Update app.py

app.py

@@ -1,38 +1,46 @@
 import gradio as gr
-from src.inference import predict_ticket  # Now points to the corrected inference.py
+from src.inference import predict_ticket  # Uses the fixed inference.py with nltk fix
 
 def predict_interface(ticket_text):
-    result = predict_ticket(ticket_text)
-    issue = result.get("issue_type", "Unknown")
-    urgency = result.get("urgency_level", "Unknown")
-    entities = result.get("entities", {})
+    try:
+        result = predict_ticket(ticket_text)
+        issue = result.get("issue_type", "Unknown")
+        urgency = result.get("urgency_level", "Unknown")
+        entities = result.get("entities", {})
 
-    # Format entity output
-    lines = []
-    for key in ["products", "dates", "complaints"]:
-        vals = entities.get(key, [])
-        lines.append(f"{key.capitalize()}: {', '.join(vals) if vals else 'None'}")
-    entities_str = "\n".join(lines)
+        # Format entity output
+        lines = []
+        for key in ["products", "dates", "complaints"]:
+            vals = entities.get(key, [])
+            lines.append(f"{key.capitalize()}: {', '.join(vals) if vals else 'None'}")
+        entities_str = "\n".join(lines)
 
-    return issue, urgency, entities_str
+        return issue, urgency, entities_str
 
+    except Exception as e:
+        return f"Prediction error: {str(e)}", "Prediction error", "Prediction error"
+
+# Build the Gradio interface
 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.'"
+        placeholder=(
+            "Describe your issue clearly.\n"
+            "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),
+        gr.Textbox(label="📌 Predicted Issue Type"),
+        gr.Textbox(label="⏱️ Predicted Urgency Level"),
+        gr.Textbox(label="🧠 Extracted Entities"),
     ],
     title="📬 Customer Support Ticket Analyzer",
     description=(
-        "Paste a customer support ticket. This tool uses ML to predict:\n"
+        "Paste a customer support ticket. This tool uses machine learning to predict:\n\n"
         "- 📌 Issue Type (e.g., Late Delivery, Refund)\n"
-        "- ⏱️ Urgency Level (Low/Medium/High)\n"
+        "- ⏱️ Urgency Level (Low / Medium / High)\n"
         "- 🧠 Extracted Entities (Products, Dates, Complaints)"
     ),
     allow_flagging="never"