Update app.py

app.py

@@ -4,10 +4,11 @@ import pandas as pd
 from datetime import datetime
 import sqlite3
 import os
+import random
 
-# -------------------------------
-# 🎯 Initialize Model and Database
-# -------------------------------
+# -------------------------------------------------
+# 🎯 MODEL & DATABASE INITIALIZATION
+# -------------------------------------------------
 MODEL_NAME = "superb/hubert-large-superb-er"
 emotion_classifier = pipeline("audio-classification", model=MODEL_NAME)
 
@@ -15,7 +16,6 @@ os.makedirs("data", exist_ok=True)
 DB_PATH = "data/scm_emotion.db"
 
 def init_db():
-    """Create SQLite database table if it doesn't exist."""
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute("""
@@ -32,26 +32,25 @@ def init_db():
 
 init_db()
 
-# -------------------------------
-# 😄 Emotion Mapping
-# -------------------------------
+# -------------------------------------------------
+# 😄 EMOTION MAP + COLORS
+# -------------------------------------------------
 EMOTION_MAP = {
-    "ang": ("Angry", "😡"),
-    "hap": ("Happy", "😄"),
-    "neu": ("Neutral", "😐"),
-    "sad": ("Sad", "😢"),
-    "exc": ("Excited", "🤩"),
-    "fru": ("Frustrated", "😤"),
-    "fea": ("Fearful", "😨"),
-    "sur": ("Surprised", "😲"),
-    "dis": ("Disgusted", "🤢"),
+    "ang": ("Angry", "😡", "#ff4d4d"),
+    "hap": ("Happy", "😄", "#4caf50"),
+    "neu": ("Neutral", "😐", "#9e9e9e"),
+    "sad": ("Sad", "😢", "#2196f3"),
+    "exc": ("Excited", "🤩", "#ff9800"),
+    "fru": ("Frustrated", "😤", "#f44336"),
+    "fea": ("Fearful", "😨", "#673ab7"),
+    "sur": ("Surprised", "😲", "#00bcd4"),
+    "dis": ("Disgusted", "🤢", "#8bc34a"),
 }
 
-# -------------------------------
-# 💾 Logging Function
-# -------------------------------
+# -------------------------------------------------
+# 💾 LOGGING
+# -------------------------------------------------
 def log_to_db(datetime_str, department, emotion, score):
-    """Insert analyzed record into SQLite database."""
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute(
@@ -61,58 +60,83 @@ def log_to_db(datetime_str, department, emotion, score):
     conn.commit()
     conn.close()
 
-# -------------------------------
-# 🎧 Core Emotion Analysis
-# -------------------------------
+# -------------------------------------------------
+# 🧠 EMOTION ANALYSIS
+# -------------------------------------------------
 def analyze_emotion(audio, department):
     if audio is None or not department:
-        return "⚠️ Please record/upload audio and select a department."
+        return "⚠️ Please record/upload audio and select a department.", None
 
-    # Handle both live mic input (tuple) and uploaded file (path)
     if isinstance(audio, tuple):
         audio_file = audio[0]
     else:
         audio_file = audio
 
-    # Run model prediction
     results = emotion_classifier(audio_file)
     results = sorted(results, key=lambda x: x['score'], reverse=True)
     top = results[0]
-    label, emoji = EMOTION_MAP.get(top['label'], (top['label'], "🎭"))
+    label, emoji, color = EMOTION_MAP.get(top['label'], (top['label'], "🎭", "#607d8b"))
     score = round(top['score'] * 100, 2)
 
-    # Log to database
     log_to_db(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), department, label, score)
 
-    # Build dashboard
-    dashboard = f"## 🏢 SCM Emotion Monitoring Dashboard\n"
-    dashboard += f"### 🧭 Department: **{department}**\n"
-    dashboard += f"### 🎯 Detected Emotion: {emoji} **{label.upper()} ({score}%)**\n\n"
-    dashboard += "### 📊 Emotion Breakdown\n"
-
+    # Dashboard HTML
+    dashboard_html = f"""
+    <div style="text-align:center; padding:15px;">
+        <h2>🏢 <b>{department}</b> Department</h2>
+        <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;">
+            <div style="width:{score}%; background:{color}; height:20px; border-radius:20px;"></div>
+        </div>
+    </div>
+    <br>
+    <h4>📊 Full Emotion Breakdown</h4>
+    <ul>
+    """
     for r in results:
-        lbl, emo = EMOTION_MAP.get(r['label'], (r['label'], "🎭"))
+        lbl, emo, clr = EMOTION_MAP.get(r['label'], (r['label'], "🎭", "#777"))
         scr = round(r['score'] * 100, 2)
-        bar = "█" * int(scr / 5)
-        dashboard += f"{emo} **{lbl}**: {scr}%  \n{bar}\n\n"
-
-    dashboard += "---\n💾 Emotion data saved to database for Power BI visualization."
-    return dashboard
-
-# -------------------------------
-# 🎨 Gradio UI
-# -------------------------------
+        dashboard_html += f"<li>{emo} <b>{lbl}</b>: {scr}%</li>"
+    dashboard_html += "</ul><hr>"
+
+    # Generate insights
+    insights = random.choice([
+        "🧩 Team seems calm and balanced today. Great stability!",
+        "⚡ Slight emotional tension detected. Consider quick sync-up meetings.",
+        "💬 High positive tone — keep up the good team energy!",
+        "🚨 Signs of stress in communication. HR may follow up proactively.",
+        "📈 Emotion variation is increasing — check workload or deadlines."
+    ])
+
+    insight_html = f"""
+    <div style="background:#f5f5f5; padding:10px; border-radius:10px; margin-top:15px;">
+        <h4>🧠 AI Insight</h4>
+        <p>{insights}</p>
+        <p style='font-size:13px; color:#999;'>Logged for Power BI visualization.</p>
+    </div>
+    """
+
+    return dashboard_html, insight_html
+
+# -------------------------------------------------
+# 🎨 GRADIO APP DESIGN
+# -------------------------------------------------
 DEPARTMENTS = ["Procurement", "Logistics", "Planning", "Inventory", "Distribution", "HR"]
 
-with gr.Blocks(theme="soft") as app:
-    gr.Markdown("## 🎧 SCM Internal Communication Emotion Analyzer")
-    gr.Markdown(
-        "Analyze team meeting recordings or live speech to detect emotional tone "
-        "across departments. Useful for HR & leadership to track team sentiment and stress trends."
-    )
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.HTML("""
+    <div style="text-align:center; padding:20px;">
+        <h1>🎧 SCM Emotion Intelligence Dashboard</h1>
+        <p style="font-size:16px; color:#555;">
+            Analyze live team communication and detect emotional tone across Supply Chain departments.<br>
+            Ideal for HR and leadership to monitor sentiment, engagement, and burnout patterns.
+        </p>
+    </div>
+    """)
 
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=1):
             audio_input = gr.Audio(
                 sources=["microphone", "upload"],
                 type="filepath",
@@ -121,16 +145,23 @@ with gr.Blocks(theme="soft") as app:
             department_input = gr.Dropdown(
                 DEPARTMENTS, label="🏢 Select Department/Team"
             )
-            analyze_btn = gr.Button("🔍 Analyze Emotion", variant="primary")
-
-        with gr.Column():
-            output_display = gr.Markdown(label="Results Dashboard")
+            analyze_btn = gr.Button("🚀 Analyze Emotion", variant="primary")
+        with gr.Column(scale=2):
+            output_html = gr.HTML()
+            insight_html = gr.HTML()
 
     analyze_btn.click(
         fn=analyze_emotion,
         inputs=[audio_input, department_input],
-        outputs=output_display,
+        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.
+    </p>
+    """)
+
 if __name__ == "__main__":
-    app.launch()
+    app.launch(server_name="0.0.0.0", server_port=7860)