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from __future__ import annotations |
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import gradio as gr |
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import logging |
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logger = logging.getLogger(__name__) |
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logging.basicConfig(level=logging.INFO, |
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format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') |
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import subprocess |
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def runcmd(command): |
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ret = subprocess.run(command,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8",timeout=60) |
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if ret.returncode == 0: |
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print("success:",ret) |
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else: |
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print("error:",ret) |
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runcmd("pip3 install --upgrade clueai") |
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|
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import clueai |
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cl = clueai.Client("", check_api_key=False) |
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|
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''' |
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#luck_t2i_btn_1, #luck_s2i_btn_1, #luck_i2i_btn_1, #luck_ici_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #BED336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #BED336; |
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border-color: #BED336; |
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} |
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|
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#luck_easy_btn_1, #luck_iti_btn_1, #luck_tsi_btn_1, #luck_isi_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #BED336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #BED336; |
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border-color: #BED336; |
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} |
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''' |
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css=''' |
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.container { max-width: 800px; margin: auto; } |
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#gen_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #f44336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #ff9800; |
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border-color: #ff9800; |
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} |
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#t2i_btn_1, #s2i_btn_1, #i2i_btn_1, #ici_btn_1, #easy_btn_1, #iti_btn_1, #tsi_btn_1, #isi_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #f44336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #ff9800; |
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border-color: #ff9800; |
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} |
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#import_t2i_btn_1, #import_s2i_btn_1, #import_i2i_btn_1, #import_ici_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #BED336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #BED336; |
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border-color: #BED336; |
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} |
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|
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#import_easy_btn_1, #import_iti_btn_1, #import_tsi_btn_1, #import_isi_btn_1{ |
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color: #fff; |
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--tw-gradient-from: #BED336; |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); |
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--tw-gradient-to: #BED336; |
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border-color: #BED336; |
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} |
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|
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#record_btn{ |
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} |
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#record_btn > div > button > span { |
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width: 2.375rem; |
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height: 2.375rem; |
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} |
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#record_btn > div > button > span > span { |
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width: 2.375rem; |
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height: 2.375rem; |
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} |
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audio { |
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margin-bottom: 10px; |
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} |
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div#record_btn > .mt-6{ |
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margin-top: 0!important; |
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} |
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div#record_btn > .mt-6 button { |
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font-size: 1em; |
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width: 100%; |
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padding: 20px; |
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height: 60px; |
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} |
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|
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div#txt2img_tab { |
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color: #BED336; |
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} |
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|
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''' |
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|
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default_generate_config = { |
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"do_sample": False, |
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"top_p": 0, |
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"top_k": 50, |
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"max_length": 64, |
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"temperature": 1, |
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"num_beams": 1, |
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"length_penalty": 0.6 |
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} |
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task_styles = [] |
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examples_list = [] |
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task_style_to_task_prefix = {} |
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import csv |
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examples_set = set() |
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def read_examples(input_file): |
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header = True |
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with open(input_file) as finput: |
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csv_input = csv.reader(finput) |
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for line in csv_input: |
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if header: |
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header = False |
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continue |
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task_style, task_prefix, example = line |
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task_styles.append(task_style) |
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task_style_to_task_prefix[task_style] = task_prefix |
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examples_list.append([task_style, example]) |
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examples_set.add((task_style, example)) |
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read_examples("./examples.csv") |
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|
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def preprocess(text, task): |
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if task == "问答": |
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text = text.replace("?", ":").replace("？", ":") |
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text = text + "：" |
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|
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return task_style_to_task_prefix[task] + "\n" + text + "\n答案：" |
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|
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def inference_gen(text, task, do_sample, top_p, top_k, max_token, temperature, beam_size, length_penalty): |
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default_example = (task, text) in examples_set |
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text = preprocess(text, task) |
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generate_config = { |
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"do_sample": do_sample, |
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"top_p": top_p, |
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"top_k": top_k, |
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"max_length": max_token, |
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"temperature": temperature, |
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"num_beams": beam_size, |
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"length_penalty": length_penalty |
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} |
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default_example = default_example and generate_config == default_generate_config |
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try_num = 3 |
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while try_num: |
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try: |
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if default_example: |
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prediction = cl.generate( |
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model_name='clueai-base', |
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prompt=text) |
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else: |
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prediction = cl.generate( |
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model_name='clueai-base', |
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prompt=text, |
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generate_config=generate_config) |
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except Exception as e: |
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logger.error(f"error, {e}") |
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return |
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if prediction.generations[0].text != "含有违规词，不予展示": |
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break |
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try_num -= 1 |
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return prediction.generations[0].text |
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|
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t2i_default_img_path_list = [] |
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import base64, requests |
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from io import BytesIO |
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from PIL import Image |
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def luck_inference_image(text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale): |
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return inference_image(text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale, luck=True) |
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|
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def inference_image(text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale, luck=False): |
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try: |
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res = requests.get(f"https://www.clueai.cn/clueai/hf_text2image?text={text}&negative_prompt={n_text}\ |
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&guidance_scale={guidance_scale}&num_inference_steps={steps}\ |
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&style={style}&shape={shape}&clarity={clarity}&shape_scale={shape_scale}&luck={luck}") |
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except Exception as e: |
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logger.error(f"error, {e}") |
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return |
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json_dict = res.json() |
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file_path_list = [] |
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for i, image in enumerate(json_dict["images"]): |
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image = image.encode('utf-8') |
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binary_data = base64.b64decode(image) |
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img_data = BytesIO(binary_data) |
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img = Image.open(img_data) |
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file_path_list.append(img) |
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return file_path_list |
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image_styles = ['无', '细节大师', '对称美', '虚拟引擎', '空间感', '机械风格', '形状艺术', '治愈', '电影构图', '电影构图（治愈）', '荒芜感', '漫画', '逃离艺术', '斯皮尔伯格', '幻想', '杰作', '壁画', '朦胧', '黑白（3d)', '梵高', '毕加索', '莫奈', '丰子恺', '现代', '欧美'] |
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with gr.Blocks(css=css, title="ClueAI") as demo: |
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|
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gr.Markdown('<h1><center><font color=red style="font-size:50px;">ClueAI全能师</font></center></h1>') |
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with gr.TabItem("文本生成", id='_tab'): |
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with gr.Row(variant="compact").style( equal_height=True): |
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text = gr.Textbox("标题：俄天然气管道泄漏爆炸", |
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label="编辑内容", show_label=False, max_lines=20, |
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placeholder="在这里输入...", |
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) |
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task = gr.Dropdown(label="任务", show_label=True, choices=task_styles, value="标题生成文章") |
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btn = gr.Button("生成",elem_id="gen_btn_1").style(full_width=False) |
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with gr.Accordion("高级操作", open=False): |
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do_sample = gr.Radio([True, False], label="是否采样", value=False) |
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top_p = gr.Slider(0, 1, value=0, step=0.1, label="越大多样性越高, 按照概率采样") |
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top_k = gr.Slider(1, 100, value=50, step=1, label="越大多样性越高，按照top k采样") |
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max_token = gr.Slider(1, 512, value=64, step=1, label="生成的最大长度") |
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temperature = gr.Slider(0,1, value=1, step=0.1, label="temperature, 越小下一个token预测概率越平滑") |
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beam_size = gr.Slider(1, 4, value=1, step=1, label="beam size, 越大解码窗口越广，") |
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length_penalty = gr.Slider(-1, 1, value=0.6, step=0.1, label="大于0鼓励长句子，小于0鼓励短句子") |
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|
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with gr.Row(variant="compact").style( equal_height=True): |
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output_text = gr.Textbox( |
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label="输出", show_label=True, max_lines=50, |
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placeholder="在这里展示结果", |
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) |
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gr.Examples(examples_list, [task, text], label="示例") |
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input_params = [text, task, do_sample, top_p, top_k, max_token, temperature, beam_size, length_penalty] |
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btn.click(inference_gen, inputs=input_params, outputs=output_text) |
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|
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with gr.TabItem("图像生成", id='txt2img_tab'): |
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with gr.Row(variant="compact").style( equal_height=True): |
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text = gr.Textbox("美丽的风景", |
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label="编辑内容", show_label=False, max_lines=2, |
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placeholder="在这里输入你的描述...", |
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) |
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btn = gr.Button("生成图像",elem_id="t2i_btn_1").style(full_width=False) |
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|
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with gr.Row().style( equal_height=True): |
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generate_prompt_btn = gr.Button("手气不错", elem_id="luck_t2i_btn_1") |
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|
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style = gr.Dropdown(label="风格", show_label=True, choices=image_styles, value="无") |
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with gr.Accordion("高级操作", open=False): |
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n_text = gr.Textbox("", |
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label="不想要生成的元素", show_label=True, max_lines=2, |
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placeholder="在这里输入你不需要包含的内容...", |
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) |
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guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="和你的描述匹配程度，越大越匹配") |
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shape = gr.Radio(["1x1", "16x9", "手机壁纸"], label="尺寸", value="1x1") |
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shape_scale = gr.Radio([1, 2, 3], label="对图放大倍数", value=1) |
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steps = gr.Slider(10, 150, value=50, step=1, label="越大质量越好，生成时间越长") |
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clarity = gr.Radio(["标清", "高清"], label="清晰度", value="标清") |
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|
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gr.Examples(["秋日的晚霞", "星空", "室内装修", "婚礼鲜花"], text, label="示例") |
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|
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t2i_gallery = gr.Gallery( |
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t2i_default_img_path_list, |
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label="生成图像", |
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show_label=False).style( |
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grid=[2], height="auto" |
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) |
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|
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input_params = [text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale] |
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generate_prompt_btn.click(luck_inference_image, inputs=input_params, outputs=[t2i_gallery]) |
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text.submit(inference_image, inputs=input_params, outputs=t2i_gallery) |
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btn.click(inference_image, inputs=input_params, outputs=t2i_gallery) |
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|
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gr.Markdown(""" |
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<center><a href="https://clustrmaps.com/site/1bsr8" title="Visit tracker"><img src="//www.clustrmaps.com/map_v2.png?d=OBV_rLBLpgrXBPyk_STupM-rByau5s53eEWDitHdn_Q&cl=ffffff" /></a></center> |
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""") |
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|
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demo.launch() |
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