Update frontend.py

frontend.py

@@ -9,23 +9,49 @@ import os
 
 st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")
 
-# Optional logo
 logo_path = os.path.join("app", "static", "logo.png")
 if os.path.exists(logo_path):
     st.image(logo_path, width=160)
 
-# Session state
-if "page" not in st.session_state:
-    st.session_state.page = "Home"
+# Session state defaults
 if "review" not in st.session_state:
     st.session_state.review = ""
+if "dark_mode" not in st.session_state:
+    st.session_state.dark_mode = False
 
-# Navigation
+# Shared Sidebar Controls
 with st.sidebar:
-    st.title("🧭 Navigation")
-    st.session_state.page = st.radio("Go to", ["Home", "Single Review", "Bulk CSV"])
+    st.header("⚙️ Global Settings")
+    st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode)
+
+    api_token = st.text_input("🔐 API Token", type="password")
+    backend_url = st.text_input("🖥️ Backend URL", value="http://0.0.0.0:8000")
+
+    sentiment_model = st.selectbox("📊 Sentiment Model", [
+        "distilbert-base-uncased-finetuned-sst-2-english",
+        "nlptown/bert-base-multilingual-uncased-sentiment"
+    ])
+
+    industry = st.selectbox("🏭 Industry Context", [
+        "Auto-detect", "Generic", "E-commerce", "Healthcare", "Education", "Travel", "Banking", "Insurance", "Gaming", "Food Delivery", "Real Estate", "Fitness", "Entertainment"
+    ])
+
+    product_category = st.selectbox("🧩 Product Category", [
+        "Auto-detect", "General", "Mobile Devices", "Laptops", "Healthcare Devices", "Banking App", "Travel Service", "Educational Tool", "Insurance Portal", "Streaming App", "Wearables", "Home Appliances", "Food Apps"
+    ])
+
+    device_type = st.selectbox("💻 Device Type", [
+        "Auto-detect", "Web", "Android", "iOS", "Desktop", "Smartwatch", "Kiosk"
+    ])
+
+    use_aspects = st.checkbox("🔬 Enable Aspect Analysis")
+    use_smart_summary = st.checkbox("🧠 Use Smart Summary (Single)")
+    use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk")
+    follow_up = st.text_input("🔁 Follow-up Question")
+    voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
+
+# Text-to-Speech Helper
 
-# Text-to-speech
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -35,103 +61,129 @@ def speak(text, lang='en'):
     mp3.seek(0)
     return mp3
 
-# Page: Home
-if st.session_state.page == "Home":
+# Tabs for modes
+tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])
+
+# --- SINGLE REVIEW MODE ---
+with tab1:
+    st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer")
     st.markdown("""
-        <div style='text-align: center;'>
-            <h1 style='font-size: 48px;'>🧠 NeuroPulse AI</h1>
-            <p style='font-size: 20px;'>Smarter feedback analyzer using GenAI for Summarization, Sentiment, Emotion, Aspects, and GPT Q&A</p>
-            <a href='https://huggingface.co/spaces/your-space-name' target='_blank' style='text-decoration: none;'>
-                <button style='padding: 12px 30px; font-size: 18px; border-radius: 8px; background: linear-gradient(90deg, #6366f1, #4f46e5); color: white; border: none;'>🚀 Try App</button>
-            </a>
-        </div>
+        <div style='font-size:16px;color:#555;'>Minimum 50–100 words recommended for optimal insights.</div>
     """, unsafe_allow_html=True)
 
-# Page: Single Review
-elif st.session_state.page == "Single Review":
-    st.title("🧠 Analyze Single Review")
-
-    with st.expander("⚙️ Settings"):
-        sentiment_model = st.selectbox("Sentiment Model", [
-            "distilbert-base-uncased-finetuned-sst-2-english",
-            "nlptown/bert-base-multilingual-uncased-sentiment"])
-        industry = st.selectbox("Industry", ["Generic", "E-commerce", "Healthcare"])
-        product_category = st.text_input("Product Category", "General")
-        device = st.text_input("Device", "Web")
-        use_aspects = st.checkbox("Enable Aspect Analysis")
-        use_smart = st.checkbox("Use Smart Summary")
-        follow_up = st.text_input("Follow-up Question")
-        voice_lang = st.selectbox("Voice Language", ["en", "fr", "es"])
-        backend_url = st.text_input("Backend URL", "http://0.0.0.0:8000")
-        api_token = st.text_input("API Token", type="password")
-
-    st.session_state.review = st.text_area("📝 Enter your review", value=st.session_state.review, height=160)
-
-    if st.button("🔍 Analyze") and st.session_state.review:
-        with st.spinner("Analyzing..."):
-            payload = {
-                "text": st.session_state.review,
-                "model": sentiment_model,
-                "industry": industry,
-                "aspects": use_aspects,
-                "follow_up": follow_up,
-                "product_category": product_category,
-                "device": device
-            }
-            headers = {"X-API-Key": api_token}
-            params = {"smart": "1"} if use_smart else {}
-            res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params)
-            if res.status_code == 200:
-                out = res.json()
-                st.success("✅ Done")
-                st.markdown(f"### 📌 Summary\n{out['summary']}")
-                st.caption(f"Smart Summary: {use_smart}")
-                audio = speak(out["summary"], lang=voice_lang)
-                st.download_button("⬇️ Download Audio", audio.read(), "summary.mp3")
-                st.metric("📊 Sentiment", out['sentiment']['label'], f"{out['sentiment']['score']:.2%}")
-                st.info(f"💢 Emotion: {out['emotion']}")
-                if out.get("aspects"):
-                    st.markdown("### 🔍 Aspects")
-                    for asp in out["aspects"]:
-                        st.write(f"- {asp['aspect']}: {asp['sentiment']} ({asp['score']:.2%})")
-                if out.get("follow_up"):
-                    st.warning(f"🧠 GPT: {out['follow_up']}")
-            else:
-                st.error(f"❌ Error: {res.status_code}")
-
-# Page: Bulk CSV
-elif st.session_state.page == "Bulk CSV":
-    st.title("📚 Analyze CSV in Bulk")
-    uploaded_file = st.file_uploader("Upload CSV with `review` column", type="csv")
-    if uploaded_file:
-        df = pd.read_csv(uploaded_file)
-        if "review" not in df.columns:
-            st.error("CSV must have a 'review' column")
+    review = st.text_area("📝 Enter a Review", value=st.session_state.review, height=180)
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        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 = "App was smooth, but the transaction failed twice on Android during checkout."
+            st.rerun()
+    with col3:
+        if st.button("🧹 Clear", use_container_width=True):
+            st.session_state.review = ""
+            st.rerun()
+
+    if analyze and review:
+        if len(review.split()) < 50:
+            st.error("⚠️ Please enter at least 50 words for meaningful analysis.")
         else:
-            st.success(f"✅ {len(df)} reviews loaded")
-            df.fillna("", inplace=True)
-            if st.button("📊 Run Bulk Analysis"):
-                with st.spinner("Running..."):
+            with st.spinner("Analyzing..."):
+                try:
                     payload = {
-                        "reviews": df["review"].tolist(),
+                        "text": review,
                         "model": sentiment_model,
-                        "industry": df["industry"].tolist() if "industry" in df else ["Generic"]*len(df),
-                        "product_category": df["product_category"].tolist() if "product_category" in df else [""]*len(df),
-                        "device": df["device"].tolist() if "device" in df else [""]*len(df),
-                        "aspects": use_aspects
+                        "industry": industry,
+                        "aspects": use_aspects,
+                        "follow_up": follow_up,
+                        "product_category": product_category,
+                        "device": device_type
                     }
                     headers = {"X-API-Key": api_token}
-                    params = {"smart": "1"} if use_smart else {}
-                    res = requests.post(f"{backend_url}/bulk/", json=payload, headers=headers, params=params)
+                    params = {"smart": "1"} if use_smart_summary else {}
+                    res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params)
                     if res.status_code == 200:
-                        results = pd.DataFrame(res.json()["results"])
-                        st.dataframe(results)
-                        st.download_button("⬇️ Download CSV", results.to_csv(index=False), "results.csv")
+                        data = res.json()
+                        st.success("✅ Analysis Complete")
+                        st.subheader("📌 Summary")
+                        st.info(data["summary"])
+                        st.caption(f"🧠 Summary Type: {'Smart Summary' if use_smart_summary else 'Standard Model'}")
+                        st.markdown(f"**Context:** {industry} | {product_category} | {device_type}")
+                        st.subheader("🔊 Audio")
+                        audio = speak(data["summary"], lang=voice_lang)
+                        st.download_button("⬇️ Download Summary Audio", audio.read(), "summary.mp3", mime="audio/mp3")
+                        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("follow_up"):
+                            st.subheader("🧠 Follow-Up Response")
+                            st.warning(data["follow_up"])
                     else:
-                        st.error(f"❌ Failed: {res.status_code}")
-    with st.expander("📄 Sample CSV"):
-        st.markdown("""
-            Download sample CSV [here](https://huggingface.co/datasets/hasi-labs/sample-neuropulse-csv/raw/main/sample.csv)
-            
-            Required column: `review` (Optional: `industry`, `product_category`, `device`)
-        """)
+                        st.error(f"❌ API Error: {res.status_code}")
+                except Exception as e:
+                    st.error(f"🚫 {e}")
+
+# --- BULK REVIEW MODE ---
+with tab2:
+    st.title("📚 Bulk CSV Upload")
+    st.markdown("""
+        <div style='font-size:16px;'>Upload a CSV with the following columns:<br>
+        <code>review</code> <span style='color:#aaa;'>(required)</span>,
+        <code>industry</code>, <code>product_category</code>, <code>device</code> <span style='color:#aaa;'>(optional)</span></div>
+    """, unsafe_allow_html=True)
+
+    sample_df = pd.DataFrame({
+        "review": ["This app helps me manage my medical appointments smoothly.", "The mobile battery drains fast when using the banking app.", "Streaming service loads slowly on Smart TV."],
+        "industry": ["Healthcare", "Banking", "Entertainment"],
+        "product_category": ["Healthcare Devices", "Banking App", "Streaming App"],
+        "device": ["Android", "iOS", "Smartwatch"]
+    })
+    sample_csv = sample_df.to_csv(index=False).encode("utf-8")
+    with st.expander("📎 Sample CSV"):
+        st.download_button("⬇️ Download sample CSV", sample_csv, "sample_reviews.csv", mime="text/csv")
+
+    uploaded_file = st.file_uploader("📁 Upload your CSV", type="csv")
+
+    if uploaded_file and api_token:
+        try:
+            df = pd.read_csv(uploaded_file)
+            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"]:
+                    if col not in df.columns:
+                        df[col] = [industry if industry != "Auto-detect" else "Generic"] * len(df)
+                    df[col] = df[col].fillna("").astype(str)
+
+                if st.button("📊 Analyze Bulk Reviews", use_container_width=True):
+                    with st.spinner("Processing CSV..."):
+                        try:
+                            payload = {
+                                "reviews": df["review"].tolist(),
+                                "model": sentiment_model,
+                                "aspects": use_aspects,
+                                "industry": df["industry"].tolist(),
+                                "product_category": df["product_category"].tolist(),
+                                "device": df["device"].tolist()
+                            }
+                            headers = {"X-API-Key": api_token}
+                            params = {"smart": "1"} if use_smart_summary_bulk else {}
+
+                            res = requests.post(f"{backend_url}/bulk/", json=payload, headers=headers, params=params)
+                            if res.status_code == 200:
+                                results = pd.DataFrame(res.json()["results"])
+                                results["summary_type"] = "Smart" if use_smart_summary_bulk else "Standard"
+                                st.dataframe(results)
+                                st.download_button("⬇️ Download Results CSV", results.to_csv(index=False), "bulk_results.csv", mime="text/csv")
+                            else:
+                                st.error(f"❌ Bulk Analysis Failed: {res.status_code}")
+                        except Exception as e:
+                            st.error(f"💥 Error: {e}")
+        except Exception as e:
+            st.error(f"❌ File Error: {e}")