Update app.py

app.py

@@ -15,7 +15,7 @@ model_path = hf_hub_download(repo_id="canadianjosieharrison/texture-logistic-mod
 classifier = joblib.load(model_path)  # Make sure this exists
 
 # Create label map
-id2label = {"0":"metal", "1":"stone", "2":"wood"}
+id2label = {0:"metal", 1:"stone", 2:"wood"}
 
 # Prediction function
 def classify_image(img: Image.Image):
@@ -27,15 +27,16 @@ def classify_image(img: Image.Image):
 
     # Step 2: Predict using classifier
     prediction = classifier.predict(embedding)
-    return f"Predicted Material: {prediction[0]}"
+    return f"Predicted Material: {id2label[prediction[0]]}"
 
 # Gradio interface
 interface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Image Classifier with Embedding Pipeline",
-    description="Uses a ViT transformer to generate image embeddings, then classifies them using a trained model."
+    title="Material texture image classifier",
+    description="Upload a detail texture image of a material and the model will predict whether it's metal, stone, or wood.",
+    examples = ["metal.PNG", "stone.PNG", "wood.PNG"],
 )
 
 if __name__ == "__main__":