Update app.py

app.py

@@ -1,14 +1,15 @@
-import gradio as gr
 from transformers import pipeline
+import gradio as gr
 
-# Load your model from Hugging Face
+# Load model and tokenizer
 classifier = pipeline(
     "text-classification",
-    model="WarTitan2077/Number-Classifier",       # Replace with your Hugging Face username/model
-    tokenizer="WarTitan2077/Number-Classifier",   # Same here
-    top_k=None                         # Return all labels with scores
+    model="WarTitan2077/Number-Classifier",
+    tokenizer="WarTitan2077/Number-Classifier",
+    top_k=None
 )
 
+# Map Hugging Face's LABEL_X to human-readable labels
 label_map = {
     "LABEL_0": "Symbolic1",
     "LABEL_1": "Numeric1",
@@ -19,7 +20,6 @@ label_map = {
     "LABEL_6": "Real1",
     "LABEL_7": "Prime1",
     "LABEL_8": "Composite1",
-    
     "LABEL_9": "Symbolic2",
     "LABEL_10": "Numeric2",
     "LABEL_11": "Natural2",
@@ -29,7 +29,6 @@ label_map = {
     "LABEL_15": "Real2",
     "LABEL_16": "Prime2",
     "LABEL_17": "Composite2",
-    
     "LABEL_18": "Symbolic3",
     "LABEL_19": "Numeric3",
     "LABEL_20": "Natural3",
@@ -41,26 +40,23 @@ label_map = {
     "LABEL_26": "Composite3"
 }
 
+# Prediction function
+def classify_numbers(input1, input2, input3):
+    # Combine inputs into a single string (or modify to match your training format)
+    text = f"{input1}, {input2}, {input3}"
+    result = classifier(text)
 
-# Define the prediction function
-def classify_numbers(n1, n2, n3):
-    input_str = f"{n1}, {n2}, {n3}"
-    result = classifier(input_str)
-    # Format the results nicely
-    output = {item["label"]: round(item["score"], 3) for item in result[0]}
+    # Convert raw LABEL_X output into readable format
+    output = {label_map[item["label"]]: round(item["score"], 3) for item in result[0]}
     return output
 
-# Create Gradio interface
+# Gradio UI
 iface = gr.Interface(
     fn=classify_numbers,
-    inputs=[
-        gr.Textbox(label="Input 1"),
-        gr.Textbox(label="Input 2"),
-        gr.Textbox(label="Input 3")
-    ],
-    output = {label_map[item["label"]]: round(item["score"], 3) for item in result[0]}
+    inputs=["text", "text", "text"],
+    outputs="json",
     title="Number Classifier",
-    description="Enter 3 numbers or symbols (e.g., 3.14, pi, 2) to classify them."
+    description="Enter three numbers or expressions to classify them (e.g., 2, pi, 3.5)"
 )
 
 iface.launch()