Update frontend.py

frontend.py

@@ -1,3 +1,4 @@
+# --- import statements remain unchanged ---
 import streamlit as st
 import requests
 import pandas as pd
@@ -8,14 +9,13 @@ from PIL import Image
 import os
 import plotly.express as px
 
+# --- Config ---
 st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")
-
-# Load logo
 logo_path = "logo.png"
 if os.path.exists(logo_path):
     st.image(logo_path, width=180)
 
-# Session State defaults
+# --- Session State ---
 if "review" not in st.session_state:
     st.session_state.review = ""
 if "dark_mode" not in st.session_state:
@@ -25,7 +25,7 @@ if "intelligence_mode" not in st.session_state:
 if "trigger_example_analysis" not in st.session_state:
     st.session_state.trigger_example_analysis = False
 
-# Apply Dark Mode Styling
+# --- Dark Mode Styling ---
 if st.session_state.dark_mode:
     st.markdown("""
         <style>
@@ -44,7 +44,7 @@ if st.session_state.dark_mode:
         </style>
     """, unsafe_allow_html=True)
 
-# Sidebar controls
+# --- Sidebar ---
 with st.sidebar:
     st.header("⚙️ Global Settings")
     st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode)
@@ -76,10 +76,11 @@ with st.sidebar:
     use_aspects = st.checkbox("🔬 Enable Aspect Analysis")
     use_smart_summary = st.checkbox("🧠 Smart Summary (Single)")
     use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk")
+    use_explain_bulk = st.checkbox("🧠 Generate Explanations (Bulk)")
     verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"])
     voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
 
-# TTS
+# --- TTS ---
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -89,13 +90,15 @@ def speak(text, lang='en'):
     mp3.seek(0)
     return mp3
 
+# --- Tabs ---
 tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])
 
-# --- SINGLE REVIEW ---
+# -------------------
+# SINGLE REVIEW TAB
+# -------------------
 with tab1:
     st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer")
     st.markdown("<div style='font-size:16px;color:#888;'>Minimum 20–50 words recommended.</div>", unsafe_allow_html=True)
-
     review = st.text_area("📝 Enter Review", value=st.session_state.review, height=180)
 
     col1, col2, col3 = st.columns(3)
@@ -132,7 +135,8 @@ with tab1:
                         "follow_up": None,
                         "product_category": product_category,
                         "verbosity": verbosity,
-                        "intelligence": st.session_state.intelligence_mode
+                        "intelligence": st.session_state.intelligence_mode,
+                        "explain": True
                     }
                     headers = {"x-api-key": st.session_state.get("api_token", api_token)}
                     params = {"smart": "1"} if use_smart_summary else {}
@@ -153,14 +157,12 @@ with tab1:
                         st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}")
                         st.info(f"💢 Emotion: {data['emotion']}")
 
-                        if data.get("aspects"):
-                            st.subheader("🔍 Aspects")
-                            for a in data["aspects"]:
-                                st.write(f"🔹 {a['aspect']}: {a['sentiment']} ({a['score']:.2%})")
+                        if data.get("explanation"):
+                            st.subheader("🧮 Explanation")
+                            st.markdown(data["explanation"])
 
-                        # --- Follow-Up Section ---
                         st.markdown("### 🔁 Got questions?")
-                        st.info("💬 You can ask a follow-up question based on this review summary.")
+                        st.info("💬 Ask a follow-up question about this review.")
                         sample_questions = [
                             "What did the user like most?",
                             "Any complaints mentioned?",
@@ -178,7 +180,11 @@ with tab1:
                                     follow = res.json().get("follow_up")
                                     if follow:
                                         st.subheader("🔁 Follow-Up Answer")
-                                        st.warning(follow)
+                                        if isinstance(follow, list):
+                                            for q in follow:
+                                                st.write("➡️", q)
+                                        else:
+                                            st.warning(follow)
                                 else:
                                     st.error(f"❌ Follow-up failed: {res.json().get('detail')}")
                     else:
@@ -186,12 +192,14 @@ with tab1:
                 except Exception as e:
                     st.error(f"🚫 Exception occurred: {e}")
 
-# --- BULK CSV ---
+# -------------------
+# BULK CSV TAB
+# -------------------
 with tab2:
     st.title("📚 Bulk CSV Upload")
     st.markdown("""
         Upload a CSV with the following columns:<br>
-        <code>review</code> (required), <code>industry</code>, <code>product_category</code>, <code>device</code> (optional)
+        <code>review</code> (required), <code>industry</code>, <code>product_category</code>, <code>device</code>, <code>follow_up</code> (optional)
     """, unsafe_allow_html=True)
 
     with st.expander("📄 Sample CSV"):
@@ -210,7 +218,7 @@ with tab2:
                     st.error("CSV must contain a `review` column.")
                 else:
                     st.success(f"✅ Loaded {len(df)} reviews")
-                    for col in ["industry", "product_category", "device"]:
+                    for col in ["industry", "product_category", "device", "follow_up"]:
                         if col not in df.columns:
                             df[col] = ["Auto-detect"] * len(df)
                         df[col] = df[col].fillna("Auto-detect").astype(str)
@@ -229,6 +237,8 @@ with tab2:
                                     "industry": df["industry"].tolist(),
                                     "product_category": df["product_category"].tolist(),
                                     "device": df["device"].tolist(),
+                                    "follow_up": df["follow_up"].tolist(),
+                                    "explain": use_explain_bulk,
                                     "intelligence": st.session_state.intelligence_mode,
                                 }
                                 res = requests.post(