Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+
+def load_amused_model():
+    return DiffusionPipeline.from_pretrained("amused/amused-256")
+
+# Generate image from prompt using AmusedPipeline
+def generate_image(prompt):
+    try:
+        pipe = load_amused_model()
+        generator = torch.Generator().manual_seed(8)  # Create a generator for reproducibility
+        image = pipe(prompt, generator=generator).images[0]  # Generate image from prompt
+        return image, None
+    except Exception as e:
+        return None, str(e)
+
+def inference(prompt):
+    image, error = generate_image(prompt)
+    if error:
+        return "Error: " + error
+    return image
+
+gradio_interface = gr.Interface(
+    fn=inference,
+    inputs="text",
+    outputs="image"
+)
+
+if __name__ == "__main__":
+    gradio_interface.launch()