload = {
                        "text": st.session_state.review,
                        "question": custom_q,
                        "verbosity": verbosity
                    }
                    headers = {"x-api-key": api_token}
                    res = requests.post(f"{backend_url}/followup/", json=follow_payload, headers=headers)
                    if res.status_code == 200:
                        st.session_state.followup_answer = res.json().get("answer")
                    else:
                        st.error(f"❌ Follow-up failed: {res.json().get('detail')}")
                except Exception as e:
                    st.error(f"⚠️ Follow-up error: {e}")

    if st.session_state.followup_answer:
        st.subheader("✅ Answer")
        st.success(st.session_state.followup_answer)