Update app.py

app.py

@@ -2,17 +2,35 @@ import gradio as gr
 from transformers import pipeline
 
 # Load the trained intent classification model
-model_name = "sonisatish119/PhysioMindAI-intent-classification-bert"  # Replace with your actual model name
+model_name = "sonisatish119/PhysioMindAI-intent-classification-bert" 
 classifier = pipeline("text-classification", model=model_name)
 
-# Define a function to predict intent
+# Define intent label mapping
+intent_labels = {
+    "LABEL_0": "book_appointment",
+    "LABEL_1": "reschedule_appointment",
+    "LABEL_2": "cancel_appointment",
+    "LABEL_3": "check_appointment_status",
+    "LABEL_4": "available_slots_inquiry",
+    "LABEL_5": "appointment_reminder",
+    "LABEL_6": "appointment_requirements",
+    "LABEL_7": "emergency_booking",
+    "LABEL_8": "appointment_location_details",
+    "LABEL_9": "modify_appointment_details",
+}
+
+# Function to predict intent
 def predict_intent(query):
     result = classifier(query)
-    intent = result[0]["label"]
+    label = result[0]["label"]  # This will be "LABEL_0", "LABEL_1", etc.
     confidence = result[0]["score"]
+    
+    # Map label to actual intent name
+    intent = intent_labels.get(label, "Unknown Intent")
+    
     return f"Intent: {intent} (Confidence: {confidence:.2f})"
 
-# Create Gradio Interface
+# Gradio Interface
 demo = gr.Interface(
     fn=predict_intent,
     inputs=gr.Textbox(placeholder="Type your query here...", lines=2),