UI Update: Glassmorphic design and Cloud logging

src/streamlit_app.py

@@ -2,113 +2,192 @@ import streamlit as st
 from transformers import pipeline
 import pandas as pd
 from datetime import datetime
+import time
 import plotly.graph_objects as go
+import json
 from streamlit_gsheets import GSheetsConnection
 
 # --- PAGE CONFIG ---
 st.set_page_config(page_title="Sentiment Analyzer AI | Bilingual Engine", page_icon="🌐", layout="wide")
 
-# --- CUSTOM CSS FOR GLASS-CARD LOOK ---
+# --- PROFESSIONAL NEUMORPHIC / GLASS CSS ---
 st.markdown("""
     <style>
-    .stApp { background: linear-gradient(135deg, #12141d 0%, #1a1c2c 100%); color: #ffffff; }
+    .stApp {
+        background: linear-gradient(135deg, #12141d 0%, #1a1c2c 100%);
+        color: #ffffff;
+    }
+    
+    div[data-baseweb="input"] {
+        background: rgba(255, 255, 255, 0.05) !important;
+        backdrop-filter: blur(10px) !important;
+        border-radius: 15px !important;
+        border: 1px solid rgba(255, 255, 255, 0.1) !important;
+        padding: 5px !important;
+    }
+
     .glass-card {
         background: rgba(255, 255, 255, 0.05);
         backdrop-filter: blur(10px);
         border-radius: 20px;
         border: 1px solid rgba(255, 255, 255, 0.1);
-        padding: 25px;
-        margin-bottom: 20px;
+        padding: 30px;
+        margin-top: 20px;
+        margin-bottom: 25px;
+        transition: 0.4s ease;
     }
+
     .stButton>button {
         background: linear-gradient(90deg, #4facfe 0%, #00f2fe 100%);
-        color: white; border-radius: 12px; font-weight: 600; width: 100%;
+        color: white;
+        border: none;
+        border-radius: 12px;
+        font-weight: 600;
+        letter-spacing: 0.5px;
+        height: 3rem;
+        transition: all 0.3s ease;
+    }
+    .stButton>button:hover {
+        box-shadow: 0 0 20px rgba(79, 172, 254, 0.4);
+        transform: scale(1.02);
     }
+
+    [data-testid="stMetricValue"] { color: #00f2fe; font-weight: 800; }
     </style>
     """, unsafe_allow_html=True)
 
 # --- CLOUD DATA LOGGING (GOOGLE SHEETS) ---
-# This connects to the sheet you shared with the service account
-conn = st.connection("gsheets", type=GSheetsConnection)
+if "GSHEETS_JSON" in st.secrets:
+    creds = json.loads(st.secrets["GSHEETS_JSON"])
+    conn = st.connection("gsheets", type=GSheetsConnection, credentials=creds)
+else:
+    conn = st.connection("gsheets", type=GSheetsConnection)
 
 def save_to_cloud(text, ai_label, ai_score, corrected_label=None):
     try:
-        # Read existing data to append
-        existing_data = conn.read(worksheet="Sheet1", ttl=0)
+        sheet_url = st.secrets.get("GSHEETS_URL", "")
+        # Using "Sheet1" as per your previous spreadsheet setup
+        existing_data = conn.read(spreadsheet=sheet_url, worksheet="Sheet1", ttl=0)
     except:
         existing_data = pd.DataFrame()
 
+    needs_review = "YES" if 0.33 <= ai_score <= 0.65 else "NO"
     new_entry = pd.DataFrame([{
         "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         "Text": text,
         "AI_Label": ai_label,
         "Confidence": f"{ai_score:.2%}",
+        "Needs_Review": needs_review,
         "Correction": corrected_label if corrected_label else "N/A"
     }])
 
     updated_df = pd.concat([existing_data, new_entry], ignore_index=True)
-    conn.update(worksheet="Sheet1", data=updated_df)
+    conn.update(spreadsheet=sheet_url, worksheet="Sheet1", data=updated_df)
 
 # --- MODEL ENGINE ---
-MODEL_REPO = "SumedhGajbhiye/Sentiment-Analyzer"
+MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer"
 
 @st.cache_resource
-def load_analysis_engine():
+def load_engine(path):
+    # Pulls directly from your HF Model Repo
+    return pipeline("sentiment-analysis", model=path, tokenizer=path)
+
+# --- HEADER SECTION ---
+col_h1, col_h2 = st.columns([3, 1])
+with col_h1:
+    st.title("Sentiment Analyzer")
+    st.caption("Advanced Bilingual Sentiment Analysis for English, Hindi & Hinglish")
+
+# --- SIDEBAR HUD ---
+with st.sidebar:
+    st.markdown("### 🛠️ ENGINE STATUS")
     try:
-        # Pulls directly from your HF Model Repo
-        return pipeline("sentiment-analysis", model=MODEL_REPO, tokenizer=MODEL_REPO)
-    except Exception as e:
-        st.error(f"System Core Connection Failed: {e}")
-        return None
-
-# --- UI HEADER ---
-st.title("🌐 Sentiment Analyzer")
-st.caption("Bilingual Engine: English | Hindi | Hinglish")
+        sheet_url = st.secrets.get("GSHEETS_URL", "")
+        df_log = conn.read(spreadsheet=sheet_url, worksheet="Sheet1", ttl=0)
+        st.metric("Total Ingested", len(df_log))
+        st.divider()
+        st.download_button("📤 Export Dataset", df_log.to_csv(index=False), "engine_feedback.csv", "text/csv")
+    except:
+        st.info("Engine is connecting to cloud...")
 
-classifier = load_analysis_engine()
+# --- ANALYSIS INTERFACE ---
+classifier = load_engine(MODEL_PATH)
 
 if classifier:
-    user_input = st.text_input("QUERY INPUT:", placeholder="Type a sentence...", key="main_input")
+    user_input = st.text_input(
+        "QUERY INPUT:", 
+        placeholder="Enter sentence for deep sentiment analysis...", 
+        key="main_input",
+        label_visibility="collapsed"
+    )
 
     if user_input:
-        with st.spinner("Neural Scan..."):
+        with st.status("Initializing Neural Weights...", expanded=False) as status:
+            time.sleep(0.4)
             result = classifier(user_input)[0]
-            label, score = result['label'], result['score']
-            color = "#00ff88" if "POS" in label.upper() else "#ff4b4b" if "NEG" in label.upper() else "#ffaa00"
-
-        # --- SIDE-BY-SIDE LAYOUT ---
-        # 1.5 ratio for left makes the gauge much bigger
-        col_left, col_right = st.columns([1.5, 1], gap="large")
-
-        with col_left:
-            st.markdown(f'<div class="glass-card"><h4>RESULT: <span style="color:{color}">{label}</span></h4></div>', unsafe_allow_html=True)
-            
-            # Big Professional Gauge
+            status.update(label="Analysis Complete", state="complete", expanded=False)
+
+        label = result['label']
+        score = result['score']
+        
+        emoji_map = {"Positive": "🟢", "Neutral": "🟡", "Negative": "🔴"}
+        color = "#00ff88" if "POS" in label.upper() else "#ff4b4b" if "NEG" in label.upper() else "#ffaa00"
+
+        # --- RESULTS GLASS CARD ---
+        st.markdown(f'''
+            <div class="glass-card">
+                <h4 style="color: #888; margin:0;">CLASSIFICATION RESULT</h4>
+                <h1 style="color: {color}; margin:0; font-size: 3.5rem;">{label} {emoji_map.get(label, "")}</h1>
+                <p style="color: #aaa; margin-top: 10px;">Deep linguistic scan detected {label.lower()} intent with {score:.1%} confidence.</p>
+            </div>
+            ''', unsafe_allow_html=True)
+
+        # --- CHART & CORRECTION ROW ---
+        col_chart, col_feed = st.columns([1, 2])
+
+        with col_chart:
             fig = go.Figure(go.Indicator(
                 mode = "gauge+number",
                 value = score * 100,
-                number = {'suffix': "%", 'font': {'color': '#fff'}},
+                domain = {'x': [0, 1], 'y': [0, 1]},
                 gauge = {
-                    'axis': {'range': [0, 100], 'tickcolor': "#888"},
+                    'axis': {'range': [None, 100], 'tickcolor': "#888", 'tickwidth': 2},
                     'bar': {'color': color},
                     'bgcolor': "rgba(0,0,0,0)",
                     'borderwidth': 2,
-                    'bordercolor': "rgba(255,255,255,0.1)"
+                    'bordercolor': "rgba(255,255,255,0.1)",
                 }
             ))
-            fig.update_layout(height=450, margin=dict(t=50, b=0), paper_bgcolor='rgba(0,0,0,0)', font={'color': "#fff"})
+            
+            fig.update_layout(
+                height=280, 
+                margin=dict(t=50, b=50, l=40, r=40), 
+                paper_bgcolor='rgba(0,0,0,0)', 
+                font={'color': "#fff", 'family': "Inter"}
+            )
             st.plotly_chart(fig, use_container_width=True)
 
-        with col_right:
-            st.markdown("### 🛠️ Neural Feedback")
-            st.info("Human verification helps the engine learn mixed-language nuances.")
+        with col_feed:
+            st.markdown("### ⚖️ HUMAN VERIFICATION")
+            c1, c2 = st.columns(2)
+            with c1:
+                if st.button("CONFIRM ACCURACY"):
+                    save_to_cloud(user_input, label, score)
+                    st.toast("Logic logged to cloud database.")
+                    time.sleep(1.0)
+                    st.rerun()
             
-            if st.button("✅ CONFIRM ACCURACY"):
-                save_to_cloud(user_input, label, score)
-                st.toast("Success! Verified data sent to Google Sheets.")
-
-            st.markdown("---")
-            correction = st.selectbox("UNCERTAIN? CORRECT LABEL:", ["Positive", "Neutral", "Negative"])
-            if st.button("💾 SAVE CORRECTION"):
-                save_to_cloud(user_input, label, score, corrected_label=correction)
-                st.warning("Correction logged for retraining.")
+            with c2:
+                correction = st.selectbox("OVERRIDE LABEL:", ["Positive", "Neutral", "Negative"])
+                if st.button("FORCE UPDATE ENGINE"):
+                    save_to_cloud(user_input, label, score, corrected_label=correction)
+                    st.toast(f"Engine forced to {correction}")
+                    time.sleep(1.0)
+                    st.rerun()
+
+# --- RECENT LOGS ---
+try:
+    with st.expander("📂 VIEW SYSTEM LOGS"):
+        st.dataframe(df_log.tail(10), use_container_width=True)
+except:
+    pass