Create app.py

app.py

@@ -0,0 +1,31 @@
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForImageClassification
+import gradio as gr
+
+# Load model and processor
+model_name = "your-username/your-siglip2-meme-classifier"  # Or local path
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoProcessor.from_pretrained(model_name)
+
+labels = model.config.id2label  # e.g., {0: "non-hateful", 1: "hateful"}
+
+def classify_meme(image: Image.Image):
+    inputs = processor(images=image, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predictions = {labels[i]: float(probs[0][i]) for i in range(len(labels))}
+    return predictions
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_meme,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Meme Sentiment Classifier (SigLIP2)",
+    description="Upload a meme to classify its sentiment using a SigLIP2-based model."
+)
+
+if __name__ == "__main__":
+    demo.launch()