Update app.py

app.py

@@ -1,44 +1,18 @@
-import gradio as gr
-from src.inference import predict_ticket  # Make sure this function is implemented and correct
-
-def predict_interface(ticket_text):
-    # Run inference
-    result = predict_ticket(ticket_text)
-    issue = result.get('issue_type', 'Unknown')
-    urgency = result.get('urgency_level', 'Unknown')
-    entities = result.get('entities', {})
-
-    # Format entities for display
-    entities_display = []
-    for key in ['products', 'dates', 'complaints']:
-        values = entities.get(key, [])
-        formatted_values = ', '.join(values) if values else 'None'
-        entities_display.append(f"{key.capitalize()}: {formatted_values}")
-    entities_str = "\n".join(entities_display)
-
-    return issue, urgency, entities_str
-
-iface = gr.Interface(
-    fn=predict_interface,
-    inputs=gr.Textbox(
-        label="📝 Customer Support Ticket",
-        lines=6,
-        placeholder="Describe your issue clearly. Example: 'I returned the washing machine on 10th May but no refund received.'"
-    ),
-    outputs=[
-        gr.Textbox(label="📌 Predicted Issue Type", lines=1),
-        gr.Textbox(label="⏱️ Predicted Urgency Level", lines=1),
-        gr.Textbox(label="🧠 Extracted Entities", lines=6)
-    ],
-    title="📬 Customer Support Ticket Analyzer",
-    description=(
-        "Paste a customer support ticket. This tool uses ML to predict:\n"
-        "- 📌 Issue Type (e.g., Late Delivery, Refund)\n"
-        "- ⏱️ Urgency Level (Low/Medium/High)\n"
-        "- 🧠 Extracted Entities (Products, Dates, Complaints)"
-    ),
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+from inference import predict_ticket_issue_type, predict_urgency_level
+
+def analyze_ticket(text):
+    issue_type = predict_ticket_issue_type(text)
+    urgency = predict_urgency_level(text)
+    return f"Issue Type: {issue_type}\nUrgency: {urgency}"
+
+demo = gr.Interface(
+    fn=analyze_ticket,
+    inputs=gr.Textbox(lines=4, placeholder="Paste customer ticket here..."),
+    outputs="text",
+    title="🎫 Customer Support Ticket Classifier",
+    description="Predicts the Issue Type and Urgency Level from a support ticket."
+)
+
+if __name__ == "__main__":
+    demo.launch()