Update frontend.py

frontend.py

@@ -9,51 +9,23 @@ 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 "history" not in st.session_state:
-    st.session_state.history = []
-if "dark_mode" not in st.session_state:
-    st.session_state.dark_mode = False
+if "page" not in st.session_state:
+    st.session_state.page = "Home"
+if "review" not in st.session_state:
+    st.session_state.review = ""
 
-# Sidebar
+# Navigation
 with st.sidebar:
-    st.header("⚙️ Settings")
-    st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode)
-
-    sentiment_model = st.selectbox("📊 Sentiment Model", [
-        "distilbert-base-uncased-finetuned-sst-2-english",
-        "nlptown/bert-base-multilingual-uncased-sentiment"
-    ])
-
-    industry = st.selectbox("🏭 Industry Context", [
-        "Generic", "E-commerce", "Healthcare", "Education", "Travel", "Banking", "Insurance"
-    ])
-
-    product_category = st.selectbox("🧩 Product Category", [
-        "General", "Mobile Devices", "Laptops", "Healthcare Devices", "Banking App",
-        "Travel Service", "Educational Tool", "Insurance Portal"
-    ])
-
-    device_type = st.selectbox("💻 Device Type", [
-        "Web", "Android", "iOS", "Desktop", "Smartwatch", "Kiosk"
-    ])
-
-    use_aspects = st.checkbox("📊 Enable Aspect-Based Analysis")
-    use_smart_summary = st.checkbox("🧠 Use Smart Summary (clustered key points)")
-    use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk CSV")
-
-    follow_up = st.text_input("🔁 Follow-up Question")
-    voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"])
-    backend_url = st.text_input("🖥️ Backend URL", value="http://0.0.0.0:8000")
-    api_token = st.text_input("🔐 API Token", type="password")
-
-# Tabs
-tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])
+    st.title("🧭 Navigation")
+    st.session_state.page = st.radio("Go to", ["Home", "Single Review", "Bulk CSV"])
 
+# Text-to-speech
 def speak(text, lang='en'):
     tts = gTTS(text, lang=lang)
     mp3 = BytesIO()
@@ -63,100 +35,103 @@ def speak(text, lang='en'):
     mp3.seek(0)
     return mp3
 
-# Tab: Single Review
-with tab1:
-    st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer")
-
-    review = st.session_state.get("review", "")
-    review = st.text_area("📝 Enter a Review", value=review, height=160)
-
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        analyze = st.button("🔍 Analyze")
-    with col2:
-        if st.button("🎲 Example"):
-            st.session_state["review"] = "App was smooth, but the transaction failed twice on Android."
-            st.rerun()
-    with col3:
-        if st.button("🕹️ Clear"):
-            st.session_state["review"] = ""
-            st.rerun()
-
-    if analyze and review:
+# Page: Home
+if st.session_state.page == "Home":
+    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>
+    """, 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..."):
-            try:
-                payload = {
-                    "text": review,
-                    "model": sentiment_model,
-                    "industry": industry,
-                    "aspects": use_aspects,
-                    "follow_up": follow_up,
-                    "product_category": product_category,
-                    "device": device_type
-                }
-                headers = {"X-API-Key": api_token} if api_token else {}
-                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:
-                    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.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"❌ API Error: {res.status_code}")
-            except Exception as e:
-                st.error(f"🚫 {e}")
+            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}")
 
-# Tab: Bulk CSV
-with tab2:
-    st.title("📚 Bulk CSV Upload")
+# 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:
-        try:
-            df = pd.read_csv(uploaded_file)
-            if "review" in df.columns:
-                st.success(f"✅ Loaded {len(df)} reviews")
-
-                for col in ["industry", "product_category", "device"]:
-                    if col not in df.columns:
-                        df[col] = ["Generic" if col == "industry" else ""] * len(df)
-                    df[col] = df[col].fillna("").astype(str)
-
-                if st.button("📊 Analyze Bulk Reviews"):
-                    with st.spinner("Processing..."):
-                        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} if api_token else {}
-                        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}")
-            else:
-                st.error("CSV must contain a column named `review`.")
-        except Exception as e:
-            st.error(f"❌ File Error: {e}")
+        df = pd.read_csv(uploaded_file)
+        if "review" not in df.columns:
+            st.error("CSV must have a 'review' column")
+        else:
+            st.success(f"✅ {len(df)} reviews loaded")
+            df.fillna("", inplace=True)
+            if st.button("📊 Run Bulk Analysis"):
+                with st.spinner("Running..."):
+                    payload = {
+                        "reviews": df["review"].tolist(),
+                        "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
+                    }
+                    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)
+                    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")
+                    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`)
+        """)