Create app.py

app.py

@@ -0,0 +1,221 @@
+import gradio as gr
+import pandas as pd
+import requests
+import os
+import numpy as np
+import re
+from tqdm import tqdm
+from time import sleep
+from PIL import Image
+import requests
+from io import BytesIO
+from datasets import Dataset, load_dataset
+import json
+import cv2
+import pathlib
+
+import imagehash
+
+MAX_MODEL_NUM = 300
+
+'''
+Yntec digplay
+'''
+
+hf_civital_image_info_dataset = load_dataset("svjack/hf_civital_image_info")
+hf_civital_image_info_df = hf_civital_image_info_dataset["train"].to_pandas()
+
+def gen_interface(model_name, max_times = 3):
+    times = 0
+    gr_model_interface = None
+    while gr_model_interface is None and times < max_times:
+        try:
+            gr_model_interface = gr.load("models/{}".format(model_name),live=True)
+        except:
+            print("error {} times {}".format(model_name, times))
+            sleep(2)
+        times += 1
+    return gr_model_interface
+
+#gr_model_interface.title
+def toImgOpenCV(imgPIL): # Conver imgPIL to imgOpenCV
+    i = np.array(imgPIL) # After mapping from PIL to numpy : [R,G,B,A]
+                         # numpy Image Channel system: [B,G,R,A]
+    red = i[:,:,0].copy(); i[:,:,0] = i[:,:,2].copy(); i[:,:,2] = red;
+    return i;
+
+def toImgPIL(imgOpenCV): return Image.fromarray(cv2.cvtColor(imgOpenCV, cv2.COLOR_BGR2RGB));
+
+def jpg_val_to_img(jpg_bytes):
+    img_buf = np.frombuffer(jpg_bytes, np.uint8)
+    img = cv2.imdecode(img_buf, cv2.IMREAD_UNCHANGED)
+    return toImgPIL(img)
+
+
+model_list = hf_civital_image_info_df["hf_repo_id"].drop_duplicates().values.tolist()
+model_interface_list = []
+for model_name in tqdm(model_list):
+    gr_model_interface = gen_interface(model_name)
+    if gr_model_interface is not None:
+        model_interface_list.append(gr_model_interface)
+        if len(model_interface_list) >= MAX_MODEL_NUM:
+            break
+
+def get_civital_iframe(url, width = 1400, height = 768, as_html = True, visible = False):
+    html= '''
+            <div style="justify-content: center; display: flex;">
+                    <iframe
+                    src="{}"
+                    frameborder="0"
+                    width="{}"
+                    height="{}"
+                    ></iframe>
+            </div>
+        '''.format(url, width, height)
+    if as_html:
+        html = gr.HTML(html, visible = visible)
+    return html
+
+def get_info_by_interface(gr_interface, model_interface_list = model_interface_list):
+    #### out: (gr_interface, civital_url, civital_info)
+    if hasattr(gr_interface, "app"):
+        civital_url = hf_civital_image_info_df[
+            hf_civital_image_info_df["hf_repo_id"] == gr_interface.title
+        ]["civital_url"].iloc[0]
+        civital_info = hf_civital_image_info_df[
+            hf_civital_image_info_df["hf_repo_id"] == gr_interface.title
+        ][["prompt", "image"]].values.tolist()
+        return gr_interface ,civital_url, civital_info
+    else:
+        civital_url = hf_civital_image_info_df[
+            hf_civital_image_info_df["hf_repo_id"] == gr_interface
+        ]["civital_url"].iloc[0]
+        civital_info = hf_civital_image_info_df[
+            hf_civital_image_info_df["hf_repo_id"] == gr_interface
+        ][["prompt", "image"]].values.tolist()
+        return list(filter(lambda x:x.title == gr_interface, model_interface_list))[0] ,civital_url, civital_info
+
+def read_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    return img
+
+def image_click(images, evt: gr.SelectData, gr_interface_value,
+    ):
+    img_selected = images[evt.index]
+    #print(img_selected)
+    im_data = img_selected["name"]
+    im = Image.open(im_data)
+    im_hash = imagehash.average_hash(
+        im, hash_size = 1024
+    )
+    min_diff = int(1e10)
+    #print(-1)
+    repo_card_im_dict = dict(
+    get_info_by_interface(gr_interface_value)[2]
+    )
+    min_repo_name = ""
+    for idx ,(repo_name, repo_card_image) in enumerate(repo_card_im_dict.items()):
+        repo_img = jpg_val_to_img(repo_card_image["bytes"])
+        repo_img_hash = imagehash.average_hash(
+            repo_img, hash_size = 1024
+        )
+        diff = im_hash - repo_img_hash
+        if diff < min_diff:
+            min_diff = diff
+            min_repo_name = repo_name
+        #print(idx)
+    prompt = min_repo_name
+    return prompt
+    #return prompt, im
+
+def try_repo_act_func(civital_url, show_civital_button):
+    repo_html_iframe_hide = get_civital_iframe(civital_url, visible = True if show_civital_button == "Show Civital Page" else False)
+    return repo_html_iframe_hide,  gr.Button("Hide Civital Page" if show_civital_button == "Show Civital Page" else "Show Civital Page")
+
+with gr.Blocks(
+    css = '''
+    .header img {
+          float: middle;
+          width: 33px;
+          height: 33px;
+        }
+        .header h1 {
+          top: 18px;
+          left: 10px;
+        }
+    '''
+) as demo:
+    gr.HTML(
+        '''
+        <center>
+        <div class="header">
+        <h1 class = "logo"> <img src="https://huggingface.co/spaces/svjack/Civital-Stable-Diffusion-HF/resolve/main/logo.png" alt="logo" />  🤗 Civital Model on Huggingface </h1>
+        </center>
+        '''
+    )
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                hf_model_dropdown = gr.Dropdown(label = "🤗 Hf model",
+                    choices=list(map(lambda x: x.title, model_interface_list)),
+                    value=list(map(lambda x: x.title, model_interface_list))[0],)
+            with gr.Column():
+                with gr.Row():
+                    civital_prompt = gr.Textbox(label = "🤭 Civital Prompt (Click from 👉 right gallery to get them, and You can edit ✍️ yourself)",
+                        interactive = True,
+                    )
+                    gen_button = gr.Button(label = "Generate")
+                hf_image = gr.Image(label = "🤭 Image generate by 🤗 Huggingface", height = 768)
+
+        with gr.Column():
+            civital_info_gallery = gr.Gallery(
+                pd.Series(
+                get_info_by_interface(hf_model_dropdown.value)[2]
+                ).sample(n = min(len(get_info_by_interface(hf_model_dropdown.value)[2]), 30)).map(lambda t2: t2[1]).map(lambda x: x["bytes"]).map(jpg_val_to_img).values.tolist(),
+                height = 1024,
+                label = "🖱️👇 ➡️ 👈 Civital image samples",
+                object_fit = "contain"
+            )
+
+    with gr.Row():
+        with gr.Column():
+            try_repo_button = gr.Button("Show Civital Page")
+            civital_iframe_html = get_civital_iframe(
+                get_info_by_interface(hf_model_dropdown.value)[1]
+            )
+
+    hf_model_dropdown.change(
+        lambda x: pd.Series(
+        get_info_by_interface(x)[2]
+        ).sample(n = min(len(get_info_by_interface(x)[2]), 30)).map(lambda t2: t2[1]).map(lambda x: x["bytes"]).map(jpg_val_to_img).values.tolist(),
+        hf_model_dropdown,
+        civital_info_gallery
+    )
+    hf_model_dropdown.change(
+        lambda _: (gr.Button("Show Civital Page"), gr.HTML(visible = False)),
+        None,
+        [try_repo_button, civital_iframe_html]
+    )
+
+    civital_info_gallery.select(
+            image_click,
+            [civital_info_gallery, hf_model_dropdown],
+            civital_prompt
+    )
+    gen_button.click(lambda hf_model_name, text_prompt:
+            get_info_by_interface(hf_model_name)[0](text_prompt),
+            [hf_model_dropdown, civital_prompt],
+            hf_image
+    )
+
+    try_repo_button.click(
+        lambda hf_model_name, button: try_repo_act_func(
+            get_info_by_interface(hf_model_name)[1]
+            , button),
+        [hf_model_dropdown, try_repo_button],
+        [civital_iframe_html, try_repo_button]
+    )
+
+demo.launch(show_api = False)