updated app.py

app.py

@@ -1,20 +1,27 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
 import torch
+import base64
+import io
 
-# Load model once at startup
 pipe = DiffusionPipeline.from_pretrained("Nihirc/Prompt2MedImage")
 if torch.cuda.is_available():
     pipe = pipe.to("cuda")
 
 def generate_image(prompt):
     image = pipe(prompt).images[0]
-    return image
+    
+    # Convert to base64 for API compatibility
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    
+    return img_str  # Return base64 string directly
 
 iface = gr.Interface(
     fn=generate_image,
     inputs=gr.Textbox(label="Prompt"),
-    outputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Base64 Image"),  # Changed from Image to Textbox
     title="Medical Image Generator"
 )