Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoProcessor
+import torch
+
+# Load the model and processor
+model_name = "DeathDaDev/Materializer"
+processor = AutoProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Define the prediction function
+def classify_image(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Perform inference
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    # Get the predicted class
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="Image Classification with Materializer",
+    description="Upload an image to classify it using the Materializer model."
+)
+
+# Launch the interface
+iface.launch()