frontend.py

import streamlit as st
import requests
import pandas as pd
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,
    "use_aspects": False,
    "use_explain_bulk": False

}
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"])
    st.session_state.use_aspects = st.checkbox("🔍 Detect Pain Points", value=st.session_state.get("use_aspects", False))
    st.session_state.use_explain_bulk = st.checkbox("🧠 Generate PM Insight (Bulk)", value=st.session_state.get("use_explain_bulk", False))
    verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"])

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)
    if review and (len(review.split()) < 20 or len(review.split()) > 50):
        st.warning("⚠️ For best results, keep the review between 20 to 50 words.")

    st.session_state.review = review

    analyze = False
    col1, col2, col3 = st.columns(3)
    with col1:
        analyze = st.button("🔍 Analyze", disabled=not (20 <= len(review.split()) <= 50))
    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": st.session_state.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:
                    try:
                        err_detail = res.json().get("detail", "No detail provided.")
                    except Exception:
                        err_detail = res.text
                    st.error(f"❌ Backend Error ({res.status_code}): {err_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 st.session_state.use_aspects:
            if data.get("pain_points"):
                st.error("🔍 Pain Points: " + ", ".join(data["pain_points"]))
            else:
                st.info("✅ No specific pain points were detected.")

        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}")

        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?",
                "What benefits did they mention?",
                "What made their experience smooth?"
            ]
        elif churn == "High Risk":
            suggestions = [
                "What made the user upset?",
                "Is this user likely to churn?",
                "What were the major complaints?",
                "What could improve their experience?"
            ]
        else:
            suggestions = [
                "What are the key takeaways?",
                "Is there any concern raised?",
                "Did the user express dissatisfaction?",
                "Is this feedback actionable?"
            ]

        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:
                    try:
                        err_detail = res.json().get("detail", "No detail provided.")
                    except Exception:
                        err_detail = res.text
                    st.error(f"❌ Follow-up API Error ({res.status_code}): {err_detail}")
            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")

    st.markdown("#### 📥 Upload CSV or Paste Reviews")
    uploaded_file = st.file_uploader("Upload a CSV with a 'review' column", type=["csv"])
    bulk_input = st.text_area("Or paste multiple reviews (one per line)", height=180)

    reviews = []

    if uploaded_file is not None:
        try:
            df_csv = pd.read_csv(uploaded_file)
            if "review" in df_csv.columns:
                reviews = df_csv["review"].dropna().astype(str).tolist()
            else:
                st.warning("CSV must contain a 'review' column.")
        except Exception as e:
            st.error(f"CSV error: {e}")
    elif bulk_input.strip():
        reviews = [line.strip() for line in bulk_input.split("\\n") if line.strip()]

    st.markdown("#### 🧠 Bulk Analysis Configuration")
    explain_bulk = st.checkbox("🧠 Generate Explanations", value=st.session_state.get("use_explain_bulk", False))
    enable_followups = st.checkbox("💬 Generate Follow-Up Q&A", value=True)

    if st.button("🚀 Analyze Bulk") and reviews:
        payload = {
            "reviews": reviews,
            "model": "distilbert-base-uncased-finetuned-sst-2-english" if sentiment_model == "Auto-detect" else sentiment_model,
            "industry": None,
            "product_category": None,
            "device": None,
            "aspects": st.session_state.use_aspects,
            "intelligence": st.session_state.intelligence_mode,
            "explain_bulk": explain_bulk,
            "follow_up": [["What is the issue here?", "What could be improved?"]] * len(reviews) if enable_followups else None
        }
        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)

                if any("follow_up" in r for r in results):
                    st.markdown("### 💬 Follow-Up Answers")
                    for r in results:
                        st.markdown(f"**Review:** {r['review']}")
                        if isinstance(r.get("follow_up"), list):
                            for ans in r["follow_up"]:
                                st.info(ans)
                        elif "follow_up" in r:
                            st.info(r["follow_up"])

                if "churn_risk" in df.columns:
                    st.markdown("### 📈 Churn Risk Chart")
                    churn_summary = df["churn_risk"].value_counts().reset_index()
                    churn_summary.columns = ["Churn Risk", "Count"]
                    fig = px.pie(churn_summary, names="Churn Risk", values="Count", title="Churn Risk Distribution")
                    st.plotly_chart(fig, use_container_width=True)

                st.download_button("⬇️ Export Results CSV", df.to_csv(index=False), "bulk_results.csv")
            else:
                try:
                    err_detail = res.json().get("detail", "No detail provided.")
                except Exception:
                    err_detail = res.text
                st.error(f"❌ Bulk API Error ({res.status_code}): {err_detail}")
        except Exception as e:
            st.error(f"Bulk analysis failed: {e}")

# === ROOT CAUSE & FIX (AI Product Triage) ===
tab3 = st.container()
with tab3:
    st.title("🛠️ Root Cause & Fix Suggestion")
    st.markdown("Get AI-generated issue triage from user feedback.")

    triage_input = st.text_area("📝 Paste a customer review or complaint here")

    if st.button("🤖 Analyze Root Cause"):
        if len(triage_input.strip().split()) < 5:
            st.warning("Please enter at least one complete issue or sentence.")
        else:
            with st.spinner("Analyzing..."):
                try:
                    res = requests.post(f"{backend_url}/rootcause/", json={"text": triage_input}, headers={"x-api-key": api_token})
                    if res.ok:
                        triage = res.json()
                        st.success("✅ Analysis complete")
                        st.markdown("### 🧩 Detected Problem")
                        st.info(triage.get("problem", "—"))
                        st.markdown("### 🛠️ Inferred Root Cause")
                        st.warning(triage.get("cause", "—"))
                        st.markdown("### 💡 Suggested Fix or Team")
                        st.success(triage.get("suggestion", "—"))
                    else:
                        st.error(f"API Error: {res.status_code}")
                except Exception as e:
                    st.error(f"Root cause analysis failed: {e}")