Duplicate from pikto/prodia

Co-authored-by: pikto kenn <pikto@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Prodia
+emoji: 🔥
+colorFrom: pink
+colorTo: blue
+sdk: gradio
+sdk_version: 3.39.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: pikto/prodia
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import numpy as np
+import gradio as gr
+import ast
+import requests
+
+import logging
+from rembg import new_session
+from cutter import remove, make_label
+from utils import *
+
+API_URL_INITIAL = "https://ysharma-playground-ai-exploration.hf.space/run/initial_dataframe"
+API_URL_NEXT10 = "https://ysharma-playground-ai-exploration.hf.space/run/next_10_rows"
+
+from theme_dropdown import create_theme_dropdown  # noqa: F401
+
+dropdown, js = create_theme_dropdown()
+
+models = [
+    {"name": "Stable Diffusion 2", "url": "stabilityai/stable-diffusion-2-1"},
+    {"name": "stability AI", "url": "stabilityai/stable-diffusion-2-1-base"},
+    {"name": "Compressed-S-D", "url": "nota-ai/bk-sdm-small"},
+    {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
+    {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
+    {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
+    {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
+    {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
+]   
+
+
+####  REM-BG
+
+remove_bg_models = {
+    "TracerUniversalB7": "TracerUniversalB7",
+    "U2NET": "u2net",
+    "U2NET Human Seg": "u2net_human_seg",
+    "U2NET Cloth Seg": "u2net_cloth_seg"
+}
+
+model_choices = keys(remove_bg_models)
+
+
+def predict(image, session, smoot, matting, bg_color):
+
+    session = new_session(remove_bg_models[session])
+
+    try:
+        return remove(session, image, smoot, matting, bg_color)
+    except ValueError as err:
+        logging.error(err)
+        return make_label(str(err)), None
+
+
+def change_show_mask(chk_state):
+    return gr.Image.update(visible=chk_state)
+
+
+def change_include_matting(chk_state):
+    return gr.Box.update(visible=chk_state), (0, 0, 0), 0, 0, 0
+
+
+def change_foreground_threshold(fg_value, value):
+    fg, bg, erode = value
+    return fg_value, bg, erode
+
+
+def change_background_threshold(bg_value, value):
+    fg, bg, erode = value
+    return fg, bg_value, erode
+
+
+def change_erode_size(erode_value, value):
+    fg, bg, erode = value
+    return fg, bg, erode_value
+
+
+def set_dominant_color(chk_state):
+    return chk_state, gr.ColorPicker.update(value=False, visible=not chk_state)
+
+
+def change_picker_color(picker, dominant):
+    if not dominant:
+        return picker
+    return dominant
+
+
+def change_background_mode(chk_state):
+    return gr.ColorPicker.update(value=False, visible=chk_state), \
+        gr.Checkbox.update(value=False, visible=chk_state)
+
+
+
+###########
+
+text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
+
+current_model = models[0]
+
+models2 = []
+for model in models:
+    model_url = f"models/{model['url']}"
+    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
+    models2.append(loaded_model)
+
+def text_it(inputs, text_gen=text_gen):
+    return text_gen(inputs)
+
+def flip_text(x):
+    return x[::-1]
+
+def send_it(inputs, model_choice):
+    proc = models2[model_choice]
+    return proc(inputs)
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+def set_model(current_model_index):
+    global current_model
+    current_model = models[current_model_index]
+    return gr.update(value=f"{current_model['name']}")
+
+#define inference function
+#First: Get initial images for the grid display 
+def get_initial_images():
+  response = requests.post(API_URL_INITIAL, json={
+            "data": []
+            }).json()
+  #data = response["data"][0]['data'][0][0][:-1]
+  response_dict = response['data'][0]
+  return response_dict  #, [resp[0][:-1] for resp in response["data"][0]["data"]]
+
+#Second: Process response dictionary to get imges as hyperlinked image tags
+def process_response(response_dict):
+  return [resp[0][:-1] for resp in response_dict["data"]]
+
+response_dict = get_initial_images()
+initial = process_response(response_dict)
+initial_imgs  = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);">\n' + "\n".join(initial[:-1])
+
+#Third: Load more images for the grid
+def get_next10_images(response_dict, row_count):
+    row_count = int(row_count)
+    #print("(1)",type(response_dict))
+    #Convert the string to a dictionary
+    if isinstance(response_dict, dict) == False :
+        response_dict = ast.literal_eval(response_dict)
+    response = requests.post(API_URL_NEXT10, json={
+              "data": [response_dict, row_count ] #len(initial)-1
+               }).json()
+    row_count+=10
+    response_dict = response['data'][0]
+    #print("(2)",type(response))
+    #print("(3)",type(response['data'][0]))
+    next_set  = [resp[0][:-1] for resp in response_dict["data"]]
+    next_set_images = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); ">\n' + "\n".join(next_set[:-1])
+    return response_dict, row_count, next_set_images  #response['data'][0]
+
+
+with gr.Blocks(theme='pikto/theme@>=0.0.1,<0.0.3') as pan:
+    gr.Markdown("AI CONTENT TOOLS.")
+                    
+    with gr.Tab("T-to-I"):
+        
+    ##model = ("stabilityai/stable-diffusion-2-1")
+         model_name1 = gr.Dropdown(
+                label="Choose Model",
+                choices=[m["name"] for m in models],
+                type="index",
+                value=current_model["name"],
+                interactive=True,
+         )
+         input_text = gr.Textbox(label="Prompt idea",)
+
+        ##  run = gr.Button("Generate Images")
+         with gr.Row():
+             see_prompts = gr.Button("Generate Prompts")
+             run = gr.Button("Generate Images", variant="primary")
+        
+         with gr.Row():
+             magic1 = gr.Textbox(label="Generated Prompt", lines=2)
+             output1 = gr.Image(label="")
+          
+             
+         with gr.Row():    
+             magic2 = gr.Textbox(label="Generated Prompt", lines=2)
+             output2 = gr.Image(label="")
+
+            
+         run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
+         run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
+        
+    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2,])
+        
+    with gr.Tab("AI Library"):
+         #Using Gradio Demos as API - This is Hot!
+#get_next10_images(response_dict=response_dict, row_count=9)
+#position: fixed; top: 0; left: 0; width: 100%; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+
+#Defining the Blocks layout
+   # with gr.Blocks(css = """#img_search img {width: 100%; height: 100%; object-fit: cover;}""") as demo:
+         gr.HTML(value="top of page", elem_id="top",visible=False)
+         gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+            <div
+            style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+            "
+            >
+            <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+            Using Gradio API - 2 </h1><br></div>
+            <div><h4 style="font-weight: 500; margin-bottom: 7px; margin-top: 5px;">
+            Stream <a href="https://github.com/playgroundai/liked_images" target="_blank">PlaygroundAI Images</a> ina beautiful grid</h4><br>
+            </div>""")
+    with gr.Tab("AI Library"):
+    #with gr.Tab(): #(elem_id = "col-container"):
+          #gr.Column(): #(elem_id = "col-container"):
+             b1 = gr.Button("Load More Images").style(full_width=False)
+             df = gr.Textbox(visible=False,elem_id='dataframe', value=response_dict)
+             row_count = gr.Number(visible=False, value=19 )
+             img_search = gr.HTML(label = 'Images from PlaygroundAI dataset', elem_id="img_search", 
+                                 value=initial_imgs ) #initial[:-1] )
+      
+
+    b1.click(get_next10_images, [df, row_count], [df, row_count, img_search], api_name = "load_playgroundai_images" ) 
+            
+##########################  REM-BG
+    with gr.Tab("Rem_BG"):
+        
+        color_state = gr.State(value=False)
+        matting_state = gr.State(value=(0, 0, 0))
+        gr.HTML("<center><h1>Remove Background Tool</h1></center>")
+        
+        with gr.Row(equal_height=False):
+            with gr.Column():
+                input_img = gr.Image(type="pil", label="Input image")
+                drp_models = gr.Dropdown(choices=model_choices, label="Model Segment", value="TracerUniversalB7")
+
+            with gr.Row():
+                chk_include_matting = gr.Checkbox(label="Matting", value=False)
+                chk_smoot_mask = gr.Checkbox(label="Smoot Mask", value=False)
+                chk_show_mask = gr.Checkbox(label="Show Mask", value=False)
+            with gr.Box(visible=False) as slider_matting:
+                slr_fg_threshold = gr.Slider(0, 300, value=270, step=1, label="Alpha matting foreground threshold")
+                slr_bg_threshold = gr.Slider(0, 50, value=20, step=1, label="Alpha matting background threshold")
+                slr_erode_size = gr.Slider(0, 20, value=11, step=1, label="Alpha matting erode size")
+            with gr.Box():
+                with gr.Row():
+                    chk_change_color = gr.Checkbox(label="Change background color", value=False)
+                    pkr_color = gr.ColorPicker(label="Pick a new color", visible=False)
+                    chk_dominant = gr.Checkbox(label="Use dominant color", value=False, visible=False)
+
+                #######################
+                    ############################
+                    #############################
+                run_btn = gr.Button(value="Remove background", variant="primary")        
+
+            with gr.Column():
+                output_img = gr.Image(type="pil", label="Image Result")
+                mask_img = gr.Image(type="pil", label="Image Mask", visible=False)
+                gr.ClearButton(components=[input_img, output_img, mask_img])
+
+        chk_include_matting.change(change_include_matting, inputs=[chk_include_matting],
+                               outputs=[slider_matting, matting_state,
+                                        slr_fg_threshold, slr_bg_threshold, slr_erode_size])
+        
+        slr_bg_threshold.change(change_background_threshold, inputs=[slr_bg_threshold, matting_state],
+                            outputs=[matting_state])
+
+        slr_fg_threshold.change(change_foreground_threshold, inputs=[slr_fg_threshold, matting_state],
+                            outputs=[matting_state])
+
+        slr_erode_size.change(change_erode_size, inputs=[slr_erode_size, matting_state],
+                          outputs=[matting_state])
+
+        chk_show_mask.change(change_show_mask, inputs=[chk_show_mask], outputs=[mask_img])
+
+        chk_change_color.change(change_background_mode, inputs=[chk_change_color],
+                            outputs=[pkr_color, chk_dominant])
+
+        pkr_color.change(change_picker_color, inputs=[pkr_color, chk_dominant], outputs=[color_state])
+
+        chk_dominant.change(set_dominant_color, inputs=[chk_dominant], outputs=[color_state, pkr_color])
+
+        run_btn.click(predict, inputs=[input_img, drp_models, chk_smoot_mask, matting_state, color_state],
+                  outputs=[output_img, mask_img])
+
+
+                
+#            text_input = gr.Textbox()                      ##   Diffuser
+#            image_output = gr.Image()
+#        image_button = gr.Button("Flip")
+
+
+
+   # text_button.click(flip_text, inputs=text_input, outputs=text_output)
+   # image_button.click(flip_image, inputs=image_input, outputs=image_output)
+pan.queue(concurrency_count=200)
+pan.launch(inline=True, show_api=True, max_threads=400)

back-app.py

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+import numpy as np
+import gradio as gr
+import ast
+import requests
+
+from theme_dropdown import create_theme_dropdown  # noqa: F401
+
+dropdown, js = create_theme_dropdown()
+
+models = [
+    {"name": "Stable Diffusion 2", "url": "stabilityai/stable-diffusion-2-1"},
+    {"name": "stability AI", "url": "stabilityai/stable-diffusion-2-1-base"},
+    {"name": "Compressed-S-D", "url": "nota-ai/bk-sdm-small"},
+    {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
+    {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
+    {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
+    {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
+    {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
+]   
+
+text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
+
+current_model = models[0]
+
+models2 = []
+for model in models:
+    model_url = f"models/{model['url']}"
+    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
+    models2.append(loaded_model)
+
+def text_it(inputs, text_gen=text_gen):
+    return text_gen(inputs)
+
+def flip_text(x):
+    return x[::-1]
+
+def send_it(inputs, model_choice):
+    proc = models2[model_choice]
+    return proc(inputs)
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+def set_model(current_model_index):
+    global current_model
+    current_model = models[current_model_index]
+    return gr.update(value=f"{current_model['name']}")
+
+
+with gr.Blocks(theme='pikto/theme@>=0.0.1,<0.0.3') as pan:
+    gr.Markdown("AI CONTENT TOOLS.")
+                    
+    with gr.Tab("T-to-I"):
+        
+    ##model = ("stabilityai/stable-diffusion-2-1")
+         model_name1 = gr.Dropdown(
+                label="Choose Model",
+                choices=[m["name"] for m in models],
+                type="index",
+                value=current_model["name"],
+                interactive=True,
+         )
+         input_text = gr.Textbox(label="Prompt idea",)
+
+        ##  run = gr.Button("Generate Images")
+         with gr.Row():
+             see_prompts = gr.Button("Generate Prompts")
+             run = gr.Button("Generate Images", variant="primary")
+        
+         with gr.Row():
+             magic1 = gr.Textbox(label="Generated Prompt", lines=2)
+             output1 = gr.Image(label="")
+          
+             
+         with gr.Row():    
+             magic2 = gr.Textbox(label="Generated Prompt", lines=2)
+             output2 = gr.Image(label="")
+
+            
+         run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
+         run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
+        
+    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2,])
+        
+    #with gr.Tab("Flip Image"):
+         #Using Gradio Demos as API - This is Hot!
+           
+
+    with gr.Tab("Diffuser"):
+        with gr.Row():
+            text_input = gr.Textbox()                      ##   Diffuser
+            image_output = gr.Image()
+        image_button = gr.Button("Flip")
+
+
+
+   # text_button.click(flip_text, inputs=text_input, outputs=text_output)
+   # image_button.click(flip_image, inputs=image_input, outputs=image_output)
+pan.queue(concurrency_count=200)
+pan.launch(inline=True, show_api=True, max_threads=400)

config.json

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+let job = await createJob({
+	prompt: "puppies in a cloud, 4k",
+});
+
+console.log("Job Created! Waiting...");
+
+while (job.status !== "succeeded" && job.status !== "failed") {
+	await new Promise((resolve) => setTimeout(resolve, 250));
+
+	job = await getJob(job.job);
+}
+
+if(job.status !== "succeeded") {
+	throw new Error("Job failed!"); 
+}
+
+console.log("Generation completed!", job.imageUrl);

cutter.py

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+import PIL
+import numpy as np
+from PIL import Image, ImageColor, ImageDraw
+from PIL.Image import Image as PILImage
+from pymatting.alpha.estimate_alpha_cf import estimate_alpha_cf
+from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
+from pymatting.util.util import stack_images
+from rembg.bg import post_process, naive_cutout, apply_background_color
+from scipy.ndimage import binary_erosion
+
+
+def alpha_matting_cutout(img: PILImage, trimap: np.ndarray) -> PILImage:
+    if img.mode == "RGBA" or img.mode == "CMYK":
+        img = img.convert("RGB")
+
+    img = np.asarray(img)
+
+    img_normalized = img / 255.0
+    trimap_normalized = trimap / 255.0
+
+    alpha = estimate_alpha_cf(img_normalized, trimap_normalized)
+    foreground = estimate_foreground_ml(img_normalized, alpha)
+    cutout = stack_images(foreground, alpha)
+
+    cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8)
+    return Image.fromarray(cutout)
+
+
+def generate_trimap(
+        mask: PILImage,
+        foreground_threshold: int,
+        background_threshold: int,
+        erode_structure_size: int,
+) -> np.ndarray:
+    mask = np.asarray(mask)
+
+    is_foreground = mask > foreground_threshold
+    is_background = mask < background_threshold
+
+    structure = None
+    if erode_structure_size > 0:
+        structure = np.ones(
+            (erode_structure_size, erode_structure_size), dtype=np.uint8
+        )
+
+    is_foreground = binary_erosion(is_foreground, structure=structure)
+    is_background = binary_erosion(is_background, structure=structure, border_value=1)
+
+    trimap = np.full(mask.shape, dtype=np.uint8, fill_value=128)
+    trimap[is_foreground] = 255
+    trimap[is_background] = 0
+
+    return trimap
+
+
+def get_background_dominant_color(img: PILImage, mask: PILImage) -> tuple:
+    negative_img = img.copy()
+    negative_mask = PIL.ImageOps.invert(mask)
+    negative_img.putalpha(negative_mask)
+    negative_img = negative_img.resize((1, 1))
+    r, g, b, a = negative_img.getpixel((0, 0))
+    return r, g, b, 255
+
+
+def remove(session, img: PILImage, smoot: bool, matting: tuple, color) -> (PILImage, PILImage):
+    mask = session.predict(img)[0]
+
+    if smoot:
+        mask = PIL.Image.fromarray(post_process(np.array(mask)))
+
+    fg_t, bg_t, erode = matting
+
+    if fg_t > 0 or bg_t > 0 or erode > 0:
+        mask = generate_trimap(mask, *matting)
+        try:
+            cutout = alpha_matting_cutout(img, mask)
+            mask = PIL.Image.fromarray(mask)
+        except ValueError as err:
+            raise err
+    else:
+        cutout = naive_cutout(img, mask)
+
+    if color is True:
+        color = get_background_dominant_color(img, mask)
+        cutout = apply_background_color(cutout, color)
+    elif isinstance(color, str):
+        r, g, b = ImageColor.getcolor(color, "RGB")
+        cutout = apply_background_color(cutout, (r, g, b, 255))
+
+    return cutout, mask
+
+
+def make_label(text, width=600, height=200, color="black") -> PILImage:
+    image = Image.new("RGB", (width, height), color)
+    draw = ImageDraw.Draw(image)
+    text_width, text_height = draw.textsize(text)
+    draw.text(((width-text_width)/2, height/2), text)
+    return image

flipper.py

@@ -0,0 +1,31 @@
+import numpy as np
+import gradio as gr
+
+
+def flip_text(x):
+    return x[::-1]
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Flip text or image files using this demo.")
+    with gr.Tab("Flip Text"):
+        text_input = gr.Textbox()
+        text_output = gr.Textbox()
+        text_button = gr.Button("Flip")
+    with gr.Tab("Flip Image"):
+        with gr.Row():
+            image_input = gr.Image()
+            image_output = gr.Image()
+        image_button = gr.Button("Flip")
+
+    with gr.Accordion("Open for More!"):
+        gr.Markdown("Look at me...")
+
+    text_button.click(flip_text, inputs=text_input, outputs=text_output)
+    image_button.click(flip_image, inputs=image_input, outputs=image_output)
+
+demo.launch()

play.py

@@ -0,0 +1,82 @@
+import ast
+import requests
+
+#Using Gradio Demos as API - This is Hot!
+API_URL_INITIAL = "https://ysharma-playground-ai-exploration.hf.space/run/initial_dataframe"
+API_URL_NEXT10 = "https://ysharma-playground-ai-exploration.hf.space/run/next_10_rows"
+
+#define inference function
+#First: Get initial images for the grid display 
+def get_initial_images():
+  response = requests.post(API_URL_INITIAL, json={
+            "data": []
+            }).json()
+  #data = response["data"][0]['data'][0][0][:-1]
+  response_dict = response['data'][0]
+  return response_dict  #, [resp[0][:-1] for resp in response["data"][0]["data"]]
+
+#Second: Process response dictionary to get imges as hyperlinked image tags
+def process_response(response_dict):
+  return [resp[0][:-1] for resp in response_dict["data"]]
+
+response_dict = get_initial_images()
+initial = process_response(response_dict)
+initial_imgs  = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);">\n' + "\n".join(initial[:-1])
+
+#Third: Load more images for the grid
+def get_next10_images(response_dict, row_count):
+    row_count = int(row_count)
+    #print("(1)",type(response_dict))
+    #Convert the string to a dictionary
+    if isinstance(response_dict, dict) == False :
+        response_dict = ast.literal_eval(response_dict)
+    response = requests.post(API_URL_NEXT10, json={
+              "data": [response_dict, row_count ] #len(initial)-1
+               }).json()
+    row_count+=10
+    response_dict = response['data'][0]
+    #print("(2)",type(response))
+    #print("(3)",type(response['data'][0]))
+    next_set  = [resp[0][:-1] for resp in response_dict["data"]]
+    next_set_images = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); ">\n' + "\n".join(next_set[:-1])
+    return response_dict, row_count, next_set_images  #response['data'][0]
+
+#get_next10_images(response_dict=response_dict, row_count=9)
+#position: fixed; top: 0; left: 0; width: 100%; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+
+#Defining the Blocks layout
+with gr.Blocks(css = """#img_search img {width: 100%; height: 100%; object-fit: cover;}""") as demo:
+  gr.HTML(value="top of page", elem_id="top",visible=False)
+  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+        <div
+        style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+        "
+        >
+        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+            Using Gradio Demos as API - 2 </h1><br></div>
+        <div><h4 style="font-weight: 500; margin-bottom: 7px; margin-top: 5px;">
+            Stream <a href="https://github.com/playgroundai/liked_images" target="_blank">PlaygroundAI Images</a> ina beautiful grid</h4><br>
+        </div>""")
+  with gr.Accordion(label="Details about the working:", open=False, elem_id='accordion'):
+    gr.HTML("""
+        <p style="margin-bottom: 10px; font-size: 90%"><br>
+        ▶️Do you see the "view api" link located in the footer of this application? 
+        By clicking on this link, a page will open which provides documentation on the REST API that developers can use to query the Interface function / Block events.<br>
+        ▶️In this demo, I am making such an API request to the <a href="https://huggingface.co/spaces/ysharma/Playground_AI_Exploration" target="_blank">Playground_AI_Exploration</a> Space.<br>
+        ▶️I am exposing an API endpoint of this Gradio app as well. This can easily be done by one line of code, just set the api_name parameter of the event listener.
+        </p></div>""")
+
+  with gr.Column(): #(elem_id = "col-container"):
+    b1 = gr.Button("Load More Images").style(full_width=False)
+    df = gr.Textbox(visible=False,elem_id='dataframe', value=response_dict)
+    row_count = gr.Number(visible=False, value=19 )
+    img_search = gr.HTML(label = 'Images from PlaygroundAI dataset', elem_id="img_search", 
+                         value=initial_imgs ) #initial[:-1] )
+      
+  gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/Stream_PlaygroundAI_Images?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></center> 
+        </p></div>''')
+  b1.click(get_next10_images, [df, row_count], [df, row_count, img_search], api_name = "load_playgroundai_images" ) 

pob

@@ -0,0 +1,63 @@
+import gradio as gr
+import requests
+import json
+url = "https://api.prodia.com/v1/job"
+
+headers = {
+    "accept": "application/json",
+    "content-type": "application/json",
+    "X-Prodia-Key": "69e66898-010d-4cd1-9e22-090f73ad007b"
+}
+models = [
+    {"name": "Timeless", "url": "timeless-1.0.ckpt 1.0.ckpt [7c4971d4]"},
+    {"name": "Dreamlike-diffusion-2.0.", "url": "dreamlike-diffusion-2.0.safetensors [fdcf65e7]"},
+    {"name": "Deliberate_v2", "url": "deliberate_v2.safetensors [10ec4b29]"},
+    {"name": "Anything-v4.5-pruned", "url": "anything-v4.5-pruned.ckpt [65745d25]"},
+]
+
+current_model = models[0]
+
+models2 = []
+for model in models:
+    model_url = f"models/{model['url']}"
+    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
+    models2.append(loaded_model)
+
+
+def text_it(inputs, text_gen=text_gen):
+    return text_gen(inputs)
+
+
+def set_model(current_model_index):
+    global current_model
+    current_model = models[current_model_index]
+    return gr.update(value=f"{current_model['name']}")
+
+
+def send_it(inputs, model_choice):
+    proc = models2[model_choice]
+    return proc(inputs)
+
+
+with gr.Blocks() as myface:
+    gr.HTML(
+
+    )
+
+    with gr.Row():
+        with gr.Row():
+            input_text = gr.Textbox(label="Iput Prompt",  placeholder="", lines=1)
+            # Model selection dropdown
+            model_name1 = gr.Dropdown(
+                label="Choose Model",
+                choices=[m["name"] for m in models],
+                type="index",
+                value=current_model["name"],
+                interactive=True,
+            )
+        with gr.Row():
+            see_prompts = gr.Button("Generate Prompts")
+            run = gr.Button("Generate Images", variant="primary")
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+rembg~=2.0.47
+pillow~=9.5.0
+pymatting
+opencv-python-headless

theme_dropdown.py

@@ -0,0 +1,57 @@
+import os
+import pathlib
+
+from gradio.themes.utils import ThemeAsset
+
+
+def create_theme_dropdown():
+    import gradio as gr
+
+    asset_path = pathlib.Path(__file__).parent / "themes"
+    themes = []
+    for theme_asset in os.listdir(str(asset_path)):
+        themes.append(
+            (ThemeAsset(theme_asset), gr.Theme.load(str(asset_path / theme_asset)))
+        )
+
+    def make_else_if(theme_asset):
+        return f"""
+        else if (theme == '{str(theme_asset[0].version)}') {{
+            var theme_css = `{theme_asset[1]._get_theme_css()}`
+        }}"""
+
+    head, tail = themes[0], themes[1:]
+    if_statement = f"""
+        if (theme == "{str(head[0].version)}") {{
+            var theme_css = `{head[1]._get_theme_css()}`
+        }} {" ".join(make_else_if(t) for t in tail)}
+    """
+
+    latest_to_oldest = sorted([t[0] for t in themes], key=lambda asset: asset.version)[
+        ::-1
+    ]
+    latest_to_oldest = [str(t.version) for t in latest_to_oldest]
+
+    component = gr.Dropdown(
+        choices=latest_to_oldest,
+        value=latest_to_oldest[0],
+        render=False,
+        label="Select Version",
+    ).style(container=False)
+
+    return (
+        component,
+        f"""
+        (theme) => {{
+            if (!document.querySelector('.theme-css')) {{
+                var theme_elem = document.createElement('style');
+                theme_elem.classList.add('theme-css');
+                document.head.appendChild(theme_elem);
+            }} else {{
+                var theme_elem = document.querySelector('.theme-css');
+            }}
+            {if_statement}
+            theme_elem.innerHTML = theme_css;
+        }}
+    """,
+    )

themes/theme_schema@0.0.1.json

@@ -0,0 +1 @@
+{"theme": {"_font": [{"__gradio_font__": true, "name": "Poppins", "class": "google"}, {"__gradio_font__": true, "name": "Source Sans Pro", "class": "google"}, {"__gradio_font__": true, "name": "system-ui", "class": "font"}, {"__gradio_font__": true, "name": "sans-system-ui", "class": "font"}], "_font_mono": [{"__gradio_font__": true, "name": "DM Mono", "class": "google"}, {"__gradio_font__": true, "name": "ui-monospace", "class": "font"}, {"__gradio_font__": true, "name": "Consolas", "class": "font"}, {"__gradio_font__": true, "name": "monospace", "class": "font"}], "_stylesheets": ["https://fonts.googleapis.com/css2?family=Poppins:wght@400;600&display=swap", "https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap", "https://fonts.googleapis.com/css2?family=DM+Mono:wght@400;600&display=swap"], "background_fill_primary": "white", "background_fill_primary_dark": "*neutral_950", "background_fill_secondary": "*neutral_50", "background_fill_secondary_dark": "*neutral_900", "block_background_fill": "*background_fill_primary", "block_background_fill_dark": "*neutral_800", "block_border_color": "*border_color_primary", "block_border_color_dark": "*border_color_primary", "block_border_width": "1px", "block_info_text_color": "*body_text_color_subdued", "block_info_text_color_dark": "*body_text_color_subdued", "block_info_text_size": "*text_sm", "block_info_text_weight": "400", "block_label_background_fill": "*background_fill_primary", "block_label_background_fill_dark": "*background_fill_secondary", "block_label_border_color": "*border_color_primary", "block_label_border_color_dark": "*border_color_primary", "block_label_border_width": "1px", "block_label_margin": "0", "block_label_padding": "*spacing_sm *spacing_lg", "block_label_radius": "calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0", "block_label_right_radius": "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)", "block_label_text_color": "*neutral_500", "block_label_text_color_dark": "*neutral_200", "block_label_text_size": "*text_sm", "block_label_text_weight": "400", "block_padding": "*spacing_xl calc(*spacing_xl + 2px)", "block_radius": "*radius_lg", "block_shadow": "none", "block_title_background_fill": "none", "block_title_border_color": "none", "block_title_border_width": "0px", "block_title_padding": "0", "block_title_radius": "none", "block_title_text_color": "*neutral_500", "block_title_text_color_dark": "*neutral_200", "block_title_text_size": "*text_md", "block_title_text_weight": "400", "body_background_fill": "*background_fill_primary", "body_background_fill_dark": "*background_fill_primary", "body_text_color": "*neutral_800", "body_text_color_dark": "*neutral_100", "body_text_color_subdued": "*neutral_400", "body_text_color_subdued_dark": "*neutral_400", "body_text_size": "*text_md", "body_text_weight": "400", "border_color_accent": "*primary_300", "border_color_accent_dark": "*neutral_600", "border_color_primary": "*neutral_200", "border_color_primary_dark": "*neutral_700", "button_border_width": "*input_border_width", "button_border_width_dark": "*input_border_width", "button_cancel_background_fill": "*button_secondary_background_fill", "button_cancel_background_fill_dark": "*button_secondary_background_fill", "button_cancel_background_fill_hover": "*button_cancel_background_fill", "button_cancel_background_fill_hover_dark": "*button_cancel_background_fill", "button_cancel_border_color": "*button_secondary_border_color", "button_cancel_border_color_dark": "*button_secondary_border_color", "button_cancel_border_color_hover": "*button_cancel_border_color", "button_cancel_border_color_hover_dark": "*button_cancel_border_color", "button_cancel_text_color": "*button_secondary_text_color", "button_cancel_text_color_dark": "*button_secondary_text_color", "button_cancel_text_color_hover": "*button_cancel_text_color", "button_cancel_text_color_hover_dark": "*button_cancel_text_color", "button_large_padding": "*spacing_lg calc(2 * *spacing_lg)", "button_large_radius": "*radius_lg", "button_large_text_size": "*text_lg", "button_large_text_weight": "600", "button_primary_background_fill": "*primary_200", "button_primary_background_fill_dark": "*primary_700", "button_primary_background_fill_hover": "*button_primary_background_fill", "button_primary_background_fill_hover_dark": "*button_primary_background_fill", "button_primary_border_color": "*primary_200", "button_primary_border_color_dark": "*primary_600", "button_primary_border_color_hover": "*button_primary_border_color", "button_primary_border_color_hover_dark": "*button_primary_border_color", "button_primary_text_color": "*primary_600", "button_primary_text_color_dark": "white", "button_primary_text_color_hover": "*button_primary_text_color", "button_primary_text_color_hover_dark": "*button_primary_text_color", "button_secondary_background_fill": "*neutral_200", "button_secondary_background_fill_dark": "*neutral_600", "button_secondary_background_fill_hover": "*button_secondary_background_fill", "button_secondary_background_fill_hover_dark": "*button_secondary_background_fill", "button_secondary_border_color": "*neutral_200", "button_secondary_border_color_dark": "*neutral_600", "button_secondary_border_color_hover": "*button_secondary_border_color", "button_secondary_border_color_hover_dark": "*button_secondary_border_color", "button_secondary_text_color": "*neutral_700", "button_secondary_text_color_dark": "white", "button_secondary_text_color_hover": "*button_secondary_text_color", "button_secondary_text_color_hover_dark": "*button_secondary_text_color", "button_shadow": "none", "button_shadow_active": "none", "button_shadow_hover": "none", "button_small_padding": "*spacing_sm calc(2 * *spacing_sm)", "button_small_radius": "*radius_lg", "button_small_text_size": "*text_md", "button_small_text_weight": "400", "button_transition": "background-color 0.2s ease", "checkbox_background_color": "*background_fill_primary", "checkbox_background_color_dark": "*neutral_800", "checkbox_background_color_focus": "*checkbox_background_color", "checkbox_background_color_focus_dark": "*checkbox_background_color", "checkbox_background_color_hover": "*checkbox_background_color", "checkbox_background_color_hover_dark": "*checkbox_background_color", "checkbox_background_color_selected": "*secondary_600", "checkbox_background_color_selected_dark": "*secondary_600", "checkbox_border_color": "*neutral_300", "checkbox_border_color_dark": "*neutral_700", "checkbox_border_color_focus": "*secondary_500", "checkbox_border_color_focus_dark": "*secondary_500", "checkbox_border_color_hover": "*neutral_300", "checkbox_border_color_hover_dark": "*neutral_600", "checkbox_border_color_selected": "*secondary_600", "checkbox_border_color_selected_dark": "*secondary_600", "checkbox_border_radius": "*radius_sm", "checkbox_border_width": "*input_border_width", "checkbox_border_width_dark": "*input_border_width", "checkbox_check": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")", "checkbox_label_background_fill": "*button_secondary_background_fill", "checkbox_label_background_fill_dark": "*button_secondary_background_fill", "checkbox_label_background_fill_hover": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_hover_dark": "*button_secondary_background_fill_hover", "checkbox_label_background_fill_selected": "*checkbox_label_background_fill", "checkbox_label_background_fill_selected_dark": "*checkbox_label_background_fill", "checkbox_label_border_color": "*border_color_primary", "checkbox_label_border_color_dark": "*border_color_primary", "checkbox_label_border_color_hover": "*checkbox_label_border_color", "checkbox_label_border_color_hover_dark": "*checkbox_label_border_color", "checkbox_label_border_width": "*input_border_width", "checkbox_label_border_width_dark": "*input_border_width", "checkbox_label_gap": "*spacing_lg", "checkbox_label_padding": "*spacing_md calc(2 * *spacing_md)", "checkbox_label_shadow": "none", "checkbox_label_text_color": "*body_text_color", "checkbox_label_text_color_dark": "*body_text_color", "checkbox_label_text_color_selected": "*checkbox_label_text_color", "checkbox_label_text_color_selected_dark": "*checkbox_label_text_color", "checkbox_label_text_size": "*text_md", "checkbox_label_text_weight": "400", "checkbox_shadow": "*input_shadow", "color_accent": "*primary_500", "color_accent_soft": "*primary_50", "color_accent_soft_dark": "*neutral_700", "container_radius": "*radius_lg", "embed_radius": "*radius_lg", "error_background_fill": "#fee2e2", "error_background_fill_dark": "*background_fill_primary", "error_border_color": "#fecaca", "error_border_color_dark": "*border_color_primary", "error_border_width": "1px", "error_text_color": "#ef4444", "error_text_color_dark": "#ef4444", "font": "'Poppins', 'Source Sans Pro', 'system-ui', 'sans-system-ui'", "font_mono": "'DM Mono', 'ui-monospace', 'Consolas', monospace", "form_gap_width": "0px", "input_background_fill": "*neutral_100", "input_background_fill_dark": "*neutral_700", "input_background_fill_focus": "*secondary_500", "input_background_fill_focus_dark": "*secondary_600", "input_background_fill_hover": "*input_background_fill", "input_background_fill_hover_dark": "*input_background_fill", "input_border_color": "*border_color_primary", "input_border_color_dark": "*border_color_primary", "input_border_color_focus": "*secondary_300", "input_border_color_focus_dark": "*neutral_700", "input_border_color_hover": "*input_border_color", "input_border_color_hover_dark": "*input_border_color", "input_border_width": "0px", "input_padding": "*spacing_xl", "input_placeholder_color": "*neutral_400", "input_placeholder_color_dark": "*neutral_500", "input_radius": "*radius_lg", "input_shadow": "none", "input_shadow_focus": "*input_shadow", "input_text_size": "*text_md", "input_text_weight": "400", "layout_gap": "*spacing_xxl", "link_text_color": "*secondary_600", "link_text_color_active": "*secondary_600", "link_text_color_active_dark": "*secondary_500", "link_text_color_dark": "*secondary_500", "link_text_color_hover": "*secondary_700", "link_text_color_hover_dark": "*secondary_400", "link_text_color_visited": "*secondary_500", "link_text_color_visited_dark": "*secondary_600", "loader_color": "*color_accent", "name": "base", "neutral_100": "#f4f4f5", "neutral_200": "#e4e4e7", "neutral_300": "#d4d4d8", "neutral_400": "#a1a1aa", "neutral_50": "#fafafa", "neutral_500": "#71717a", "neutral_600": "#52525b", "neutral_700": "#3f3f46", "neutral_800": "#27272a", "neutral_900": "#18181b", "neutral_950": "#0f0f11", "panel_background_fill": "*background_fill_secondary", "panel_background_fill_dark": "*background_fill_secondary", "panel_border_color": "*border_color_primary", "panel_border_color_dark": "*border_color_primary", "panel_border_width": "0", "primary_100": "#d6def5", "primary_200": "#adbeeb", "primary_300": "#819adf", "primary_400": "#5879d5", "primary_50": "#ebeffa", "primary_500": "#3e5ac8", "primary_600": "#28489f", "primary_700": "#112344", "primary_800": "#0b172d", "primary_900": "#060d18", "primary_950": "#03060c", "prose_header_text_weight": "600", "prose_text_size": "*text_md", "prose_text_weight": "400", "radio_circle": "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")", "radius_lg": "12px", "radius_md": "8px", "radius_sm": "6px", "radius_xl": "16px", "radius_xs": "4px", "radius_xxl": "24px", "radius_xxs": "2px", "secondary_100": "#f3f4f6", "secondary_200": "#e5e7eb", "secondary_300": "#d1d5db", "secondary_400": "#9ca3af", "secondary_50": "#f9fafb", "secondary_500": "#6b7280", "secondary_600": "#4b5563", "secondary_700": "#374151", "secondary_800": "#1f2937", "secondary_900": "#111827", "secondary_950": "#0b0f19", "section_header_text_size": "*text_md", "section_header_text_weight": "400", "shadow_drop": "rgba(0,0,0,0.05) 0px 1px 2px 0px", "shadow_drop_lg": "0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)", "shadow_inset": "rgba(0,0,0,0.05) 0px 2px 4px 0px inset", "shadow_spread": "3px", "shadow_spread_dark": "1px", "slider_color": "auto", "spacing_lg": "10px", "spacing_md": "8px", "spacing_sm": "6px", "spacing_xl": "14px", "spacing_xs": "4px", "spacing_xxl": "28px", "spacing_xxs": "2px", "stat_background_fill": "*primary_300", "stat_background_fill_dark": "*primary_500", "table_border_color": "*neutral_300", "table_border_color_dark": "*neutral_700", "table_even_background_fill": "white", "table_even_background_fill_dark": "*neutral_950", "table_odd_background_fill": "*neutral_50", "table_odd_background_fill_dark": "*neutral_900", "table_radius": "*radius_lg", "table_row_focus": "*color_accent_soft", "table_row_focus_dark": "*color_accent_soft", "text_lg": "16px", "text_md": "14px", "text_sm": "12px", "text_xl": "22px", "text_xs": "10px", "text_xxl": "26px", "text_xxs": "9px"}, "version": "0.0.1"}

transform.py

@@ -0,0 +1,13 @@
+import requests
+
+url = "https://api.prodia.com/v1/transform"
+
+headers = {
+    "accept": "application/json",
+    "content-type": "application/json",
+    "X-Prodia-Key": "69e66898-010d-4cd1-9e22-090f73ad007b"
+}
+
+response = requests.post(url, headers=headers)
+
+print(response.text)

utils.py

@@ -0,0 +1,6 @@
+def keys(dictionary: dict):
+    return [k for k, v in dictionary.items()]
+
+
+def split_numbers(numbers: str):
+    return [int(i) for i in numbers.split(",")]