Update app.py

app.py

@@ -3,6 +3,9 @@ import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
+import base64
+from io import BytesIO
+from PIL import Image
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -19,7 +22,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    prompt = f"{prompt_part1} {color} colored {dress_type} with {design} design, {prompt_part5} and also show the back, left and right side of the cloth in the form of collage with the front view"
+    prompt = f"{prompt_part1} {color} colored {dress_type} with {design} design, {prompt_part5}"
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -35,13 +38,18 @@ def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt
         height=height,
         generator=generator
     ).images[0] 
+
+    # Convert the PIL image to base64
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
     
-    return image
+    return img_str
 
 examples = [
-    "red, t-shirt, yellow stripes",
-    "blue, hoodie, minimalist",
-    "red, sweat shirt, geometric design",
+    ["red", "t-shirt", "yellow stripes"],
+    ["blue", "hoodie", "minimalist"],
+    ["red", "sweatshirt", "geometric design"],
 ]
 
 css = """
@@ -172,7 +180,7 @@ with gr.Blocks(css=css) as demo:
         
         gr.Examples(
             examples=examples,
-            inputs=[prompt_part2]
+            inputs=[prompt_part2, prompt_part3, prompt_part4]
         )
 
     run_button.click(