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| 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 | |
| from datetime import datetime | |
| import uuid | |
| # Simulated in-memory storage for churn log (replace with a database or file in production) | |
| 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; | |
| } | |
| .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"])) | |
| # Add churn risk to session 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()) | |
| }) | |
| # Keep log lightweight | |
| 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.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) | |
| # 📈 Show churn trend chart (optional) | |
| if st.checkbox("📊 Show Churn Risk Trends", value=False): | |
| 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() | |
| st.markdown("#### 📅 Daily Churn Trend") | |
| fig = px.bar(trend, x="date", y=["High Risk", "Low Risk"], barmode="group", title="Daily Churn Risk Distribution") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.download_button("⬇️ Export Trend CSV", trend.to_csv(index=False), "churn_trend.csv", mime="text/csv") | |
| except Exception as e: | |
| st.error(f"❌ Failed to generate trend: {e}") | |