Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Load your model from Hugging Face Hub
+MODEL_NAME = "Pisethan/sangapac-math"
+reverse_label_mapping = {
+    0: "arithmetic",
+    1: "multiplication",
+    2: "division",
+    3: "algebra",
+    4: "geometry",
+}
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+def predict(input_text):
+    # Predict using the model
+    result = classifier(input_text)
+    label_id = int(result[0]["label"].split("_")[-1])  # Extract label ID
+    category = reverse_label_mapping[label_id]  # Map label to category
+
+    # Return prediction result
+    return {
+        "Category": category,
+        "Confidence": result[0]["score"],
+    }
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a math problem..."),
+    outputs="json",
+    title="Sangapac Math Model",
+    description="A model to classify math problems into categories like Arithmetic, Multiplication, Division, Algebra, and Geometry.",
+)
+
+# Launch the app
+interface.launch()