Update frontend.py

frontend.py

@@ -22,6 +22,8 @@ if "dark_mode" not in st.session_state:
     st.session_state.dark_mode = False
 if "intelligence_mode" not in st.session_state:
     st.session_state.intelligence_mode = True
+if "trigger_example_analysis" not in st.session_state:
+    st.session_state.trigger_example_analysis = False
 
 # Apply Dark Mode Styling
 if st.session_state.dark_mode:
@@ -48,13 +50,11 @@ with st.sidebar:
     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)
 
-    DEFAULT_DEMO_TOKEN = "my-secret-key"  # 💡 replace with secure demo token
+    DEFAULT_DEMO_TOKEN = "my-secret-key"
     api_token = st.text_input("🔐 API Token", value=DEFAULT_DEMO_TOKEN, type="password")
-     # 🟡 Insert this warning right after the token field
     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", [
@@ -76,12 +76,10 @@ 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")
-
     verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"])
-    follow_up = st.text_input("🔁 Follow-up Question")
     voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
 
-# Text-to-Speech
+# TTS
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -105,16 +103,24 @@ with tab1:
         analyze = st.button("🔍 Analyze", use_container_width=True, disabled=not api_token)
     with col2:
         if st.button("🎲 Example", use_container_width=True):
-            st.session_state.review = "I love this phone! Super fast performance, great battery, and smooth UI."
+            st.session_state.review = (
+                "I love this phone! Super fast performance, great battery, and smooth UI. "
+                "Camera is awesome too, though the price is a bit high. Overall, very happy."
+            )
+            st.session_state.trigger_example_analysis = True
             st.rerun()
     with col3:
         if st.button("🧹 Clear", use_container_width=True):
             st.session_state.review = ""
             st.rerun()
 
+    if st.session_state.trigger_example_analysis and st.session_state.review:
+        analyze = True
+        st.session_state.trigger_example_analysis = False
+
     if analyze and review:
-        if len(review.split()) < 50:
-            st.warning("⚠️ Please enter at least 50 words.")
+        if len(review.split()) < 20:
+            st.warning("⚠️ Please enter at least 20 words.")
         else:
             with st.spinner("Analyzing..."):
                 try:
@@ -123,7 +129,7 @@ with tab1:
                         "model": sentiment_model,
                         "industry": industry,
                         "aspects": use_aspects,
-                        "follow_up": follow_up,
+                        "follow_up": None,
                         "product_category": product_category,
                         "verbosity": verbosity,
                         "intelligence": st.session_state.intelligence_mode
@@ -152,19 +158,34 @@ with tab1:
                             for a in data["aspects"]:
                                 st.write(f"🔹 {a['aspect']}: {a['sentiment']} ({a['score']:.2%})")
 
-                        if data.get("follow_up"):
-                            st.subheader("🔁 Follow-Up Answer")
-                            st.warning(data["follow_up"])
-
-                        if data.get("explanation"):
-                            st.subheader("🧮 Explain This")
-                            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.")
+                        sample_questions = [
+                            "What did the user like most?",
+                            "Any complaints mentioned?",
+                            "Is it positive overall?",
+                            "What are the improvement areas?"
+                        ]
+                        selected_q = st.selectbox("💡 Sample Questions", ["Type your own..."] + sample_questions)
+                        custom_q = st.text_input("🔍 Ask a follow-up", value="" if selected_q == "Type your own..." else selected_q)
+
+                        if custom_q:
+                            with st.spinner("Thinking..."):
+                                payload["follow_up"] = custom_q
+                                res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params)
+                                if res.status_code == 200:
+                                    follow = res.json().get("follow_up")
+                                    if follow:
+                                        st.subheader("🔁 Follow-Up Answer")
+                                        st.warning(follow)
+                                else:
+                                    st.error(f"❌ Follow-up failed: {res.json().get('detail')}")
                     else:
                         st.error(f"❌ API Error {res.status_code}: {res.json().get('detail', 'Unknown error')}")
-
                 except Exception as e:
                     st.error(f"🚫 Exception occurred: {e}")
-  
+
 # --- BULK CSV ---
 with tab2:
     st.title("📚 Bulk CSV Upload")
@@ -194,7 +215,6 @@ with tab2:
                             df[col] = ["Auto-detect"] * len(df)
                         df[col] = df[col].fillna("Auto-detect").astype(str)
 
-                    # Replace "Auto-detect" with fallback
                     df["industry"] = df["industry"].apply(lambda x: "Generic" if x.lower() == "auto-detect" else x)
                     df["product_category"] = df["product_category"].apply(lambda x: "General" if x.lower() == "auto-detect" else x)
                     df["device"] = df["device"].apply(lambda x: "Web" if x.lower() == "auto-detect" else x)
@@ -211,21 +231,16 @@ with tab2:
                                     "device": df["device"].tolist(),
                                     "intelligence": st.session_state.intelligence_mode,
                                 }
-
-                                # ✅ Updated: Pass token as query param (NOT in headers)
                                 res = requests.post(
-                                    f"{backend_url}/bulk/?token={api_token}",
+                                    f"{backend_url}/bulk/?token={st.session_state.get('api_token', api_token)}",
                                     json=payload
                                 )
-
                                 if res.status_code == 200:
                                     results = pd.DataFrame(res.json()["results"])
                                     st.dataframe(results)
-
-                                    if "sentiment" in results:
+                                    if "sentiment" in results.columns:
                                         fig = px.pie(results, names="sentiment", title="Sentiment Distribution")
                                         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')}")