Update frontend.py

frontend.py

@@ -1,162 +1,162 @@
-import streamlit as st
-import requests
-import pandas as pd
-from gtts import gTTS
-import base64
-from io import BytesIO
-from PIL import Image
-import os
-
-st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")
-
-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
-
-# Sidebar
-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://127.0.0.1:8000")
-    api_token = st.text_input("🔐 API Token", type="password")
-
-# Tabs
-tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"])
-
-def speak(text, lang='en'):
-    tts = gTTS(text, lang=lang)
-    mp3 = BytesIO()
-    tts.write_to_fp(mp3)
-    b64 = base64.b64encode(mp3.getvalue()).decode()
-    st.markdown(f'<audio controls><source src="data:audio/mp3;base64,{b64}" type="audio/mp3"></audio>', unsafe_allow_html=True)
-    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:
-        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}")
-
-# Tab: Bulk CSV
-with tab2:
-    st.title("📚 Bulk CSV Upload")
-    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] = [""] * 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}")
+import streamlit as st
+import requests
+import pandas as pd
+from gtts import gTTS
+import base64
+from io import BytesIO
+from PIL import Image
+import os
+
+st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide")
+
+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
+
+# Sidebar
+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"])
+
+def speak(text, lang='en'):
+    tts = gTTS(text, lang=lang)
+    mp3 = BytesIO()
+    tts.write_to_fp(mp3)
+    b64 = base64.b64encode(mp3.getvalue()).decode()
+    st.markdown(f'<audio controls><source src="data:audio/mp3;base64,{b64}" type="audio/mp3"></audio>', unsafe_allow_html=True)
+    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:
+        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}")
+
+# Tab: Bulk CSV
+with tab2:
+    st.title("📚 Bulk CSV Upload")
+    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] = [""] * 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}")