Update frontend.py

frontend.py

@@ -63,6 +63,7 @@ with st.sidebar:
 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.")
@@ -93,7 +94,6 @@ with tab1:
             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,
@@ -133,7 +133,6 @@ with tab1:
             else:
                 st.info("✅ No specific pain points were detected.")
 
-
         try:
             st.session_state.churn_log.append({
                 "timestamp": datetime.now(),
@@ -150,12 +149,28 @@ with tab1:
         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?"]
+            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?"]
+            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?"]
+            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
@@ -196,19 +211,40 @@ with tab1:
 # === 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()]
+
+    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": lines,
+            "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": st.session_state.use_explain_bulk
-
+            "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)
@@ -216,6 +252,24 @@ with tab2:
                 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: