Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+
+# Load the Shap-E pipeline (adjust the model path if needed)
+pipeline = DiffusionPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16)
+pipeline = pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def generate_image(prompt):
+    # Generate an image based on the prompt using the pipeline
+    output = pipeline(prompt)
+    # Assuming that the output has a `images` field with a list of images
+    return output.images[0]
+
+# Create Gradio interface
+title = "Shap-E Model Demo"
+description = "Generate images from text prompts using the Shap-E diffusion model."
+
+# Set up the interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=gr.Textbox(label="Prompt"),
+    outputs=gr.Image(label="Generated Image"),
+    title=title,
+    description=description,
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()