Update frontend.py

frontend.py

@@ -9,10 +9,10 @@ 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)
+# 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"):
@@ -39,34 +39,17 @@ if st.session_state.dark_mode:
         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
+# 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 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"
-    ])
+    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")
@@ -74,7 +57,7 @@ with st.sidebar:
     verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"])
     voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
 
-# TTS
+# Text-to-Speech
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -86,40 +69,34 @@ def speak(text, lang='en'):
 
 tab1, tab2 = st.tabs(["🧠 Analyze Review", "📚 Bulk Reviews"])
 
-# === SINGLE REVIEW ===
+# === 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
 
     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:
+    if col1.button("🔍 Analyze"):
         st.session_state.trigger_example_analysis = False
-        st.session_state.followup_answer = None
+    if col2.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()
+    if col3.button("🧹 Clear"):
+        for key in ["review", "last_response", "followup_answer"]:
+            st.session_state[key] = ""
+        st.rerun()
+
+    if (st.session_state.review and (st.session_state.trigger_example_analysis or st.button("Refresh"))):
         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",
+                    "model": sentiment_model if sentiment_model != "Auto-detect" else None,
                     "industry": industry,
                     "product_category": product_category,
                     "verbosity": verbosity,
@@ -128,10 +105,10 @@ with tab1:
                 }
                 headers = {"x-api-key": api_token}
                 res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers)
-                if res.status_code == 200:
+                if res.ok:
                     st.session_state.last_response = res.json()
                 else:
-                    st.error(f"API error: {res.status_code} - {res.json().get('detail')}")
+                    st.error(f"Error: {res.status_code} - {res.json().get('detail')}")
             except Exception as e:
                 st.error(f"🚫 Exception: {e}")
 
@@ -139,19 +116,17 @@ with tab1:
     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"]:
+        if data.get("pain_points"):
             st.error("🔍 Pain Points: " + ", ".join(data["pain_points"]))
-        # Add churn risk to session log
+
+        # Add to churn log
         try:
             st.session_state.churn_log.append({
                 "timestamp": datetime.now(),
@@ -159,9 +134,8 @@ with tab1:
                 "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:]
+            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}")
 
@@ -170,66 +144,60 @@ with tab1:
         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?"
-            ]
+            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:
-            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}")
+            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:
+            follow_payload = {"text": st.session_state.review, "question": q_input, "verbosity": verbosity}
+            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 answer.")
+
+        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()
+                st.markdown("#### 📅 Daily Churn Trend")
+                fig = px.bar(trend, x="date", y=["High Risk", "Low Risk"], 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": sentiment_model if sentiment_model != "Auto-detect" else None,
+            "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}")