Update app.py

app.py

@@ -1,25 +1,29 @@
 import gradio as gr
 from PIL import Image
 from diffusers import DiffusionPipeline
+import time
 
 # Load model and scheduler
 ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
 
 def generate_image(prompt, negative_prompt="Low quality", width=512, height=512):
     # Run pipeline in inference (sample random noise and denoise)
+    start_time = time.time()
     images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6, negative_prompts=[negative_prompt]).images
     # Resize image to desired width and height
     resized_images = [image.resize((int(width), int(height))) for image in images]
     # Save images
     for idx, image in enumerate(resized_images):
         image.save(f"squirrel-{idx}.png")
-    return "Images generated successfully!"
+    end_time = time.time()
+    elapsed_time = round(end_time - start_time, 2)
+    return f"Images generated successfully! (Elapsed Time: {elapsed_time} seconds)"
 
 # Define the interface
 iface = gr.Interface(
     fn=generate_image,
     inputs=["text", "text", "number", "number"],
-    outputs="text",
+    outputs=gr.outputs.Image(label="Generated Image"),
     layout="vertical",
     title="Image Generation",
     description="Generate images based on prompts.",