Create app.py

app.py

@@ -0,0 +1,116 @@
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+from datetime import datetime
+import os
+
+# -----------------------------
+# Load pretrained 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_MAP = {
+    "ang": ("Angry", "😡"),
+    "hap": ("Happy", "😄"),
+    "neu": ("Neutral", "😐"),
+    "sad": ("Sad", "😢"),
+    "exc": ("Excited", "🤩"),
+    "fru": ("Frustrated", "😤"),
+    "fea": ("Fearful", "😨"),
+    "sur": ("Surprised", "😲"),
+    "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",
+    "Wrong Product Delivered",
+    "Missing Items",
+    "Rude Delivery Staff",
+    "Payment Issue",
+    "Return/Refund Request",
+    "Order Cancelled Automatically",
+    "Other Complaint"
+]
+
+# -----------------------------
+# Emotion 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."
+
+    # Run 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'], "🎭"))
+    score = round(top['score'] * 100, 2)
+
+    # -----------------------------
+    # 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"
+
+    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"
+
+    # -----------------------------
+    # Save results 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]
+    })
+    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
+# -----------------------------
+if __name__ == "__main__":
+    app.launch()