Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from transformers import pipeline
 
+# Load model
 classifier = pipeline(
     "text-classification",
     model="WarTitan2077/Number-Classifier",
@@ -11,7 +12,7 @@ classifier = pipeline(
 labels = ["Symbolic", "Numeric", "Natural", "Integer", "Rational", "Irrational", "Real", "Prime", "Composite"]
 label_map = {f"LABEL_{i}": label for i, label in enumerate(labels)}
 
-# 🎯 Route exposed at /predict
+# ✅ This function gets exposed at /run/predict
 @gr.predict()
 def classify_numbers(input1: str, input2: str, input3: str) -> dict:
     inputs = {"Input1": input1, "Input2": input2, "Input3": input3}
@@ -19,23 +20,20 @@ def classify_numbers(input1: str, input2: str, input3: str) -> dict:
     for name, value in inputs.items():
         if value.strip():
             output = classifier(value)[0]
-            result = {
-                label_map[item["label"]]: round(item["score"], 3)
-                for item in output
-            }
+            result = {label_map[item["label"]]: round(item["score"], 3) for item in output}
             results[name] = result
         else:
             results[name] = {}
     return results
 
-# Optional web UI (so Space still looks nice)
+# Optional web UI for users
 with gr.Blocks() as demo:
     with gr.Row():
         input1 = gr.Textbox(label="Input 1")
         input2 = gr.Textbox(label="Input 2")
         input3 = gr.Textbox(label="Input 3")
     output = gr.JSON()
-    button = gr.Button("Classify")
-    button.click(fn=classify_numbers, inputs=[input1, input2, input3], outputs=output)
+    btn = gr.Button("Classify")
+    btn.click(fn=classify_numbers, inputs=[input1, input2, input3], outputs=output)
 
 demo.launch()