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from langchain.text_splitter import RecursiveCharacterTextSplitter
from streamlit_mic_recorder import mic_recorder
from streamlit.components.v1 import html,iframe
from huggingface_hub import InferenceClient
import google.generativeai as genai
import speech_recognition as sr
from PyDeepLX import PyDeepLX
from docx import Document
from openai import OpenAI
import streamlit as st
from gtts import gTTS
from PIL import Image
import pandas as pd
import requests
import hashlib
import base64
import langid
import PyPDF2
import io
if "openai_model_list" not in st.session_state:
# author parameter
st.session_state.author_key = ""
st.session_state.gpt_choice = True
st.session_state.gpt_choice_name = "Gemini"
# chat parameter
st.session_state.mode_list = ["**🤖Chat**","**🔤Deeplx**","**🎨Txt2Img**","**📊Data**"]
st.session_state.mode = "**🤖Chat**"
st.session_state.sys_prompt = ""
st.session_state.chat_speech = True
st.session_state.speech_input = False
st.session_state.speech_input_lists = ["中文-zh","English-en","日本語-ja","Русский язык-ru","Deutsch-de","Français-fr","중국어-ko"]
st.session_state.speech_language = st.session_state.speech_input_lists[0]
st.session_state.audio_prompt = None
st.session_state.chat_short_file = None
st.session_state.openai_model_list = [
"gpt-3.5-turbo",
"gpt-3.5-turbo-instruct",
"gpt-4",
"gpt-4-32k",
"gpt-4-1106-preview",
]
st.session_state.openai_model = st.session_state.openai_model_list[0]
st.session_state.openai_session = []
st.session_state.openai_history = []
st.session_state.google_model_list = ["gemini-pro","gemini-pro-vision"]
st.session_state.google_model = st.session_state.google_model_list[0]
st.session_state.google_session = []
st.session_state.google_histgory = []
st.session_state.google_attachment = None
# translate parameter
st.session_state.translate_session = []
st.session_state.lang_lists = ["auto","中文-zh","English-en","日本語-ja","Русский язык-ru","Deutsch-de","Français-fr","중국어-ko"]
st.session_state.target_lang = st.session_state.lang_lists[0]
st.session_state.translate_speech = True
st.session_state.translate_api_list = [
"https://api.deeplx.org/translate",
"https://deeplx.aivvm.com/",
"PyDeeplx"]
st.session_state.translate_api = st.session_state.translate_api_list[0]
# draw parameter
st.session_state.draw_model = "初始-StableDiffusion-2-1"
st.session_state.draw_model_list = {
"现实-AbsoluteReality_v1.8.1":"https://api-inference.huggingface.co/models/digiplay/AbsoluteReality_v1.8.1",
"现实-Absolute-Reality-1.81":"https://api-inference.huggingface.co/models/Lykon/absolute-reality-1.81",
"动漫-AingDiffusion9.2":"https://api-inference.huggingface.co/models/digiplay/AingDiffusion9.2",
"现实动漫-BluePencilRealistic_v01":"https://api-inference.huggingface.co/models/digiplay/bluePencilRealistic_v01",
"动漫写实-Counterfeit-v2.5":"https://api-inference.huggingface.co/models/gsdf/Counterfeit-V2.5",
"动漫写实-Counterfeit-v25-2.5d-tweak":"https://api-inference.huggingface.co/models/digiplay/counterfeitV2525d_tweak",
"动漫可爱-Cuteyukimix":"https://api-inference.huggingface.co/models/stablediffusionapi/cuteyukimix",
"动漫可爱-Cuteyukimixadorable":"https://api-inference.huggingface.co/models/stablediffusionapi/cuteyukimixadorable",
"现实动漫-Dreamshaper-7":"https://api-inference.huggingface.co/models/Lykon/dreamshaper-7",
"现实动漫-Dreamshaper_LCM_v7":"https://api-inference.huggingface.co/models/SimianLuo/LCM_Dreamshaper_v7",
"动漫3D-DucHaitenDreamWorld":"https://api-inference.huggingface.co/models/DucHaiten/DucHaitenDreamWorld",
"现实-EpiCRealism":"https://api-inference.huggingface.co/models/emilianJR/epiCRealism",
"现实照片-EpiCPhotoGasm":"https://api-inference.huggingface.co/models/Yntec/epiCPhotoGasm",
"动漫丰富-Ether-Blu-Mix-b5":"https://api-inference.huggingface.co/models/tensor-diffusion/Ether-Blu-Mix-V5",
"动漫-Flat-2d-Animerge":"https://api-inference.huggingface.co/models/jinaai/flat-2d-animerge",
"动漫风景-Genshin-Landscape-Diffusion":"https://api-inference.huggingface.co/models/Apocalypse-19/Genshin-Landscape-Diffusion",
"现实照片-Juggernaut-XL-v7":"https://api-inference.huggingface.co/models/stablediffusionapi/juggernaut-xl-v7",
"现实风景-Landscape_PhotoReal_v1":"https://api-inference.huggingface.co/models/digiplay/Landscape_PhotoReal_v1",
"艺术水墨-MoXin":"https://api-inference.huggingface.co/models/zhyemmmm/MoXin",
"现实写实-OnlyRealistic":"https://api-inference.huggingface.co/models/stablediffusionapi/onlyrealistic",
"现实-Realistic-Vision-v51":"https://api-inference.huggingface.co/models/stablediffusionapi/realistic-vision-v51",
"初始-StableDiffusion-2-1":"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1",
"初始-StableDiffusion-XL-0.9":"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-0.9",
"动漫-TMND-Mix":"https://api-inference.huggingface.co/models/stablediffusionapi/tmnd-mix",
"animagine-XL-3.0":"https://api-inference.huggingface.co/models/cagliostrolab/animagine-xl-3.0",
"艺术-Zavychromaxl-v3":"https://api-inference.huggingface.co/models/stablediffusionapi/zavychromaxlv3",
"Dalle-v1.1":"https://api-inference.huggingface.co/models/dataautogpt3/OpenDalleV1.1",
"Dalle-3-xl":"https://api-inference.huggingface.co/models/openskyml/dalle-3-xl",
"playground-v2-美化":"https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic",
"Dalle-proteus-v0.2":"https://api-inference.huggingface.co/models/dataautogpt3/ProteusV0.2",
}
st.session_state.negative_prompt = "extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, bad anatomy, bad proportions, extra limbs, cloned face, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs"
st.session_state.StableDiffusion_URL = st.session_state.draw_model_list[st.session_state.draw_model]
st.session_state.auto_translate = True
st.session_state.chat_draw = True
st.session_state.wait_for_model = True
st.session_state.draw_sesson = []
st.session_state.draw_chat_system = """
I want you to act as a prompt generator for Midjourney's artificial intelligence program. Your job is to based on conversations with users provide detailed and creative descriptions that will inspire unique and interesting images from the AI. Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible. For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures. The more detailed and imaginative your description, the more interesting the resulting image will be. Remember to generate a description in English only.
"""
st.session_state.chat_draw_session = [{'role':'system','content':st.session_state.draw_chat_system}]
st.session_state.prompts_gpt = pd.DataFrame(columns=['act', 'prompt'])
# token
st.session_state.openai_api_key = ""
st.session_state.openai_base_url = ""
st.session_state.google_api_key = ""
st.session_state.huggingface_token = ""
# 类
st.session_state.sr = sr.Recognizer()
st.session_state.openai_client = None
st.session_state.google_client = None
########################### element ###########################
header = st.empty()
# 整体页面
show_app = st.container()
with show_app:
# 文字聊天
show_chat = st.container()
# 语音对话
show_talk = st.container()
# deepl翻译
show_translate = st.container()
# 文本生成图片
show_draw = st.container()
# 数据
show_data = st.container()
########################### function ###########################
@st.cache_data
def sha256_hash(string):
# 创建SHA256哈希对象
sha256_hasher = hashlib.sha256()
# 将字符串编码为字节流并更新哈希对象
sha256_hasher.update(string.encode('utf-8'))
# 获取哈希结果
hashed_string = sha256_hasher.hexdigest()
return hashed_string
def get_response(flag,model,history,stream=True):
try:
if not flag:
response = st.session_state.openai_client.chat.completions.create(
model = model,
messages = history,
stream=stream
)
else:
response = st.session_state.google_client.generate_content(
contents = history,
stream=stream,
safety_settings={'HARASSMENT':'block_none'}
)
return True,response
except Exception as e:
st.error("Chat AI response error:{}".format(e))
return False,e
def chat_ai(message,model,history,session,flag=st.session_state.gpt_choice,attachment=st.session_state.google_attachment):
if len(history) != 0 and len(session) != 0:
if history[-1]["role"] == "user":
history.pop()
if session[-1]["role"] == "user":
session.pop()
# openai
if not flag:
history.append({"role":"user","content":message})
session.append({"role":"user","content":message})
response_check,response = get_response(flag,model,history)
if response_check:
show_chat_page(flag,session)
reply={"role":"assistant","content":""}
with show_chat:
with st.chat_message(reply["role"]):
line = st.empty()
for chunk in response:
message = chunk.choices[0].delta.content
if message is not None:
reply["content"] += message
line.empty()
line.write(reply["content"])
history.append(reply)
session.append(reply)
if st.session_state.chat_speech == True:
if reply["content"] != "":
mytts(reply["content"])
else:
history.pop()
session.pop()
st.error(response)
# google
else:
if model == "gemini-pro-vision":
if attachment is not None:
history=[{"role":"user","parts":[message,]+attachment},]
session=[{"role":"user","parts":[message,]+attachment},]
attachment = None
else:
st.error("Please attach a Image")
return False
else:
history.append({"role":"user","parts":[message,]})
session.append({"role":"user","parts":[message,]})
response_check,response = get_response(flag,model,history)
if response_check:
show_chat_page(flag,session)
reply = {"role":"model","parts":["",]}
with show_chat:
with st.chat_message(reply["role"]):
line = st.empty()
for chunk in response:
try:
message = chunk.text
reply["parts"][0] += message
line.empty()
line.write(reply["parts"][0])
except Exception as e:
print(f'{type(e).__name__}: {e}')
history.append(reply)
session.append(reply)
if st.session_state.chat_speech == True:
if reply["parts"][0]!="":
mytts(reply["parts"][0])
else:
if model != "gemini-pro-vision":
history.pop()
session.pop()
st.error(response)
def mytts(text):
def autoplay_audio(audio_data:io.BytesIO):
data = audio_data.getvalue()
b64 = base64.b64encode(data).decode()
md = f"""
<audio controls autoplay="true" id="myAudio" style="width: 100%;">
<source src="data:audio/ogg;base64,{b64}" type="audio/ogg">
</audio>
<script>
var audio = document.getElementById("myAudio");
audio.playbackRate = 1.5;
</script>
"""
html(md)
text = text.replace("```"," ").replace("`"," ").replace("***"," ").replace("**"," ").replace("$$"," ").replace("###"," ").replace("##"," ").replace("#"," ").replace("---"," ")
lang,conf = langid.classify(text)
tts = gTTS(text=text,lang=lang)
speach_BytesIO = io.BytesIO()
tts.write_to_fp(speach_BytesIO)
autoplay_audio(speach_BytesIO)
st.write(lang,conf)
@st.cache_data
def audio2text(audio_prompt,language):
audio_data = sr.AudioData(audio_prompt['bytes'],audio_prompt['sample_rate'],audio_prompt['sample_width'])
output = st.session_state.sr.recognize_google(audio_data,language=language)
return output
def show_chat_page(flag,session):
if not flag:
with show_chat:
for section in session:
with st.chat_message(section["role"]):
st.write(section["content"])
else:
with show_chat:
for section in session:
with st.chat_message(section["role"]):
for piece in section["parts"]:
if isinstance(piece,str):
st.write(piece)
elif isinstance(piece,Image.Image):
st.image(piece,use_column_width=True)
@st.cache_data
def get_file_reader(file,name,type):
def get_text(file,type):
def extract_text_from_docx(file):
doc = Document(file)
text = ""
for paragraph in doc.paragraphs:
text += paragraph.text + "\n"
return text
def extract_text_from_pdf(file):
pdf = PyPDF2.PdfReader(file)
text = ""
for page_num in range(len(pdf.pages)):
page = pdf.pages[page_num]
text += page.extract_text()
return text
# 文件类型判断
if type == 'pdf':
text = extract_text_from_pdf(file)
elif type == 'docx':
text = extract_text_from_docx(file)
elif type == 'txt' or type == 'md' or type == 'py' or type == 'c' or type == 'cpp' or type == 'js':
text = file.getvalue().decode("utf-8")
else:
st.error("The file type is not supported.(only pdf, docx, txt, md supported)")
return []
return text
def get_splitted_text(text):
r_splitter = RecursiveCharacterTextSplitter(
chunk_size=4000,
chunk_overlap=0
)
return r_splitter.split_text(text)
assistant_reply = "Acknowledged"
start_content = "You are a file reading bot. Next, the user will send a file. After reading, you should fully understand the content of the file and be able to analyze, interpret, and respond to questions related to the file in both Chinese and Markdown formats. Please only answer questions based on the content of the document. If the question is not mentioned in the document, please reply directly to the article without referring to other materials. Answer step-by-step."
end_content = "File sent. Next, please reply in Chinese and format your response using markdown based on the content.'"
st.session_state.openai_history = [{'role':'system','content':start_content}]
st.session_state.google_histgory = [{'role':'user','parts':[start_content,]},{'role':'model','parts':[assistant_reply,]}]
# 文本提取并拆分
text = get_text(file,type)
text_list = get_splitted_text(text)
pages = len(text_list)
start_message = f"The file name is {name}, and I will now send you the content of the file in {len(text_list)} sections. Please ensure that you are ready to receive the instructions for sending the file. Once you receive the instructions, please be prepared to answer my question."
st.session_state.openai_history+=[{'role':'user','content':start_message},{'role':'assistant','content':assistant_reply}]
st.session_state.google_histgory+=[{'role':'user','parts':[start_message,]},{'role':'model','parts':[assistant_reply,]}]
# 分段输入
for i in range(pages):
st.session_state.openai_history+=[{'role':'user','content':text_list[i]},{'role':'assistant','content':assistant_reply}]
st.session_state.google_histgory+=[{'role':'user','parts':[text_list[i],]},{'role':'model','parts':[assistant_reply,]}]
# 结束文本输入
st.session_state.openai_history+=[{'role':'user','content':end_content},{'role':'assistant','content':"I have finished reading the file content, you can ask me anything."}]
st.session_state.google_histgory+=[{'role':'user','parts':[end_content,]},{'role':'model','parts':["I have finished reading the file content, you can ask me anything.",]}]
def deeplx_translate(text,source_lang,target_lang,api):
if api == st.session_state.translate_api_list[0]:
if source_lang is None:
source_lang="auto"
headers = {"Content-Type": "application/json"}
body = {
"text":text,
"target_lang":target_lang,
"source_lang":source_lang
}
try:
response = requests.post(api, json=body, headers=headers)
return True,response.json()["data"]
except Exception as e:
st.error("Deeplx response error: {}".format(e))
return False,e
elif api == st.session_state.translate_api_list[1]:
if source_lang is None:
source_lang,conf = langid.classify(text)
headers = {"Content-Type": "application/json"}
body = {
"text":text,
"target_lang":target_lang,
"source_lang":source_lang
}
try:
response = requests.post(api, json=body, headers=headers)
return True,response.json()["response"]["translated_text"]
except Exception as e:
st.error("Deeplx response error: {}".format(e))
return False,e
elif api == st.session_state.translate_api_list[-1]:
try:
response = PyDeepLX.translate(text,'auto',target_lang)
return True,response
except Exception as e:
st.error("Deeplx response error: {}".format(e))
return False,e
def translate(text,target_lang,api=st.session_state.translate_api):
st.session_state.translate_session.append({"role":"user","content":text})
show_translate_page()
if target_lang == "to":
lang,conf = langid.classify(text)
if lang == "zh":
flag,reply = deeplx_translate(text,lang,"en",api)
else:
lang_list = [i[-2:] for i in st.session_state.lang_lists]
lang_list.remove("to")
if lang not in lang_list:
flag,reply = deeplx_translate(text,"en","zh",api)
else:
flag,reply = deeplx_translate(text,lang,"zh",api)
else:
flag,reply = deeplx_translate(text,None,target_lang,api)
if flag:
st.session_state.translate_session.append({"role":"assistant","content":reply})
with show_translate.chat_message("assistant"):
st.write(reply)
if st.session_state.translate_speech == True:
if reply != "":
mytts(reply)
else:
st.error(reply)
def show_translate_page():
for section in st.session_state.translate_session:
with show_translate.chat_message(section['role']):
st.write(section['content'])
def text2img(prompt,token=st.session_state.huggingface_token,StableDiffusion_URL=st.session_state.StableDiffusion_URL):
def query(client,payload):
try:
response = client.post(json=payload,model=StableDiffusion_URL)
return True, response
except requests.exceptions.RequestException as e:
return False,e
huggingface_client = InferenceClient(token=token)
st.session_state.draw_sesson.append({"role":"user","prompt":prompt})
if st.session_state.chat_draw:
if len(st.session_state.chat_draw_session) != 0:
if st.session_state.chat_draw_session[-1]["role"] == "user":
st.session_state.chat_draw_session.pop()
st.session_state.chat_draw_session.append({"role":"user","content":prompt})
response_check,response = get_response(False,st.session_state.openai_model,st.session_state.chat_draw_session,stream=False)
if response_check:
prompt = response.choices[0].message.content
st.session_state.chat_draw_session.append({"role":"assistant","content":prompt})
if st.session_state.auto_translate:
lang,conf = langid.classify(prompt)
if lang != "en":
flag,prompt = deeplx_translate(prompt,lang,"en",st.session_state.translate_api)
if not flag:
return False
show_draw_page()
with show_draw.chat_message("assistant"):
st.write("**"+st.session_state.draw_model+"**: "+prompt)
flag,response = query(huggingface_client,{
"inputs":prompt,
"negative_prompt":st.session_state.negative_prompt,
})
image = response
st.session_state.draw_sesson.append({"role":"assistant","prompt":"**"+st.session_state.draw_model+"**: "+prompt,"image":image,"flag":flag})
if flag:
st.image(image,use_column_width=True)
else:
st.write(image)
else:
if st.session_state.auto_translate:
lang,conf = langid.classify(prompt)
if lang != "en":
flag,prompt = deeplx_translate(prompt,lang,"en",st.session_state.translate_api)
if not flag:
return False
show_draw_page()
with show_draw.chat_message("assistant"):
st.write("**"+st.session_state.draw_model+"**: "+prompt)
flag,response = query(huggingface_client,{
"inputs":prompt,
"negative_prompt":st.session_state.negative_prompt,
})
image = response
st.session_state.draw_sesson.append({"role":"assistant","prompt":"**"+st.session_state.draw_model+"**: "+prompt,"image":image,"flag":flag})
if flag:
st.image(image,use_column_width=True)
else:
st.write(image)
def show_draw_page():
for section in st.session_state.draw_sesson:
with show_draw.chat_message(section["role"]):
if section["role"] == "user":
st.write(section["prompt"])
else:
st.write(section["prompt"])
if section["flag"]:
st.image(section["image"],use_column_width=True)
else:
st.write(section["image"])
@st.cache_data
def get_data(file):
data = pd.read_csv(file)
return data
########################### mount ###########################
def new_chat():
# openai
if st.session_state.sys_prompt == "":
st.session_state.openai_history = []
else:
st.session_state.openai_history = [{"role":"system","content":st.session_state.sys_prompt},]
st.session_state.openai_session = []
# google
st.session_state.google_histgory = []
st.session_state.google_session = []
if st.session_state.google_api_key:
genai.configure(api_key=st.session_state.google_api_key)
st.session_state.google_client = genai.GenerativeModel(st.session_state.google_model)
# translate
st.session_state.translate_session = []
# draw
st.session_state.draw_sesson = []
st.session_state.chat_draw_session = [{'role':'system','content':st.session_state.draw_chat_system}]
def author_channel():
author_key_hash = sha256_hash(st.session_state.author_key.strip())
if author_key_hash in st.secrets.pwsds:
# openai
st.session_state.openai_api_key = st.secrets.openai_api_keys[st.secrets.pwsds[author_key_hash]]
st.session_state.openai_base_url = st.secrets.openai_base_urls[st.secrets.pwsds[author_key_hash]]
st.session_state.openai_client = OpenAI(
api_key=st.session_state.openai_api_key,
base_url=st.session_state.openai_base_url
)
# google
st.session_state.google_api_key = st.secrets.google_api_keys[st.secrets.pwsds[author_key_hash]]
genai.configure(api_key=st.session_state.google_api_key)
st.session_state.google_client = genai.GenerativeModel(st.session_state.google_model)
# huggingface
st.session_state.huggingface_token = st.secrets.huggingface_tokens[st.secrets.pwsds[author_key_hash]]
def gpt_choice():
st.session_state.gpt_choice = not st.session_state.gpt_choice
if st.session_state.gpt_choice:
st.session_state.gpt_choice_name = "Gemini"
else:
st.session_state.gpt_choice_name = "ChatGPT"
def upload_google_attachment():
st.session_state.google_attachment = st.session_state.google_attachment
if st.session_state.google_attachment is not None:
attachment = []
for upload_img in st.session_state.google_attachment:
attachment.append(Image.open(upload_img))
st.session_state.google_attachment = attachment
def get_file_chat():
def collect_file(file_upload):
file_name = ".".join(file_upload.name.split('.')[0:-1])
file_type = file_upload.name.split('.')[-1]
return file_name,file_type
st.session_state.chat_short_file = st.session_state.chat_short_file
if st.session_state.chat_short_file:
file_name,file_type = collect_file(st.session_state.chat_short_file)
get_file_reader(st.session_state.chat_short_file,file_name,file_type)
def change_paramater():
st.session_state.openai_api_key = st.session_state.openai_api_key
st.session_state.openai_base_url = st.session_state.openai_base_url
st.session_state.sys_prompt = st.session_state.sys_prompt
st.session_state.google_api_key = st.session_state.google_api_key
st.session_state.chat_speech = st.session_state.chat_speech
st.session_state.google_api_key = st.session_state.google_api_key
st.session_state.speech_input = st.session_state.speech_input
st.session_state.speech_language = st.session_state.speech_language
st.session_state.draw_model = st.session_state.draw_model
st.session_state.StableDiffusion_URL = st.session_state.draw_model_list[st.session_state.draw_model]
st.session_state.huggingface_token = st.session_state.huggingface_token
st.session_state.negative_prompt = st.session_state.negative_prompt
st.session_state.mode = st.session_state.mode
st.session_state.translate_api = st.session_state.translate_api
st.session_state.target_lang = st.session_state.target_lang
st.session_state.translate_speech = st.session_state.translate_speech
st.session_state.auto_translate = st.session_state.auto_translate
st.session_state.chat_draw = st.session_state.chat_draw
st.session_state.wait_for_model = st.session_state.wait_for_model
st.session_state.prompts_gpt = st.session_state.prompts_gpt
def get_save():
change_paramater()
# openai
if st.session_state.openai_api_key and st.session_state.openai_base_url:
st.session_state.openai_client = OpenAI(
api_key=st.session_state.openai_api_key,
base_url=st.session_state.openai_base_url
)
if len(st.session_state.openai_history) == 0:
if st.session_state.sys_prompt != "":
st.session_state.openai_history = [{"role":"system","content":st.session_state.sys_prompt},]
# google
if st.session_state.google_api_key:
genai.configure(api_key=st.session_state.google_api_key)
st.session_state.google_client = genai.GenerativeModel(st.session_state.google_model)
# show
if st.session_state.mode == "**🤖Chat**":
if not st.session_state.gpt_choice:
show_chat_page(False,st.session_state.openai_session)
else:
show_chat_page(True,st.session_state.google_session)
elif st.session_state.mode == "**🔤Deeplx**":
show_translate_page()
elif st.session_state.mode == "**🎨Txt2Img**":
show_draw_page()
########################### sidebar ###########################
with st.sidebar:
# 新的开始
with st.container():
st.button("🆕 New Chat",use_container_width=True,key="New Chat")
if st.session_state.get("New Chat"):
new_chat()
# 作者通道
with st.container():
st.session_state.author_key = st.text_input("author channel",type='password',value=st.session_state.author_key,key="author channel")
if st.session_state.get("author channel"):
author_channel()
# 聊天设置
with st.container():
with st.expander("**Chat Settings**"):
col1,col2 = st.columns(2)
with col1:
st.session_state.gpt_choice = st.toggle(st.session_state.gpt_choice_name,value=st.session_state.gpt_choice,on_change=gpt_choice)
with col2:
st.session_state.chat_speech = st.toggle("speech",st.session_state.chat_speech,on_change=change_paramater)
if not st.session_state.gpt_choice:
st.session_state.openai_model = st.selectbox("Chat Models",sorted(st.session_state.openai_model_list),on_change=new_chat)
st.session_state.openai_api_key = st.text_input("api key",value=st.session_state.openai_api_key,type='password')
st.session_state.openai_base_url = st.text_input("api base",value=st.session_state.openai_base_url)
st.session_state.sys_prompt = st.text_input("sys prompt",value=st.session_state.sys_prompt,on_change=change_paramater)
st.session_state.chat_short_file = st.file_uploader("Chat short file",label_visibility="collapsed")
st.button("ChatFile",use_container_width=True,key="ChatFile")
if st.session_state.get("ChatFile"):
get_file_chat()
else:
st.session_state.google_model = st.selectbox("Chat Models",sorted(st.session_state.google_model_list),on_change=new_chat)
st.session_state.google_api_key = st.text_input("api key",value=st.session_state.google_api_key,type='password',on_change=change_paramater)
if st.session_state.google_model == "gemini-pro-vision":
st.session_state.google_attachment = st.file_uploader("Image for gemini-pro-vision",type=['jpg','png','jpeg'],accept_multiple_files=True,label_visibility="collapsed")
st.button("Send Image",key="google attachment",use_container_width=True)
if st.session_state.get("google attachment"):
upload_google_attachment()
else:
st.session_state.chat_short_file = st.file_uploader("Chat short file",label_visibility="collapsed")
st.button("ChatFile",use_container_width=True,key="ChatFile")
if st.session_state.get("ChatFile"):
get_file_chat()
st.session_state.speech_input = st.toggle("talk mode",st.session_state.speech_input,on_change=change_paramater)
# 翻译设置
with st.container():
with st.expander("**Translate Settings**"):
st.session_state.translate_api = st.selectbox("Translate API",st.session_state.translate_api_list,on_change=change_paramater)
st.session_state.target_lang = st.selectbox("Target Language",st.session_state.lang_lists,on_change=change_paramater)
st.session_state.translate_speech = st.toggle('translate speech', st.session_state.translate_speech,on_change=change_paramater)
# 绘画设置
with st.container():
with st.expander("**Draw Settings**"):
st.session_state.draw_model = st.selectbox('Draw Models', sorted(st.session_state.draw_model_list.keys(),key=lambda x:x.split("-")[0]),on_change=change_paramater)
st.session_state.huggingface_token = st.text_input('Huggingface Token',type='password',value=st.session_state.huggingface_token,on_change=change_paramater)
st.session_state.negative_prompt = st.text_input('Negative Prompt',value=st.session_state.negative_prompt,on_change=change_paramater)
# col1,col2,col3 = st.columns(3)
# with col1:
st.session_state.chat_draw = st.toggle('Chat', st.session_state.chat_draw,on_change=change_paramater)
# with col2:
st.session_state.auto_translate = st.toggle('Translate', st.session_state.auto_translate,on_change=change_paramater)
# with col3:
st.session_state.wait_for_model = st.toggle('Wait', st.session_state.wait_for_model,on_change=change_paramater)
# 保存
st.button("Save",use_container_width=True,key="Save")
if st.session_state.get("Save"):
get_save()
# 模式
with st.container():
with st.container():
st.session_state.mode = st.radio("Choose Mode",st.session_state.mode_list,on_change=change_paramater)
########################### 聊天展示区 ###########################
if st.session_state.mode == "**🤖Chat**":
if not st.session_state.gpt_choice:
header.write("<h2> 🤖 "+st.session_state.openai_model+"</h2>",unsafe_allow_html=True)
else:
header.write("<h2> 🤖 "+st.session_state.google_model+"</h2>",unsafe_allow_html=True)
if not st.session_state.speech_input:
user_prompt = st.chat_input("Send a message")
if user_prompt:
if not st.session_state.gpt_choice:
chat_ai(user_prompt,st.session_state.openai_model,st.session_state.openai_history,st.session_state.openai_session)
else:
chat_ai(user_prompt,st.session_state.google_model,st.session_state.google_histgory,st.session_state.google_session)
else:
with st.container():
st.session_state.speech_language = st.selectbox("🎙️language",st.session_state.speech_input_lists,on_change=change_paramater)
st.session_state.audio_prompt = mic_recorder(
start_prompt="🎙️开始说话",
stop_prompt="🛑结束说话",
just_once=True,
use_container_width=True,
callback=None,
args=(),
kwargs={},
key=None
)
if st.session_state.audio_prompt:
user_prompt = audio2text(st.session_state.audio_prompt,st.session_state.speech_language[-2:])
if not st.session_state.gpt_choice:
chat_ai(user_prompt,st.session_state.openai_model,st.session_state.openai_history,st.session_state.openai_session)
else:
chat_ai(user_prompt,st.session_state.google_model,st.session_state.google_histgory,st.session_state.google_session)
elif st.session_state.mode == "**🔤Deeplx**":
header.write("<h2> 🔤 Deeplx-"+st.session_state.target_lang+"</h2>",unsafe_allow_html=True)
txt_prompt = st.chat_input("Input your content to be translated",max_chars=5000)
if txt_prompt:
translate(txt_prompt,st.session_state.target_lang[-2:])
elif st.session_state.mode == "**🎨Txt2Img**":
header.write("<h2> 🎨 "+st.session_state.draw_model+"</h2>",unsafe_allow_html=True)
draw_prompt = st.chat_input("Send your prompt")
if draw_prompt:
text2img(draw_prompt)
elif st.session_state.mode == "**📊Data**":
# 获取数据
prompts_gpt = get_data("./prompts.csv")
# 显示
header.write("<h2> 📊Data </h2>",unsafe_allow_html=True)
keywords = st.chat_input("Send your keywords")
with show_data:
tab_gpt,tab_sd = st.tabs(["GPT-Prompts","SD-Prompts"])
with tab_gpt:
if keywords:
st.session_state.prompts_gpt = pd.DataFrame(columns=['act', 'prompt'])
idx = 0
for index,row in prompts_gpt.iterrows():
if keywords.lower() in row["act"].lower():
idx += 1
new_row = pd.DataFrame({"act":row["act"], "prompt":row["prompt"]}, index=[idx,])
st.session_state.prompts_gpt = pd.concat([st.session_state.prompts_gpt, new_row],ignore_index=True)
st.dataframe(st.session_state.prompts_gpt)
else:
st.session_state.prompts_gpt = prompts_gpt
st.dataframe(st.session_state.prompts_gpt)
with show_data:
pass
change_paramater()