created app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+
+# 1) load model once at startup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionPipeline.from_pretrained(
+    "Nihirc/Prompt2MedImage",
+    torch_dtype=torch.float16 if device=="cuda" else torch.float32
+).to(device)
+
+def generate(prompt: str):
+    img = pipe(prompt).images[0]
+    return img
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Textbox(lines=2, placeholder="Enter medical prompt…"),
+    outputs="image",
+    title="Prompt2MedImage",
+    description="Generate medical images from text prompts."
+)
+
+if __name__=="__main__":
+    demo.launch()