郑州建设网站制作公司,网站开发原创动漫,微信公众平台申请注册入口,南昌企业网站建设----------------------- #x1f388;API 相关直达 #x1f388;-------------------------- #x1f680;Gradio: 实用篇 | 关于Gradio快速构建人工智能模型实现界面#xff0c;你想知道的都在这里-CSDN博客 #x1f680;Streamlit :实用篇 | 一文快速构建人工智能前端展… ----------------------- API 相关直达 -------------------------- Gradio: 实用篇 | 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-CSDN博客 Streamlit :实用篇 | 一文快速构建人工智能前端展示streamlit应用-CSDN博客 Flask: 实用篇 | 一文学会人工智能中API的Flask编写(内含模板)-CSDN博客 Streamlit是一个用于机器学习、数据可视化的 Python 框架它能几行代码就构建出一个精美的在线 app 应用。相比于Gradio能展示更多的功能~
目录
1.Streamlit的安装
2.Streamlit的语法
2.1.基本语法
2.2.进阶语法
2.2.1.图片语音视频
2.2.2.进程提示
2.3.高级语法
2.3.1.st.cache_data
2.3.2.st.cache_resource
3.创建一个简单的app
实时读取数据并作图
4.人工智能深度学习项目Streamlit实例
4.1.实例1文本生成
4.1.1ChatGLM的交互
4.1.2.OpenAI的交互
4.2.图像类
4.2.1.图像分类
4.2.2.图片生成
4.3.语音类
4.3.1.语音合成 4.3.2.语音转文本
参考文献 官网Get started - Streamlit Docs 1.Streamlit的安装
# 安装
pip install streamlit
pip install streamlit-chat# 测试
streamlit hello 会出现一些案例 2.Streamlit的语法
2.1.基本语法
import streamlit as st
最常用的几种
标题st.title() : st.title(标题)写入st.write(): st.write(Hello world )文本st.text():单行文本多行文本框st.text_area():st.text_area(文本框,valuekeyNone)滑动条st.slider():st.slider(““)按钮st.button():st.button(“按钮“)输入文本st.text_input():st.text_input(“请求用户输入“)单选框组件st.radio() 2.2.进阶语法
2.2.1.图片语音视频
都可以输入向量值比特值加载文件文件路径
st.image()st.audio()st.video()
2.2.2.进程提示
st.progress() 显示进度st.spinner()显示执行状态st.error()显示错误信息st.warning - 显示警告信息 2.3.高级语法
2.3.1.st.cache_data
当使用 Streamlit 的缓存注释标记函数时它会告诉 Streamlit 每当调用函数时它应该检查两件事
用于函数调用的输入参数函数内部的代码
2.3.2.st.cache_resource
用于缓存返回全局资源例如数据库连接、ML 模型的函数的装饰器。
缓存的对象在所有用户、会话和重新运行之间共享。他们 必须是线程安全的因为它们可以从多个线程访问 同时。如果线程安全是一个问题请考虑改用 st.session_state 来存储每个会话的资源。
默认情况下cache_resource函数的所有参数都必须是可哈希的。 名称以 _ 开头的任何参数都不会进行哈希处理。
3.创建一个简单的app
实时读取数据并作图
import streamlit as st
import pandas as pd
import numpy as npst.title(Uber pickups in NYC)DATA_COLUMN data/time
DATA_URL (https://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz)# 增加缓存
st.cache_data
# 下载数据函数
def load_data(nrows):# 读取csv文件data pd.rea_csv(data_url,nrowsnrows)# 转换小写字母lowercase lambda x:tr(x).lower()# 将数据重命名 data.rename(lowercase,axiscolumns,inplaceTrue)# 将数据以panda的数据列的形式展示出来data[DATA_COLUMN] pd.to_datatime(data[DATA_COLUMN])# 返回最终数据return data# 直接打印文本信息
data_load_state st.text(正在下载)
# 下载一万条数据中的数据
data load_data(10000)
# 最后输出文本显示
data_load_state.text(完成(using st.cache_data))# 检查原始数据
if st.checkbox(Show raw data):st.subheader(Raw data)st.write(data)# 绘制直方图
# 添加一个子标题
st.subheader(Number of pickups by hour)# 使用numpy生成一个直方图按小时排列
hist_values np.histogram(data[DATE_COLUMN].dt.hour, bins24, range(0,24))[0]
# 使用Streamlit 的 st.bar_chart 方法来绘制直方图
st.bar_chart(hist_values)# 使用滑动块筛选结果
hour_to_filter st.slider(hour, 0, 23, 17)
# 实时更新
filtered_data data[data[DATE_COLUMN].dt.hour hour_to_filter]# 为地图添加一个副标题
st.subheader(Map of all pickups at %s:00 % hour_to_filter)
# 使用st.map()函数绘制数据
st.map(filtered_data)运行
streamlit run demo.py 4.人工智能深度学习项目Streamlit实例
4.1.实例1文本生成
4.1.1ChatGLM的交互
from transformers import AutoModel, AutoTokenizer
import streamlit as st
from streamlit_chat import messagest.set_page_config(page_titleChatGLM-6b 演示,page_icon:robot:
)st.cache_resource
def get_model():tokenizer AutoTokenizer.from_pretrained(THUDM/chatglm-6b, trust_remote_codeTrue)model AutoModel.from_pretrained(THUDM/chatglm-6b, trust_remote_codeTrue).half().cuda()model model.eval()return tokenizer, modelMAX_TURNS 20
MAX_BOXES MAX_TURNS * 2def predict(input, max_length, top_p, temperature, historyNone):tokenizer, model get_model()if history is None:history []with container:if len(history) 0:if len(history)MAX_BOXES:history history[-MAX_TURNS:]for i, (query, response) in enumerate(history):message(query, avatar_stylebig-smile, keystr(i) _user)message(response, avatar_stylebottts, keystr(i))message(input, avatar_stylebig-smile, keystr(len(history)) _user)st.write(AI正在回复:)with st.empty():for response, history in model.stream_chat(tokenizer, input, history, max_lengthmax_length, top_ptop_p,temperaturetemperature):query, response history[-1]st.write(response)return historycontainer st.container()# create a prompt text for the text generation
prompt_text st.text_area(label用户命令输入,height 100,placeholder请在这儿输入您的命令)max_length st.sidebar.slider(max_length, 0, 4096, 2048, step1
)
top_p st.sidebar.slider(top_p, 0.0, 1.0, 0.6, step0.01
)
temperature st.sidebar.slider(temperature, 0.0, 1.0, 0.95, step0.01
)if state not in st.session_state:st.session_state[state] []if st.button(发送, keypredict):with st.spinner(AI正在思考请稍等........):# text generationst.session_state[state] predict(prompt_text, max_length, top_p, temperature, st.session_state[state])
4.1.2.OpenAI的交互
from openai import OpenAI
import streamlit as stwith st.sidebar:openai_api_key st.text_input(OpenAI API Key, keychatbot_api_key, typepassword)[Get an OpenAI API key](https://platform.openai.com/account/api-keys)[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)[](https://codespaces.new/streamlit/llm-examples?quickstart1)st.title( Chatbot)
st.caption( A streamlit chatbot powered by OpenAI LLM)
if messages not in st.session_state:st.session_state[messages] [{role: assistant, content: How can I help you?}]for msg in st.session_state.messages:st.chat_message(msg[role]).write(msg[content])if prompt : st.chat_input():if not openai_api_key:st.info(Please add your OpenAI API key to continue.)st.stop()client OpenAI(api_keyopenai_api_key)st.session_state.messages.append({role: user, content: prompt})st.chat_message(user).write(prompt)response client.chat.completions.create(modelgpt-3.5-turbo, messagesst.session_state.messages)msg response.choices[0].message.contentst.session_state.messages.append({role: assistant, content: msg})st.chat_message(assistant).write(msg)
4.2.图像类
4.2.1.图像分类
import streamlit as stst.markdown(h1 stylecolor:black;Vgg 19 Image classification model/h1, unsafe_allow_htmlTrue)
st.markdown(h2 stylecolor:gray;The image classification model classifies image into following categories:/h2, unsafe_allow_htmlTrue)
st.markdown(h3 stylecolor:gray; street, buildings, forest, sea, mountain, glacier/h3, unsafe_allow_htmlTrue)# 背景图片background image to streamlitst.cache(allow_output_mutationTrue)
# 以base64的方式传输文件
def get_base64_of_bin_file(bin_file):with open(bin_file, rb) as f:data f.read()return base64.b64encode(data).decode()
#设置背景图片,颜色等
def set_png_as_page_bg(png_file):bin_str get_base64_of_bin_file(png_file) page_bg_img style.stApp {background-image: url(data:image/png;base64,%s);background-size: cover;background-repeat: no-repeat;background-attachment: scroll; # doesnt work}/style % bin_strst.markdown(page_bg_img, unsafe_allow_htmlTrue)returnset_png_as_page_bg(/content/background.webp)# 上传png/jpg的照片
upload st.file_uploader(Insert image for classification, type[png,jpg])
c1, c2 st.columns(2)
if upload is not None:im Image.open(upload)img np.asarray(im)image cv2.resize(img,(224, 224))img preprocess_input(image)img np.expand_dims(img, 0)c1.header(Input Image)c1.image(im)c1.write(img.shape)# 下载预训练模型# 输入尺寸input_shape (224, 224, 3)# 定义优化器optim_1 Adam(learning_rate0.0001)# 分类数n_classes6# 定义模型vgg_model model(input_shape, n_classes, optim_1, fine_tune2)# 下载权重vgg_model.load_weights(/content/drive/MyDrive/vgg/tune_model19.weights.best.hdf5)#预测vgg_preds vgg_model.predict(img)vgg_pred_classes np.argmax(vgg_preds, axis1)c2.header(Output)c2.subheader(Predicted class :)c2.write(classes[vgg_pred_classes[0]] )4.2.2.图片生成
import streamlit as st
from dotenv import load_dotenv
import os
import openai
from diffusers import StableDiffusionPipeline
import torchload_dotenv()
openai.api_key os.getenv(OPENAI_API_KEY)#function to generate AI based images using OpenAI Dall-E
def generate_images_using_openai(text):response openai.Image.create(prompt text, n1, size512x512)image_url response[data][0][url]return image_url#function to generate AI based images using Huggingface Diffusers
def generate_images_using_huggingface_diffusers(text):pipe StableDiffusionPipeline.from_pretrained(runwayml/stable-diffusion-v1-5, torch_dtypetorch.float16)pipe pipe.to(cuda)prompt textimage pipe(prompt).images[0] return image#Streamlit Code
choice st.sidebar.selectbox(Select your choice, [Home, DALL-E, Huggingface Diffusers])if choice Home:st.title(AI Image Generation App)with st.expander(About the App):st.write(This is a simple image generation app that uses AI to generates images from text prompt.)elif choice DALL-E:st.subheader(Image generation using Open AIs DALL-E)input_prompt st.text_input(Enter your text prompt)if input_prompt is not None:if st.button(Generate Image):image_url generate_images_using_openai(input_prompt)st.image(image_url, captionGenerated by DALL-E)elif choice Huggingface Diffusers:st.subheader(Image generation using Huggingface Diffusers)input_prompt st.text_input(Enter your text prompt)if input_prompt is not None:if st.button(Generate Image):image_output generate_images_using_huggingface_diffusers(input_prompt)st.info(Generating image.....)st.success(Image Generated Successfully)st.image(image_output, captionGenerated by Huggingface Diffusers)
4.3.语音类
4.3.1.语音合成
import torch
import streamlit as st
# 这里使用coqui-tts,直接pip install tts就可以
from TTS.api import TTS
import tempfile
import osdevice cuda if torch.cuda.is_available() else cpu
# 模型选择
model_name tts_models/en/jenny/jenny
tts TTS(model_name).to(device)st.title(Coqui TTS)# 输入文本
text_to_speak st.text_area(Entire article text here:, )# 点击按钮监听
if st.button(Listen):if text_to_speak:# temp path needed for audio to listen to# 定义合成语音文件名称temp_audio_path ./temp_audio.wav# 使用tts库中的tts_to_file函数tts.tts_to_file(texttext_to_speak, file_pathtemp_audio_path)#输出语音st.audio(temp_audio_path, formataudio/wav)os.unlink(temp_audio_path)4.3.2.语音转文本
import logging
import logging.handlers
import queue
import threading
import time
import urllib.request
import os
from collections import deque
from pathlib import Path
from typing import Listimport av
import numpy as np
import pydub
import streamlit as st
from twilio.rest import Clientfrom streamlit_webrtc import WebRtcMode, webrtc_streamerHERE Path(__file__).parentlogger logging.getLogger(__name__)# This code is based on https://github.com/streamlit/demo-self-driving/blob/230245391f2dda0cb464008195a470751c01770b/streamlit_app.py#L48 # noqa: E501
def download_file(url, download_to: Path, expected_sizeNone):# Dont download the file twice.# (If possible, verify the download using the file length.)if download_to.exists():if expected_size:if download_to.stat().st_size expected_size:returnelse:st.info(f{url} is already downloaded.)if not st.button(Download again?):returndownload_to.parent.mkdir(parentsTrue, exist_okTrue)# These are handles to two visual elements to animate.weights_warning, progress_bar None, Nonetry:weights_warning st.warning(Downloading %s... % url)progress_bar st.progress(0)with open(download_to, wb) as output_file:with urllib.request.urlopen(url) as response:length int(response.info()[Content-Length])counter 0.0MEGABYTES 2.0 ** 20.0while True:data response.read(8192)if not data:breakcounter len(data)output_file.write(data)# We perform animation by overwriting the elements.weights_warning.warning(Downloading %s... (%6.2f/%6.2f MB)% (url, counter / MEGABYTES, length / MEGABYTES))progress_bar.progress(min(counter / length, 1.0))# Finally, we remove these visual elements by calling .empty().finally:if weights_warning is not None:weights_warning.empty()if progress_bar is not None:progress_bar.empty()# This code is based on https://github.com/whitphx/streamlit-webrtc/blob/c1fe3c783c9e8042ce0c95d789e833233fd82e74/sample_utils/turn.py
st.cache_data # type: ignore
def get_ice_servers():Use Twilios TURN server because Streamlit Community Cloud has changedits infrastructure and WebRTC connection cannot be established without TURN server now. # noqa: E501We considered Open Relay Project (https://www.metered.ca/tools/openrelay/) too,but it is not stable and hardly works as some people reported like https://github.com/aiortc/aiortc/issues/832#issuecomment-1482420656 # noqa: E501See https://github.com/whitphx/streamlit-webrtc/issues/1213# Ref: https://www.twilio.com/docs/stun-turn/apitry:account_sid os.environ[TWILIO_ACCOUNT_SID]auth_token os.environ[TWILIO_AUTH_TOKEN]except KeyError:logger.warning(Twilio credentials are not set. Fallback to a free STUN server from Google. # noqa: E501)return [{urls: [stun:stun.l.google.com:19302]}]client Client(account_sid, auth_token)token client.tokens.create()return token.ice_serversdef main():st.header(Real Time Speech-to-Text)st.markdown(
This demo app is using [DeepSpeech](https://github.com/mozilla/DeepSpeech),
an open speech-to-text engine.A pre-trained model released with
[v0.9.3](https://github.com/mozilla/DeepSpeech/releases/tag/v0.9.3),
trained on American English is being served.
)# https://github.com/mozilla/DeepSpeech/releases/tag/v0.9.3MODEL_URL https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.pbmm # noqaLANG_MODEL_URL https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.scorer # noqaMODEL_LOCAL_PATH HERE / models/deepspeech-0.9.3-models.pbmmLANG_MODEL_LOCAL_PATH HERE / models/deepspeech-0.9.3-models.scorerdownload_file(MODEL_URL, MODEL_LOCAL_PATH, expected_size188915987)download_file(LANG_MODEL_URL, LANG_MODEL_LOCAL_PATH, expected_size953363776)lm_alpha 0.931289039105002lm_beta 1.1834137581510284beam 100sound_only_page Sound only (sendonly)with_video_page With video (sendrecv)app_mode st.selectbox(Choose the app mode, [sound_only_page, with_video_page])if app_mode sound_only_page:app_sst(str(MODEL_LOCAL_PATH), str(LANG_MODEL_LOCAL_PATH), lm_alpha, lm_beta, beam)elif app_mode with_video_page:app_sst_with_video(str(MODEL_LOCAL_PATH), str(LANG_MODEL_LOCAL_PATH), lm_alpha, lm_beta, beam)def app_sst(model_path: str, lm_path: str, lm_alpha: float, lm_beta: float, beam: int):webrtc_ctx webrtc_streamer(keyspeech-to-text,modeWebRtcMode.SENDONLY,audio_receiver_size1024,rtc_configuration{iceServers: get_ice_servers()},media_stream_constraints{video: False, audio: True},)status_indicator st.empty()if not webrtc_ctx.state.playing:returnstatus_indicator.write(Loading...)text_output st.empty()stream Nonewhile True:if webrtc_ctx.audio_receiver:if stream is None:from deepspeech import Modelmodel Model(model_path)model.enableExternalScorer(lm_path)model.setScorerAlphaBeta(lm_alpha, lm_beta)model.setBeamWidth(beam)stream model.createStream()status_indicator.write(Model loaded.)sound_chunk pydub.AudioSegment.empty()try:audio_frames webrtc_ctx.audio_receiver.get_frames(timeout1)except queue.Empty:time.sleep(0.1)status_indicator.write(No frame arrived.)continuestatus_indicator.write(Running. Say something!)for audio_frame in audio_frames:sound pydub.AudioSegment(dataaudio_frame.to_ndarray().tobytes(),sample_widthaudio_frame.format.bytes,frame_rateaudio_frame.sample_rate,channelslen(audio_frame.layout.channels),)sound_chunk soundif len(sound_chunk) 0:sound_chunk sound_chunk.set_channels(1).set_frame_rate(model.sampleRate())buffer np.array(sound_chunk.get_array_of_samples())stream.feedAudioContent(buffer)text stream.intermediateDecode()text_output.markdown(f**Text:** {text})else:status_indicator.write(AudioReciver is not set. Abort.)breakdef app_sst_with_video(model_path: str, lm_path: str, lm_alpha: float, lm_beta: float, beam: int
):frames_deque_lock threading.Lock()frames_deque: deque deque([])async def queued_audio_frames_callback(frames: List[av.AudioFrame],) - av.AudioFrame:with frames_deque_lock:frames_deque.extend(frames)# Return empty frames to be silent.new_frames []for frame in frames:input_array frame.to_ndarray()new_frame av.AudioFrame.from_ndarray(np.zeros(input_array.shape, dtypeinput_array.dtype),layoutframe.layout.name,)new_frame.sample_rate frame.sample_ratenew_frames.append(new_frame)return new_frameswebrtc_ctx webrtc_streamer(keyspeech-to-text-w-video,modeWebRtcMode.SENDRECV,queued_audio_frames_callbackqueued_audio_frames_callback,rtc_configuration{iceServers: get_ice_servers()},media_stream_constraints{video: True, audio: True},)status_indicator st.empty()if not webrtc_ctx.state.playing:returnstatus_indicator.write(Loading...)text_output st.empty()stream Nonewhile True:if webrtc_ctx.state.playing:if stream is None:from deepspeech import Modelmodel Model(model_path)model.enableExternalScorer(lm_path)model.setScorerAlphaBeta(lm_alpha, lm_beta)model.setBeamWidth(beam)stream model.createStream()status_indicator.write(Model loaded.)sound_chunk pydub.AudioSegment.empty()audio_frames []with frames_deque_lock:while len(frames_deque) 0:frame frames_deque.popleft()audio_frames.append(frame)if len(audio_frames) 0:time.sleep(0.1)status_indicator.write(No frame arrived.)continuestatus_indicator.write(Running. Say something!)for audio_frame in audio_frames:sound pydub.AudioSegment(dataaudio_frame.to_ndarray().tobytes(),sample_widthaudio_frame.format.bytes,frame_rateaudio_frame.sample_rate,channelslen(audio_frame.layout.channels),)sound_chunk soundif len(sound_chunk) 0:sound_chunk sound_chunk.set_channels(1).set_frame_rate(model.sampleRate())buffer np.array(sound_chunk.get_array_of_samples())stream.feedAudioContent(buffer)text stream.intermediateDecode()text_output.markdown(f**Text:** {text})else:status_indicator.write(Stopped.)breakif __name__ __main__:import osDEBUG os.environ.get(DEBUG, false).lower() not in [false, no, 0]logging.basicConfig(format[%(asctime)s] %(levelname)7s from %(name)s in %(pathname)s:%(lineno)d: %(message)s,forceTrue,)logger.setLevel(levellogging.DEBUG if DEBUG else logging.INFO)st_webrtc_logger logging.getLogger(streamlit_webrtc)st_webrtc_logger.setLevel(logging.DEBUG)fsevents_logger logging.getLogger(fsevents)fsevents_logger.setLevel(logging.WARNING)main()
参考文献
【1】API Reference - Streamlit Docs
【2】andfanilo/streamlit-lottie: Streamlit component to render Lottie animations (github.com)turner-anderson/streamlit-cropper: A simple image cropper for Streamlit (github.com)andfanilo/streamlit-lottie: Streamlit component to render Lottie animations (github.com)
【3】awetomate/text-to-speech-streamlit: Text-to-Speech solution using Googles Cloud TTS API and a Streamlit front end (github.com) 【4】Using streamlit for an STT / TTS model demo? - Streamlit Components - Streamlit
【5】AI-App/Streamlit-TTS (github.com)
【6】Building a Voice Assistant using ChatGPT API | Vahids ML-Blog (vahidmirjalili.com)
【7】streamlit/llm-examples: Streamlit LLM app examples for getting started (github.com)
【8】whitphx/streamlit-stt-app: Real time web based Speech-to-Text app with Streamlit (github.com)
