Update frontend.py

frontend.py

@@ -23,7 +23,10 @@ defaults = {
     "intelligence_mode": True,
     "trigger_example_analysis": False,
     "last_response": None,
-    "followup_answer": None
+    "followup_answer": None,
+    "use_aspects": False,
+    "use_explain_bulk": False
+
 }
 for k, v in defaults.items():
     if k not in st.session_state:
@@ -53,8 +56,8 @@ with st.sidebar:
     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)")
+    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"])
@@ -90,12 +93,13 @@ 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,
                     "product_category": product_category,
                     "verbosity": verbosity,
-                    "aspects": use_aspects,
+                    "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")}
@@ -123,8 +127,12 @@ with tab1:
             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"]))
+        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({
@@ -197,8 +205,10 @@ with tab2:
             "industry": None,
             "product_category": None,
             "device": None,
-            "aspects": use_aspects,
+            "aspects": st.session_state.use_aspects,
             "intelligence": st.session_state.intelligence_mode
+            "explain_bulk": st.session_state.use_explain_bulk
+
         }
         try:
             res = requests.post(f"{backend_url}/bulk/?token={api_token}", json=payload)