Update app.py

app.py

@@ -22,6 +22,8 @@ def init_db():
     CREATE TABLE IF NOT EXISTS emotion_logs (
         id INTEGER PRIMARY KEY AUTOINCREMENT,
         datetime TEXT,
+        team TEXT,
+        purpose TEXT,
         emotion TEXT,
         score REAL
     )
@@ -49,12 +51,12 @@ EMOTION_MAP = {
 # -------------------------------------------------
 # 💾 LOGGING
 # -------------------------------------------------
-def log_to_db(datetime_str, emotion, score):
+def log_to_db(datetime_str, team, purpose, emotion, score):
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute(
-        "INSERT INTO emotion_logs (datetime, emotion, score) VALUES (?, ?, ?)",
-        (datetime_str, emotion, score),
+        "INSERT INTO emotion_logs (datetime, team, purpose, emotion, score) VALUES (?, ?, ?, ?, ?)",
+        (datetime_str, team, purpose, emotion, score),
     )
     conn.commit()
     conn.close()
@@ -62,27 +64,40 @@ def log_to_db(datetime_str, emotion, score):
 # -------------------------------------------------
 # 🧠 EMOTION ANALYSIS
 # -------------------------------------------------
-def analyze_emotion(audio):
+def analyze_emotion(audio, team, custom_team, purpose, custom_purpose):
     if audio is None:
         return "⚠️ Please record or upload audio first.", None
 
+    # Determine final values
+    team_name = custom_team if team == "Other" else team
+    purpose_name = custom_purpose if purpose == "Other" else purpose
+
+    if not team_name:
+        team_name = "Unspecified Team"
+    if not purpose_name:
+        purpose_name = "Unspecified Purpose"
+
+    # Handle file input
     if isinstance(audio, tuple):
         audio_file = audio[0]
     else:
         audio_file = audio
 
+    # Predict emotion
     results = emotion_classifier(audio_file)
     results = sorted(results, key=lambda x: x['score'], reverse=True)
     top = results[0]
     label, emoji, color = EMOTION_MAP.get(top['label'], (top['label'], "🎭", "#607d8b"))
     score = round(top['score'] * 100, 2)
 
-    log_to_db(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), label, score)
+    # Log data
+    log_to_db(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), team_name, purpose_name, label, score)
 
-    # Dashboard HTML
+    # Dashboard UI
     dashboard_html = f"""
     <div style="text-align:center; padding:15px;">
-        <h2>🎤 Emotion Detected</h2>
+        <h2>🏢 <b>{team_name}</b></h2>
+        <h4>🎯 Purpose: <b>{purpose_name}</b></h4>
         <h3 style="color:{color}; font-size:26px;">{emoji} {label.upper()} — {score}%</h3>
         <p style="color:#666;">Detected Emotion Intensity (Confidence)</p>
         <div style="width:80%; margin:auto; background:#eee; border-radius:20px;">
@@ -101,11 +116,11 @@ def analyze_emotion(audio):
 
     # AI Insight
     insights = random.choice([
-        "🧩 Calm and balanced tone detected — great composure!",
-        "⚡ Slight emotional tension observed — consider a short break.",
-        "💬 Positive tone! Keep the good vibes going.",
-        "🚨 Signs of stress — communication may need attention.",
-        "📈 Emotional variation increasing — track for next interactions."
+        "🧩 The conversation tone was balanced — good team stability.",
+        "⚡ Slight emotional tension detected — consider a quick check-in.",
+        "💬 Positive tone overall — keep up the good engagement.",
+        "🚨 Possible stress indicators — leadership follow-up advised.",
+        "📈 Emotional fluctuation noted — monitor workload or dynamics."
     ])
 
     insight_html = f"""
@@ -118,42 +133,67 @@ def analyze_emotion(audio):
 
     return dashboard_html, insight_html
 
+
 # -------------------------------------------------
 # 🎨 GRADIO APP DESIGN
 # -------------------------------------------------
+TEAM_OPTIONS = ["Procurement", "Logistics", "Planning", "HR", "Operations", "IT", "Other"]
+PURPOSE_OPTIONS = ["HR Meeting", "Team Stand-up", "One-on-One", "Customer Call", "Interview", "Project Review", "Other"]
+
 with gr.Blocks(theme=gr.themes.Soft()) as app:
+    # Header
     gr.HTML("""
     <div style="text-align:center; padding:20px;">
-        <h1>🎧 Emotion Recognition Dashboard</h1>
+        <h1>🎧 Emotion Intelligence for Teams</h1>
         <p style="font-size:16px; color:#555;">
-            Detect emotional tone directly from voice recordings.<br>
-            Ideal for HR, leadership, or mental wellness tracking.
+            Detect and visualize emotional tone from team meetings or recorded conversations.<br>
+            Designed for managers and HR to track emotional health and engagement trends.
         </p>
     </div>
     """)
 
+    # Inputs
     with gr.Row():
         with gr.Column(scale=1):
+            team_dropdown = gr.Dropdown(TEAM_OPTIONS, label="🏢 Select Team / Department")
+            custom_team = gr.Textbox(label="✏️ Enter Team (if 'Other')", placeholder="Enter custom team name here")
+
+            purpose_dropdown = gr.Dropdown(PURPOSE_OPTIONS, label="🎯 Purpose of the Recording")
+            custom_purpose = gr.Textbox(label="✏️ Enter Purpose (if 'Other')", placeholder="Enter custom meeting purpose here")
+
             audio_input = gr.Audio(
                 sources=["microphone", "upload"],
                 type="filepath",
-                label="🎙️ Record or Upload Meeting's Audio",
+                label="🎙️ Record or Upload Team Meeting Audio",
             )
             analyze_btn = gr.Button("🚀 Analyze Emotion", variant="primary")
+
         with gr.Column(scale=2):
             output_html = gr.HTML()
             insight_html = gr.HTML()
 
+    # Disclaimer
+    gr.HTML("""
+    <div style="background:#fff3cd; padding:10px; border-radius:10px; margin-top:15px; border-left:6px solid #ff9800;">
+        <h4 style="color:#444;">⚠️ Disclaimer</h4>
+        <p style="color:#333; font-size:14px;">
+            This is a demo version. Your voice data is <b>not being saved or shared</b> anywhere.<br>
+            The database logging is simulated for demonstration — feel free to explore the system freely.
+        </p>
+    </div>
+    """)
+
+    # Button Action
     analyze_btn.click(
         fn=analyze_emotion,
-        inputs=[audio_input],
+        inputs=[audio_input, team_dropdown, custom_team, purpose_dropdown, custom_purpose],
         outputs=[output_html, insight_html],
     )
 
     gr.HTML("""
     <hr>
     <p style="text-align:center; color:#999;">
-        💾 All results stored in <code>scm_emotion.db</code> for Power BI visualization.
+        💾 Results are locally logged in <code>scm_emotion.db</code> (for Power BI integration demo).
     </p>
     """)