initial app

app.py

@@ -1,28 +1,39 @@
 import gradio as gr
-import random
+import random, os
+from PIL import Image
 
-def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
-  if seed == None :
-    seed = random.randint(0, 10000)
-  print( prompt, im_prompt, seed, n_steps)
-  prompts = [prompt]
-  im_prompts = []
-  if im_prompt != None:
-    im_prompts = [im_prompt]
-  pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
-  return pil_ims[0]
+def open_ims(choice):
+    print(choice)
+    imnames = os.listdir('images/'+'Photo_portrait_of_a_'+choice.replace(' ',' ')+'/Seed_46267/')
+    images = [(Image.open(name)) for name in imnames]
+    return images
 
+def random_image():
+    cat = random.choice(categories)
+    print(cat)
+    imnames = os.listdir('images/'+'Photo_portrait_of_a_'+choice.replace(' ',' ')+'/Seed_46267/')
+    ims = [(Image.open(name)) for name in imnames]
+    print(ims)
+    return ims
+
+categories=os.listdir('images/')
+categories = [c.replace('Photo_portrait_of_a_','').replace('_',' ') for c in categories]
+print(categories)
 
 with gr.Blocks() as demo:
     gr.Markdown("# Stable Diffusion Explorer")
-    gr.Markdown("#### Try your hand at guessing the category of each image displayed, from the options provided. Compare your answers to that of a neural network trained on the dataset, and see if you can do better!")
+    gr.Markdown("## TODO")
+    demo.load(random_image, None, images)
     with gr.Row():
         with gr.Column():
-              gr.inputs.Textbox(label="Text prompt"),
-              gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
+              choice = gr.Dropdown(categories, label = "Choose a first category", interactive=True)
+              gr.Gallery(images, label="Images"),
         with gr.Column():
-              gr.inputs.Textbox(label="Text prompt"),
-              gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
+              gr.Textbox(label="Text prompt"),
+              gr.Image(optional=True, label="Image prompt", type='filepath'),
+
+
 
+    choice.change(open_ims, [prompt], [images])
 
 demo.launch()