import streamlit as st
import requests
import pandas as pd
import azure.cognitiveservices.speech as speechsdk
import tempfile
import os
import plotly.express as px
from datetime import datetime
import uuid

# Simulated in-memory storage for churn log
if "churn_log" not in st.session_state:
    st.session_state.churn_log = []

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;
    }
    </style>
    """, unsafe_allow_html=True)

# Sidebar config
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 api_token.strip() == "my-secret-key":
        st.warning("🧪 Demo Mode — Not all features are active. Add your API token to unlock full features.")
    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"])
    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"])

# Text-to-Speech
# Setup usage tracking
if "tts_usage_count" not in st.session_state:
    st.session_state.tts_usage_count = 0
if "enable_audio" not in st.session_state:
    st.session_state.enable_audio = False

# Azure TTS function
def azure_speak(text, lang='en-US'):
    import os
    if "HF_SPACE_ID" in os.environ:
        st.warning("Azure TTS is not supported on Hugging Face Spaces. Using fallback TTS.")
        return None

    if st.session_state.tts_usage_count > 20:
        st.warning("🔇 TTS usage limit reached.")
        return None

    try:
        import azure.cognitiveservices.speech as speechsdk
        speech_config = speechsdk.SpeechConfig(
            subscription=st.secrets["AZURE_SPEECH_KEY"],
            region=st.secrets["AZURE_REGION"]
        )
        speech_config.speech_synthesis_language = lang
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmpfile:
            audio_config = speechsdk.audio.AudioOutputConfig(filename=tmpfile.name)
            synthesizer = speechsdk.SpeechSynthesizer(speech_config, audio_config)
            result = synthesizer.speak_text_async(text).get()
        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
            st.session_state.tts_usage_count += 1
            return tmpfile.name
        else:
            st.error("❌ Azure TTS failed.")
            return None
    except Exception as e:
        st.error(f"Azure TTS error: {e}")
        return None



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

# === SINGLE REVIEW ANALYSIS ===
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

    analyze = False
    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 st.session_state.review and (analyze or st.session_state.get("trigger_example_analysis")):
        with st.spinner("Analyzing feedback..."):
            try:
                model_used = None if sentiment_model == "Auto-detect" else sentiment_model
                payload = {
                    "text": st.session_state.review,
                    "model": model_used or "distilbert-base-uncased-finetuned-sst-2-english",
                    "industry": industry,
                    "product_category": product_category,
                    "verbosity": verbosity,
                    "aspects": use_aspects,
                    "intelligence": st.session_state.get("intelligence_mode", False)
                }
                headers = {"x-api-key": st.session_state.get("api_token", "my-secret-key")}
                res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers)
                if res.ok:
                    st.session_state.last_response = res.json()
                else:
                    st.error(f"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.markdown(f"**Industry:** `{data['industry']}` | **Category:** `{data['product_category']}` | **Device:** Web")
        st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}")
        st.progress(data["sentiment"]["score"])
        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 data.get("pain_points"):
            st.error("🔍 Pain Points: " + ", ".join(data["pain_points"]))

        # Add to churn log
        try:
            st.session_state.churn_log.append({
                "timestamp": datetime.now(),
                "product": data.get("product_category", "General"),
                "churn_risk": data.get("churn_risk", "Unknown"),
                "session_id": str(uuid.uuid4())
            })
            if len(st.session_state.churn_log) > 1000:
                st.session_state.churn_log = st.session_state.churn_log[-1000:]
        except Exception as e:
            st.warning(f"🧪 Logging failed: {e}")

        # Only show toggle and button for audio
        st.subheader("🔊 Audio Summary")
        st.session_state.enable_audio = st.toggle("🎧 Generate Audio Summary")

        if st.session_state.enable_audio:
            if st.session_state.tts_usage_count > 20:
                st.warning("🔇 Azure TTS usage limit reached for this session.")
            else:
                if st.button("▶️ Generate & Play Audio"):
                    audio_path = azure_speak(data["summary"], lang=f"{voice_lang}-US")
                    if audio_path:
                        audio_bytes = open(audio_path, "rb").read()
                        st.audio(audio_bytes, format="audio/mp3")
                        st.download_button("⬇️ Download Audio", audio_bytes, "summary.mp3")

        st.markdown("### 🔁 Ask a Follow-Up")
        sentiment = data["sentiment"]["label"].lower()
        churn = data.get("churn_risk", "")
        pain = data.get("pain_points", [])
        if sentiment == "positive" and churn == "Low Risk":
            suggestions = ["What features impressed the user?", "Would they recommend the product?"]
        elif churn == "High Risk":
            suggestions = ["What made the user upset?", "Is this user likely to churn?"]
        else:
            suggestions = ["What are the key takeaways?", "Is there any concern raised?"]

        selected_q = st.selectbox("💡 Suggested Questions", ["Type your own..."] + suggestions)
        q_input = st.text_input("🔍 Your Question") if selected_q == "Type your own..." else selected_q

        if q_input:
            try:
                follow_payload = {
                    "text": st.session_state.review,
                    "question": q_input,
                    "verbosity": verbosity
                }
                headers = {"x-api-key": api_token}

                res = requests.post(f"{backend_url}/followup/", json=follow_payload, headers=headers)
                if res.ok:
                    st.success(res.json().get("answer"))
                else:
                    st.error("Failed to get follow-up answer.")
            except Exception as e:
                st.error(f"⚠️ Follow-up error: {e}")

        if st.checkbox("📊 Show Churn Risk Trends"):
            try:
                df = pd.DataFrame(st.session_state.churn_log)
                df["date"] = pd.to_datetime(df["timestamp"]).dt.date
                trend = df.groupby(["date", "churn_risk"]).size().unstack(fill_value=0).reset_index()
                y_columns = [col for col in trend.columns if col != "date"]
                st.markdown("#### 📅 Daily Churn Trend")
                fig = px.bar(trend, x="date", y=y_columns, barmode="group")
                st.plotly_chart(fig, use_container_width=True)
                st.download_button("⬇️ Export Trend CSV", trend.to_csv(index=False), "churn_trend.csv")
            except Exception as e:
                st.error(f"Trend error: {e}")


# === BULK REVIEW ANALYSIS ===
with tab2:
    st.title("📚 Bulk Feedback Analysis")
    bulk_input = st.text_area("📥 Paste multiple reviews (one per line)", height=250)
    if st.button("🚀 Analyze Bulk"):
        lines = [l.strip() for l in bulk_input.strip().splitlines() if l.strip()]
        payload = {
            "reviews": lines,
            "model": "distilbert-base-uncased-finetuned-sst-2-english" if sentiment_model == "Auto-detect" else sentiment_model,
            "industry": None,
            "product_category": None,
            "device": None,
            "aspects": use_aspects,
            "intelligence": st.session_state.intelligence_mode
        }
        try:
            res = requests.post(f"{backend_url}/bulk/?token={api_token}", json=payload)
            if res.ok:
                results = res.json().get("results", [])
                df = pd.DataFrame(results)
                st.dataframe(df)
                st.download_button("⬇️ Export Results CSV", df.to_csv(index=False), "bulk_results.csv")
            else:
                st.error(f"API Error: {res.status_code}")
        except Exception as e:
            st.error(f"Bulk analysis failed: {e}")