first start for osiris

app.py

@@ -0,0 +1,44 @@
+import torch
+import gradio as gr
+from diffusers import DiffusionPipeline
+import os
+
+# UI
+DESCRIPTION = '''
+<div>
+<h1 style="text-align: center;">Osiris 🦥</h1>
+<p>This has an open source stable diffuser from <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0"><b>stable-diffusion-xl-base-1.0</b></a></p>
+</div>
+'''
+
+# pipeline
+pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to('cuda')
+
+# function to take input and generate text tokena
+def osiris(prompt: str,
+           history: list,
+           temperature: float,
+           max_new_tokens: int):
+    """
+    Takes input, passes it into the pipeline, 
+    get the top 5 scores, and ouput those scores into images
+    """
+
+    # Generate image based on text
+    image = pipeline(prompt=prompt).images[0]
+
+    return image
+
+with gr.Blocks(fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.Interface(
+        fn=osiris,
+        inputs="text",
+        outputs="image",
+        fill_height=True,
+        # additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        # additional_inputs=[]
+    )
+
+if __name__ == "__main__":
+    demo.launch()