Update frontend.py

frontend.py

@@ -12,19 +12,20 @@ st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")
 if os.path.exists("logo.png"):
     st.image("logo.png", width=180)
 
-# Session variables
-for key, default in {
+# Session state setup
+defaults = {
     "review": "",
     "dark_mode": False,
     "intelligence_mode": True,
     "trigger_example_analysis": False,
     "last_response": None,
-    "followup_answer": None,
-}.items():
-    if key not in st.session_state:
-        st.session_state[key] = default
+    "followup_answer": None
+}
+for k, v in defaults.items():
+    if k not in st.session_state:
+        st.session_state[k] = v
 
-# Dark mode CSS
+# Dark mode styling
 if st.session_state.dark_mode:
     st.markdown("""
     <style>
@@ -43,7 +44,7 @@ if st.session_state.dark_mode:
     </style>
     """, unsafe_allow_html=True)
 
-# Sidebar controls
+# Sidebar settings
 with st.sidebar:
     st.header("⚙️ Global Settings")
     st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode)
@@ -52,18 +53,21 @@ with st.sidebar:
     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("🧪 Running in demo mode — for full access, enter a valid API key.")
+
     backend_url = st.text_input("🌐 Backend URL", value="http://localhost:8000")
 
     sentiment_model = st.selectbox("📊 Sentiment Model", [
-        "distilbert-base-uncased-finetuned-sst-2-english",
+        "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("🔬 Enable Aspect Analysis")
+    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 Function
+# TTS
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -73,10 +77,9 @@ def speak(text, lang='en'):
     mp3.seek(0)
     return mp3
 
-# Tabs
 tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])
 
-# ---- SINGLE REVIEW ----
+# ==== SINGLE REVIEW ====
 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)
@@ -106,12 +109,14 @@ with tab1:
         st.session_state.followup_answer = None
         with st.spinner("Analyzing..."):
             try:
+                model = None if sentiment_model == "Auto-detect" else sentiment_model
                 payload = {
                     "text": st.session_state.review,
-                    "model": sentiment_model,
+                    "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}
@@ -127,7 +132,7 @@ with tab1:
     if data:
         st.subheader("📌 Summary")
         st.info(data["summary"])
-        st.caption(f"🧠 Summary Type: {'Smart' if use_smart_summary else 'Standard'} | {verbosity} Response")
+        st.caption(f"🧠 Summary | {verbosity} response")
         st.markdown(f"**Context:** `{data['industry']}` | `{data['product_category']}` | `Web`")
 
         st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}")
@@ -149,7 +154,7 @@ with tab1:
                         "question": custom_q,
                         "verbosity": verbosity
                     }
-                    headers = {"x-api-key": st.session_state.get("api_token", api_token)} 
+                    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")
@@ -162,12 +167,12 @@ with tab1:
         st.subheader("🔁 Follow-Up Answer")
         st.success(st.session_state.followup_answer)
 
-# ---- BULK CSV ----
+# ==== BULK CSV ====
 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>, <code>follow_up</code> (optional)
+    Upload a CSV with columns:<br>
+    <code>review</code>, <code>industry</code>, <code>product_category</code>, <code>device</code>, <code>follow_up</code> (optional)
     """, unsafe_allow_html=True)
 
     with st.expander("📄 Sample CSV"):
@@ -185,7 +190,6 @@ with tab2:
                 if "review" not in df.columns:
                     st.error("CSV must contain a `review` column.")
                 else:
-                    st.success(f"✅ Loaded {len(df)} reviews")
                     for col in ["industry", "product_category", "device", "follow_up"]:
                         if col not in df.columns:
                             df[col] = ["Auto-detect"] * len(df)
@@ -200,17 +204,16 @@ with tab2:
                             try:
                                 payload = {
                                     "reviews": df["review"].tolist(),
-                                    "model": sentiment_model,
+                                    "model": None if sentiment_model == "Auto-detect" else sentiment_model,
                                     "industry": df["industry"].tolist(),
                                     "product_category": df["product_category"].tolist(),
                                     "device": df["device"].tolist(),
                                     "follow_up": df["follow_up"].tolist(),
-                                    "intelligence": st.session_state.intelligence_mode,
+                                    "explain": use_explain_bulk,
+                                    "aspects": use_aspects,
+                                    "intelligence": st.session_state.intelligence_mode
                                 }
-                                res = requests.post(
-                                    f"{backend_url}/bulk/?token={api_token}",
-                                    json=payload
-                                )
+                                res = requests.post(f"{backend_url}/bulk/?token={api_token}", json=payload)
                                 if res.status_code == 200:
                                     results = pd.DataFrame(res.json()["results"])
                                     st.dataframe(results)
@@ -219,8 +222,8 @@ with tab2:
                                         st.plotly_chart(fig)
                                     st.download_button("⬇️ Download Results CSV", results.to_csv(index=False), "results.csv", mime="text/csv")
                                 else:
-                                    st.error(f"❌ Bulk Error {res.status_code}: {res.json().get('detail', 'Unknown error')}")
+                                    st.error(f"❌ Bulk Error {res.status_code}: {res.json().get('detail')}")
                             except Exception as e:
-                                st.error(f"🚨 Processing Error: {e}")
+                                st.error(f"🚨 Bulk Processing Error: {e}")
             except Exception as e:
                 st.error(f"❌ File Read Error: {e}")