Update app.py

app.py

@@ -4,13 +4,13 @@ import pandas as pd
 from datetime import datetime
 import os
 
-# -----------------------------
-# Load pretrained emotion model
-# -----------------------------
+# ----------------------------------------------------
+# Load pretrained Hugging Face emotion model
+# ----------------------------------------------------
 model_name = "superb/hubert-large-superb-er"
 emotion_classifier = pipeline("audio-classification", model=model_name)
 
-# Emotion mapping (short label → full name + emoji)
+# Emotion mapping
 EMOTION_MAP = {
     "ang": ("Angry", "😡"),
     "hap": ("Happy", "😄"),
@@ -23,13 +23,12 @@ EMOTION_MAP = {
     "dis": ("Disgusted", "🤢"),
 }
 
-# -----------------------------
+# ----------------------------------------------------
 # Setup data storage
-# -----------------------------
+# ----------------------------------------------------
 os.makedirs("data", exist_ok=True)
 CSV_PATH = "data/customer_complaints_emotion.csv"
 
-# Example categories — these can be linked to your delivery/order system
 ORDER_ISSUES = [
     "Late Delivery",
     "Damaged Package",
@@ -42,75 +41,115 @@ ORDER_ISSUES = [
     "Other Complaint"
 ]
 
-# -----------------------------
-# Emotion analysis function
-# -----------------------------
+# ----------------------------------------------------
+# Priority estimation
+# ----------------------------------------------------
+def get_priority(label, score):
+    if label in ["Angry", "Frustrated", "Fearful", "Disgusted"] and score >= 60:
+        return "🔴 High Priority"
+    elif label in ["Sad", "Surprised", "Excited"] and score >= 50:
+        return "🟠 Medium Priority"
+    else:
+        return "🟢 Low Priority"
+
+# ----------------------------------------------------
+# Main analysis function
+# ----------------------------------------------------
 def analyze_complaint(audio_file, complaint_type, order_id):
     if audio_file is None or not complaint_type or not order_id:
-        return "⚠️ Please upload an audio complaint, select issue type, and enter Order ID."
+        return (
+            "⚠️ Please upload an audio complaint, select issue type, and enter Order ID.",
+            "",
+            None
+        )
 
-    # Run prediction
+    # Run model
     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'], "🎭"))
     score = round(top['score'] * 100, 2)
+    priority = get_priority(label, score)
 
-    # -----------------------------
-    # Build dashboard text
-    # -----------------------------
-    dashboard = f"## 📞 Customer Complaint Emotion Dashboard\n"
-    dashboard += f"### 🎯 Detected Emotion: {emoji} **{label.upper()} ({score}%)**\n\n"
-    dashboard += f"### 📦 Complaint Type: **{complaint_type}**\n"
-    dashboard += f"### 🧾 Order ID: **{order_id}**\n\n"
-    dashboard += f"### 📊 Emotion Breakdown\n"
+    # Dashboard summary (markdown)
+    summary = f"""
+### 🎯 Detected Emotion: {emoji} **{label.upper()} ({score}%)**
+### ⚡ Priority Level: {priority}
+---
+**Complaint Type:** {complaint_type}  
+**Order ID:** {order_id}  
+**Timestamp:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}  
+"""
 
+    # Detailed emotion breakdown
+    breakdown = "### 📊 Emotion Breakdown\n"
     for r in results:
         lbl, emo = EMOTION_MAP.get(r['label'], (r['label'], "🎭"))
         scr = round(r['score'] * 100, 2)
         bar = "█" * int(scr / 5)
-        dashboard += f"{emo} **{lbl}**: {scr}%  \n{bar}\n\n"
+        breakdown += f"{emo} **{lbl}**: {scr}%  \n{bar}\n\n"
 
-    # -----------------------------
-    # Save results to CSV
-    # -----------------------------
+    # Save log to CSV
     df = pd.DataFrame({
         "datetime": [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
         "order_id": [order_id],
         "complaint_type": [complaint_type],
         "emotion": [label],
-        "score": [score]
+        "score": [score],
+        "priority": [priority]
     })
     if os.path.exists(CSV_PATH):
         df.to_csv(CSV_PATH, mode="a", header=False, index=False)
     else:
         df.to_csv(CSV_PATH, index=False)
 
-    return dashboard
-
-
-# -----------------------------
-# Gradio Interface
-# -----------------------------
-app = gr.Interface(
-    fn=analyze_complaint,
-    inputs=[
-        gr.Audio(type="filepath", label="🎙️ Upload Customer Complaint Voice"),
-        gr.Dropdown(ORDER_ISSUES, label="🚚 Select Complaint Type"),
-        gr.Textbox(label="📦 Enter Order ID"),
-    ],
-    outputs="markdown",
-    title="🎧 Customer Service Emotion Analyzer",
-    description=(
-        "Analyze voice-based customer complaints to detect emotions and "
-        "prioritize based on emotional intensity. Ideal for supply chain "
-        "and logistics feedback monitoring."
-    ),
-    allow_flagging="never",
-)
-
-# -----------------------------
-# Run the app
-# -----------------------------
+    # Display updated log
+    try:
+        log_df = pd.read_csv(CSV_PATH).tail(10)
+    except:
+        log_df = pd.DataFrame()
+
+    return summary, breakdown, log_df
+
+
+# ----------------------------------------------------
+# Gradio Interface with Blocks
+# ----------------------------------------------------
+with gr.Blocks(theme=gr.themes.Soft(), title="Customer Complaint Emotion Analyzer") as app:
+    gr.Markdown(
+        """
+    # 🎧 Customer Complaint Emotion Analyzer  
+    Analyze customer **voice complaints** to detect emotional tone and **auto-prioritize** based on urgency.  
+    Ideal for logistics, delivery, and supply chain customer service operations.  
+    ---
+    """
+    )
+
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="🎙️ Upload Complaint Audio")
+        complaint_type = gr.Dropdown(ORDER_ISSUES, label="🚚 Complaint Type", interactive=True)
+        order_id = gr.Textbox(label="📦 Order ID / Reference Number", placeholder="e.g., ORD123456")
+
+    analyze_btn = gr.Button("🔍 Analyze Complaint", variant="primary")
+
+    with gr.Row():
+        summary_output = gr.Markdown(label="Summary")
+        breakdown_output = gr.Markdown(label="Detailed Breakdown")
+
+    with gr.Accordion("📁 Recent Complaint Logs (Last 10 Entries)", open=False):
+        log_table = gr.Dataframe(headers=["datetime", "order_id", "complaint_type", "emotion", "score", "priority"], interactive=False)
+
+    analyze_btn.click(
+        fn=analyze_complaint,
+        inputs=[audio_input, complaint_type, order_id],
+        outputs=[summary_output, breakdown_output, log_table],
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("👨‍💼 **Developed for Supply Chain Customer Service Emotion Monitoring.**")
+
+# ----------------------------------------------------
+# Run app
+# ----------------------------------------------------
 if __name__ == "__main__":
     app.launch()