API相关 | 关于Gradio快速构建人工智能模型实现界面，你想知道的都在这里

pip install gradio

pip install gradio==3.50.2 -i  https://pypi.python.org/pypi

import gradio as gr # 定义一个处理多种 Gradio 输入组件值的函数def greet(textbox, dropdown, radio, checkbox, slider, image, audio, video, file, number, dataframe, color):    # 创建一个包含用户选择的字符串    output_text = f"您选择了：{textbox}，{dropdown}，{radio}，{checkbox}，滑块值：{slider}，数字：{number}，颜色：{color}"    # 创建一个包含用户选择的 Markdown 字符串（加粗文本）    output_markdown = f"**您选择了**：{textbox}，{dropdown}，{radio}，{checkbox}，滑块值：{slider}，数字：{number}，颜色：{color}"    # 创建一个包含用户输入选项的 JSON 字典    output_json = {        "textbox": textbox,        "dropdown": dropdown,        "radio": radio,        "checkbox": checkbox,        "slider": slider,        "number": number,        "color": color    }     # 保存图像（如果提供了输入图像）    output_image = None    if image is not None:        output_image = "output_image.png"        image.save(output_image)     # 保存音频（如果提供了输入音频）    output_audio = None    if audio is not None:        output_audio = "output_audio.wav"        with open(output_audio, "wb") as f:            f.write(audio)     # 保存视频（如果提供了输入视频）    output_video = None    if video is not None:        output_video = "output_video.mp4"        with open(output_video, "wb") as f:            f.write(video)     # 保存文件（如果提供了输入文件）    output_file = None    if file:        output_file = 'example_output.txt'        with open(output_file, 'w') as f:            f.write(output_text)     # 处理数据框（如果提供了输入数据框）    output_dataframe = None    if dataframe is not None:        output_dataframe = dataframe.to_html(index=False)     # 返回颜色    output_color = "您选择的颜色是：{color}"     # 返回处理后的各种输出组件值    return None, output_text, None, output_markdown, output_image, output_audio, output_json, output_file, output_video, output_dataframe, output_color # 创建一个 Gradio Interface 实例，包含多个输入和输出组件iface2 = gr.Interface(    greet,    [        gr.components.Textbox(lines=2, placeholder="请输入文本…"),        gr.components.Dropdown(choices=["选项A", "选项B", "选项C"]),        gr.components.Radio(choices=["选项1", "选项2", "选项3"]),        gr.components.Checkbox(label="选择此选项"),        gr.components.Slider(minimum=10, maximum=90),        gr.components.Image(shape=(100, 100)),        gr.components.Audio(),        gr.components.Video(),        gr.components.File(),        gr.components.Number(),        gr.components.Dataframe(headers=["列A", "列B", "列C"]),        gr.components.ColorPicker(),  # 添加颜色选择器输入组件    ],    outputs=[        gr.components.Textbox(),        gr.components.Textbox(),        gr.components.Textbox(),        gr.components.Markdown(),        gr.components.Image(),        gr.components.Audio(),        gr.components.Json(),        gr.components.File(),        gr.components.Video(),        gr.components.HTML(),        gr.components.Textbox(),  # 添加新的输出组件    ],    title="Gradio 示例",    description="这是一个 Gradio 示例应用。",    examples=[        [            "您好",            "选项A",            "选项1",            True,            25,            "path/to/image.png",            "path/to/audio.wav",            "path/to/video.mp4",            "path/to/file.txt",            42,            [                ["值A1", "值B1", "值C1"],                ["值A2", "值B2", "值C2"],            ],            "#FF0000",  # 颜色选择器示例值        ]    ],).launch()

interface.launch(share=True)

git clone https://huggingface.co/spaces/94insane/tts-fastspeech2-myvoice-ko

import gradio as gr def greet(name):    return "Hello " + name + "!!" iface = gr.Interface(fn=greet, inputs="text", outputs="text")iface.launch()

git add app.pygit commit -m "Add application file"git push

git config --global user.email "注册huggingface的邮箱"git config --global user.name "注册huggingface的名称"

import gradio as grimport torchimport requestsfrom torchvision import transforms model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()response = requests.get("https://git.io/JJkYN")labels = response.text.split("\n") def predict(inp):  inp = transforms.ToTensor()(inp).unsqueeze(0)  with torch.no_grad():    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}      return confidences demo = gr.Interface(fn=predict,              inputs=gr.inputs.Image(type="pil"),             outputs=gr.outputs.Label(num_top_classes=3),             examples=[["cheetah.jpg"]],             )             demo.launch()

import numpy as npimport gradio as gr def sepia(input_img):    sepia_filter = np.array([        [0.393, 0.769, 0.189],         [0.349, 0.686, 0.168],         [0.272, 0.534, 0.131]    ])    sepia_img = input_img.dot(sepia_filter.T)    sepia_img /= sepia_img.max()    return sepia_img demo = gr.Interface(sepia, gr.Image(), "image")demo.launch()

import gradio as grfrom transformers import pipeline pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") def predict(image):  predictions = pipeline(image)  return {p["label"]: p["score"] for p in predictions} gr.Interface(    predict,    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),    outputs=gr.outputs.Label(num_top_classes=2),    title="Hot Dog? Or Not?",    allow_flagging="manual").launch()

import tempfileimport gradio as gr # 借用第三方语音合成库from neon_tts_plugin_coqui import CoquiTTS # 把模型中的所有语言显示出来LANGUAGES = list(CoquiTTS.langs.keys())coquiTTS = CoquiTTS() # def tts(text: str, language: str):    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:        coquiTTS.get_tts(text, fp, speaker = {"language" : language})        return fp.name # 前端实现：#### 一个文本框（框上标着“Input”，框内调用的是tts库中的值，显示三行）#### 一个语音（框上标着“Language”，在可选择的语言中，默认英文）inputs = [gr.Textbox(label="Input", value=CoquiTTS.langs["en"]["sentence"], max_lines=3),             gr.Radio(label="Language", choices=LANGUAGES, value="en")] # 前端输出：一个语音#### 一个语音（框上标着“Output”）outputs = gr.Audio(label="Output") # demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs) demo.launch()

# @ 2023.10.23# @ Elenaimport sys, osimport loggingimport refrom scipy.io.wavfile import write import torchimport argparseimport utilsfrom models import SynthesizerTrnfrom text.symbols import symbolsfrom text import text_to_sequenceimport gradio as grimport webbrowserimport numpy as np net_g = None if sys.platform == "darwin" and torch.backends.mps.is_available():    device = "mps"    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"else:    device = "cuda" model = './G_1000.pth'config = './config.json'share = Truehps = utils.get_hparams_from_file(config)if "use_mel_posterior_encoder" in hps.model.keys() and hps.model.use_mel_posterior_encoder == True:        print("Using mel posterior encoder for VITS2")        posterior_channels = 80  # vits2        hps.data.use_mel_posterior_encoder = Trueelse:    print("Using lin posterior encoder for VITS1")    posterior_channels = hps.data.filter_length // 2 + 1    hps.data.use_mel_posterior_encoder = Falsedevice = (    "cuda:1"    if torch.cuda.is_available()    else (        "mps"        if sys.platform == "darwin" and torch.backends.mps.is_available()        else "cpu"    ))net_g = SynthesizerTrn(    len(symbols),    posterior_channels,    hps.train.segment_size // hps.data.hop_length,    n_speakers=hps.data.n_speakers, #- >0 for multi speaker    **hps.model).to(device)_ = net_g.eval() _ = utils.load_checkpoint(model, net_g, None) speakers = hps.data.n_speakerslanguages = ["KO"] def intersperse(lst, item):    result = [item] * (len(lst) * 2 + 1)    result[1::2] = lst    return result def get_text(text, hps):    text_norm = text_to_sequence(text, hps.data.text_cleaners)    if hps.data.add_blank:        text_norm = intersperse(text_norm, 0)    text_norm = torch.LongTensor(text_norm)    return text_norm def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):    global net_g    fltstr = re.sub(r"[\[\]\(\)\{\}]", "", text)    stn_tst = get_text(fltstr, hps)     speed = 1    output_dir = 'output'    sid = 0    with torch.no_grad():        x_tst = stn_tst.to(device).unsqueeze(0)        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1 / speed)[0][                0, 0].data.cpu().float().numpy()     return audio def tts_fn(    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):    slices = text.split("|")    audio_list = []    with torch.no_grad():        for slice in slices:            audio = infer(                slice,                sdp_ratio=sdp_ratio,                noise_scale=noise_scale,                noise_scale_w=noise_scale_w,                length_scale=length_scale,                sid=speaker,             )            audio_list.append(audio)            silence = np.zeros(hps.data.sampling_rate)              audio_list.append(silence)     audio_concat = np.concatenate(audio_list)    return "Success", (hps.data.sampling_rate, audio_concat) if __name__ == "__main__":    with gr.Blocks() as app:        with gr.Row():            with gr.Column():                text = gr.TextArea(                    label="Text",                    placeholder="Input Text Here",                    value="TTS是语音合成",                )                 speaker = gr.Slider(                    minimum=0, maximum=speakers-1, value=0, step=1, label="0"                )                sdp_ratio = gr.Slider(                    minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"                )                noise_scale = gr.Slider(                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"                )                noise_scale_w = gr.Slider(                    minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"                )                length_scale = gr.Slider(                    minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"                )                language = gr.Dropdown(                    choices=languages, value=languages[0], label="Language"                )                btn = gr.Button("Generate!", variant="primary")            with gr.Column():                text_output = gr.Textbox(label="Message")                audio_output = gr.Audio(label="Output Audio")         btn.click(            tts_fn,            inputs=[                text,                speaker,                sdp_ratio,                noise_scale,                noise_scale_w,                length_scale,            ],            outputs=[text_output, audio_output],        )     webbrowser.open("http://127.0.0.1:7878")    app.launch(share=True)        

import gradio as grfrom transformers import pipeline generator = pipeline('text-generation', model = 'gpt2') def generate_text(text_prompt):  response = generator(text_prompt, max_length = 30, num_return_sequences=5)  return response[0]['generated_text'] textbox = gr.Textbox() demo = gr.Interface(generate_text, textbox, textbox) demo.launch()

import gradio as gr def greet(a, b):    return f"Hello {a} ", f"Greet {b}" demo = gr.Interface(    fn=greet,    inputs=[gr.Text(label='input_text_a'), gr.Text(label='input_text_b')],    outputs=[gr.Text(label='output_text_a'), gr.Text(label='output_text_b')]) demo.launch(auth=("user_name", "pwd"),   # 设置这个demo需要输入的认证信息           )

# V1.0import gradio as grfrom transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, StoppingCriteriaListimport osimport torch  title = " 大语言模型生成 Demo" bad_words = [  '...',  '....',  'http'] description = "模型参数 12.8B "examples = [    ["问题: 今天吃什么? \n回答:"],    ["问题: 天空为什么是蓝色? \n回答:"],    ["问题: 怎么缓解压力? \n回答:"]]  model_name = 'Polyglot-12.8B' model = AutoModelForCausalLM.from_pretrained(    model_name,    torch_dtype=torch.float16,    low_cpu_mem_usage=True,).to(device=f"cuda", non_blocking=True)model.eval() pipe = pipeline(    'text-generation',     model=model,    tokenizer=model_name,    device=0)   chat_history = []for i in range(3):    chat_history.append({"###问题":"","###回答":""})  def ask(text, context='', is_input_full=False):    history = str(chat_history[-1]) + '\n' + str(chat_history[-2]) + '\n' + str(chat_history[-3]) + '\n'    ans = pipe(        f"### 以前对话内容: {history}\n" +         f"### 问题: {text}\n\n### 上下文: {context}\n\n### 回答:" if context else f"### 提问: {text}\n\n### 回答:",         do_sample=True,         max_new_tokens=512,        temperature=0.7,        top_p=0.9,        return_full_text=False,        eos_token_id=2,    )    return ans[0]['generated_text'] def API(input_text):    result = ask(input_text)    chat_history.append({"###问题":str(input_text),"###回答":str(result)})    return result,input_text,chat_history[-3:] ########################################################################################################### Interface building##########################################################################################################iface = gr.Interface(  fn=API,   inputs='text',  outputs=[        gr.Textbox(label="@ LLM"),        gr.Textbox(label="@ 输入文本"),             gr.Textbox(label="@ 历史记录"),      ],  examples=examples)  iface.launch(share=True)

# -*- coding: utf-8 -*-# @ 2023.12.04# @ Elena# 添加超参数调整，输入为4个，输出为俩个import gradio as grimport webbrowserfrom transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, StoppingCriteriaListimport osimport torch  title = "LLM Demo"  description = "模型 12.8B"article = "<p style='text-align: center'><a href='' target='_blank'>模型 </a></p>"   ###### 加载预训练模型model_name = '/model-12.8B'  model = AutoModelForCausalLM.from_pretrained(    model_name,    torch_dtype=torch.float16,    low_cpu_mem_usage=True,).to(device=f"cuda", non_blocking=True)model.eval()  #####  pipeline pipe = pipeline(    'text-generation',     model=model,    tokenizer=model_name,    device=6)   chat_history = []for i in range(3):    chat_history.append({"###Q":"","###A":""})  def ask(text,max_new_tokens,temperature,top_p,context='', is_input_full=False):    history = str(chat_history[-1]) + '\n' + str(chat_history[-2]) + '\n' + str(chat_history[-3]) + '\n'    ans = pipe(        f"### history: {history}\n" +         f"### 提问: {text}\n\n### 上下文: {context}\n\n### 回答:" if context else f"### 提问: {text}\n\n### 回答:",         do_sample=True,         max_new_tokens=512,        temperature=0.7,        top_p=0.9,        return_full_text=False,        eos_token_id=2,    )    return ans[0]['generated_text'] def API(text,max_new_tokens,temperature,top_p):        result = ask(        text,        max_new_tokens=max_new_tokens,        temperature=temperature,        top_p=top_p,)    chat_history.append({"###提问":str(text),"###回答":str(result)})    chat_3_history=chat_history[-3:]    #print(chat_3_history)    return result,chat_3_history ########################################################################################################### Block building# ######################################################################################################### if __name__ == "__main__":    with gr.Blocks() as app:        with gr.Row():            with gr.Column():                text = gr.TextArea(                    label="Text",                    placeholder="在这里输入",                    value="LLM 代码如何修改？",                )                 max_new_tokens = gr.Slider(                    minimum=0, maximum=1028,value=512, label="max_new_tokens"                )                temperature = gr.Slider(                    minimum=0.1, maximum=1, value=0.7, step=0.1, label="temperature"                )                top_p = gr.Slider(                    minimum=0.1, maximum=1, value=0.9, step=0.1, label="top_p"                )                 btn = gr.Button("Text Generate!", variant="primary")            with gr.Column():                result = gr.Textbox(label="@ LLM")                chat_3_history = gr.Textbox(label="@ 历史记录")         btn.click(            API,            inputs=[                text,                max_new_tokens,                temperature,                top_p,            ],            outputs=[result, chat_3_history],            #examples=examples        )     webbrowser.open("http://127.0.0.1:7878")    app.launch(share=True) 

from transformers import AutoModel, AutoTokenizerimport gradio as grimport mdtex2html tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()model = model.eval() """Override Chatbot.postprocess"""  def postprocess(self, y):    if y is None:        return []    for i, (message, response) in enumerate(y):        y[i] = (            None if message is None else mdtex2html.convert((message)),            None if response is None else mdtex2html.convert(response),        )    return y  gr.Chatbot.postprocess = postprocess  def parse_text(text):    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""    lines = text.split("\n")    lines = [line for line in lines if line != ""]    count = 0    for i, line in enumerate(lines):        if "```" in line:            count += 1            items = line.split('`')            if count % 2 == 1:                lines[i] = f'<pre><code class="language-{items[-1]}">'            else:                lines[i] = f'<br></code></pre>'        else:            if i > 0:                if count % 2 == 1:                    line = line.replace("`", "\`")                    line = line.replace("<", "&lt;")                    line = line.replace(">", "&gt;")                    line = line.replace(" ", "&nbsp;")                    line = line.replace("*", "&ast;")                    line = line.replace("_", "&lowbar;")                    line = line.replace("-", "&#45;")                    line = line.replace(".", "&#46;")                    line = line.replace("!", "&#33;")                    line = line.replace("(", "&#40;")                    line = line.replace(")", "&#41;")                    line = line.replace("$", "&#36;")                lines[i] = "<br>"+line    text = "".join(lines)    return text  def predict(input, chatbot, max_length, top_p, temperature, history):    chatbot.append((parse_text(input), ""))    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,                                               temperature=temperature):        chatbot[-1] = (parse_text(input), parse_text(response))                yield chatbot, history  def reset_user_input():    return gr.update(value='')  def reset_state():    return [], []  with gr.Blocks() as demo:    gr.HTML("""<h1 align="center">ChatGLM</h1>""")     chatbot = gr.Chatbot()    with gr.Row():        with gr.Column(scale=4):            with gr.Column(scale=12):                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(                    container=False)            with gr.Column(min_width=32, scale=1):                submitBtn = gr.Button("Submit", variant="primary")        with gr.Column(scale=1):            emptyBtn = gr.Button("Clear History")            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)     history = gr.State([])     submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],                    show_progress=True)    submitBtn.click(reset_user_input, [], [user_input])     emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) demo.queue().launch(share=False, inbrowser=True)

import randomimport gradio as gr def random_response(message, history):    return random.choice(["Yes", "No"]) demo = gr.ChatInterface(random_response) demo.launch()

import gradio as grimport openaiimport os  # Load openai keyopenai.api_key = os.getenv('OPENAI_KEY') # Initialize message history arraymessage_history = []initial_message = "Please write your prompt here and press 'enter'" # Create function to process prompt and append previous prompts as "context"def predict_prompt(input):     global message_history    message_history.append({"role": "user", "content": input})    create_prompt = openai.ChatCompletion.create(        model = "gpt-3.5-turbo",        messages = message_history    )     reply_prompt = create_prompt.choices[0].message.content    # print(reply_prompt)     # Append answer as assistant reply to keep history of prompts    message_history.append({"role": "assistant", "content": reply_prompt})     response = [(message_history[i]["content"], message_history[i+1]["content"]) for i in range(0, len(message_history) -1, 2)]     return response # Create UI using gradiowith gr.Blocks(theme='abidlabs/dracula_test') as chatblock:     gr.Markdown("<h1><center>Welcome to my personal AI assistant (powered by OpenAI)</center></h1>")     Chatbot = gr.Chatbot()    with gr.Row():        txt = gr.Textbox(            show_label=False,             placeholder = initial_message).style(container=False)        state = gr.State()        txt.submit(predict_prompt, txt, Chatbot)        txt.submit(None, None, txt, _js="() => {''}") chatblock.launch()

# 导入huggingface_hubfrom huggingface_hub import hf_hub_download config_path=hf_hub_download(repo_id="94insane/tts-fastspeech-mydata",                             filename="synthesize.py", )

import gradio as gr gr.Interface.load("models/facebook/fastspeech2-en-ljspeech").launch()

chmod +x 文件名

cd /jf-training-home/src/elena sudo chmod +x /opt/conda/lib/python3.8/site-packages/gradio/frpc_linux_amd64_v0.2 

pip install gradio==3.38.0pip install gradio_client 0.2.10chmod +x /opt/conda/lib/python3.8/site-packages/gradio/frpc_linux_amd64_v0.2

pip install MarkupSafe==1.1.1

import osos.system('pip install gradio==2.3.0a0')os.system('pip freeze')import gradio as grfrom subprocess import call if (not os.path.exists("korean.py")):  os.system("wget https://raw.githubusercontent.com/TensorSpeech/TensorFlowTTS/master/tensorflow_tts/utils/korean.py -O korean.py") import korean def run_cmd(command):    try:        print(command)        call(command)    except KeyboardInterrupt:        print("Process interrupted")        sys.exit(1) def inference(text):  cmd = ['tts', '--text', "".join(korean.tokenize(text)), '--model_path', 'vits-kss-checkpoint_90000.pth', '--config_path', 'vits-kss-config.json']  run_cmd(cmd)  return 'tts_output.wav' if (not os.path.exists("vits-kss-checkpoint_90000.pth")):  os.system("wget -q https://huggingface.co/youngs3/coqui-vits-ko/resolve/main/vits-kss-checkpoint_90000.pth -O vits-kss-checkpoint_90000.pth")  os.system("wget -q https://huggingface.co/youngs3/coqui-vits-ko/resolve/main/vits-kss-config.json -O vits-kss-config.json") inputs = gr.inputs.Textbox(lines=5, label="Input Text")outputs =  gr.outputs.Audio(type="file",label="Output Audio")title = "Korean Language coqui-ai-TTS"description = "Gradio demo for coqui-ai-TTS, using a VITS model trained on the kss dataset. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."article = "<p style='text-align: center'><a href='https://tts.readthedocs.io/en/latest/'>TTS is a library for advanced Text-to-Speech generation</a> | <a href='https://github.com/coqui-ai/TTS'>Github Repo</a></p>"examples = [ ["공부가 가장 쉬웠어요!"]]gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()