Update app.py

app.py

@@ -1,8 +1,6 @@
 import gradio as gr
 from transformers import pipeline
-import datetime
 
-# Load your fine-tuned multi-label classification model
 classifier = pipeline(
     "text-classification",
     model="WarTitan2077/Number-Classifier",
@@ -10,48 +8,34 @@ classifier = pipeline(
     top_k=None
 )
 
-# Define labels in the correct order
 labels = ["Symbolic", "Numeric", "Natural", "Integer", "Rational", "Irrational", "Real", "Prime", "Composite"]
 label_map = {f"LABEL_{i}": label for i, label in enumerate(labels)}
 
-# Define the prediction function
-def classify_numbers(input1, input2, input3):
+# 🎯 Route exposed at /predict
+@gr.predict()
+def classify_numbers(input1: str, input2: str, input3: str) -> dict:
     inputs = {"Input1": input1, "Input2": input2, "Input3": input3}
-    print(f"[{datetime.datetime.now()}] Received inputs: {inputs}")
-    
     results = {}
-    try:
-        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}
-                results[name] = result
-            else:
-                results[name] = {}
-        print(f"[{datetime.datetime.now()}] Returning results: {results}")
-    except Exception as e:
-        print(f"[{datetime.datetime.now()}] Error during classification: {e}")
-        raise e
-    
+    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
+            }
+            results[name] = result
+        else:
+            results[name] = {}
     return results
 
-
-# Define Gradio interface
-inputs = [
-    gr.Textbox(label="Input 1"),
-    gr.Textbox(label="Input 2"),
-    gr.Textbox(label="Input 3")
-]
-
-output = gr.JSON(label="Predictions")
-
-demo = gr.Interface(
-    fn=classify_numbers,
-    inputs=inputs,
-    outputs=output,
-    title="Number Classifier",
-    description="Enter up to three inputs to classify them as Symbolic, Numeric, Prime, etc."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+# Optional web UI (so Space still looks nice)
+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)
+
+demo.launch()