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c0fb54b eef36cf c0fb54b 6778e19 c0fb54b 6778e19 c0fb54b 6778e19 c0fb54b 6778e19 c0fb54b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | import time
import base64
from openai import OpenAI
import gradio
import google.generativeai as genai
def generation(prompt_text):
a_key = "QUl6YVN5RGRJZXlsSnVUOERWMndlbXBfeE8wWUpmZEl4OFZ4S1FJ"
genai.configure(api_key=str(base64.b64decode(a_key), encoding="utf-8"))
generation_config = {"temperature": 0.9, "top_p": 1, "top_k": 1, "max_output_tokens": 2048}
safety_settings = [
{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}]
model = genai.GenerativeModel(model_name="gemini-pro", generation_config=generation_config, safety_settings=safety_settings)
response = model.generate_content(prompt_text)
return response
chatgpt_model_type_normal = "gpt-3.5-turbo-instruct：普通对话型"
chatgpt_model_type_memory = "gpt-3.5-turbo-16k：记忆对话型"
chatgpt_model_type_gemini = "这是Google旗下原生多模态大模型 Gemini google-gemini-pro：普通对话型"
conversation_history = []
prompt_parts = [""]
def chatgpt(input_text, model_type, field):
a_key = "c2stRFo3ZzRadXlzOEhMd2RWZnFYVWJUM0JsYmtGSkFPTm9PNHdPMmJZTTc5Tm5DTVQ5"
client = OpenAI(api_key=str(base64.b64decode(a_key), encoding="utf-8"))
if field != "":
input_text = input_text + "\n" + "假设你扮演的人物角色或你专注的学术领域为：" + field
if model_type == chatgpt_model_type_normal:
response = client.completions.create(
model="gpt-3.5-turbo-instruct",
temperature=0.25,
max_tokens=2500,
frequency_penalty=0.5,
presence_penalty=0.5,
top_p=0.88,
prompt=input_text
)
response_text = response.choices[0].text.strip()
if model_type == chatgpt_model_type_memory:
conversation_system = {"role": "system", "content": ""}
conversation_user = {"role": "user", "content": input_text}
if conversation_history == []:
conversation_history.append(conversation_system)
conversation_history.append(conversation_user)
else:
conversation_history.append(conversation_user)
response = client.chat.completions.create(
model="gpt-3.5-turbo-16k",
temperature=0.25,
max_tokens=5000,
frequency_penalty=0.5,
presence_penalty=0.5,
top_p=0.88,
messages=conversation_history,
)
response_text = response.choices[0].message.content
conversation_system = {"role": "system", "content": response_text}
conversation_history.append(conversation_system)
if model_type == chatgpt_model_type_gemini:
if prompt_parts[0] == "":
prompt_parts[0] += input_text
else:
prompt_parts[0] += (" " + input_text)
response_gemini = generation(prompt_parts)
response_text = response_gemini.text
prompt_parts[0] += (" " + response_text)
return response_text
dall_e_model_type_2 = "Dall-E-2：较快 | 较差"
dall_e_model_type_3 = "Dall-E-3：较慢 | 较好"
def dall_e(input_text, add_text, model_type):
a_key = "c2stRFo3ZzRadXlzOEhMd2RWZnFYVWJUM0JsYmtGSkFPTm9PNHdPMmJZTTc5Tm5DTVQ5"
client = OpenAI(api_key=str(base64.b64decode(a_key), encoding="utf-8"))
complete_text = input_text + "\n" + "增加图片风格的细节或场景的细节：" + add_text
if model_type == dall_e_model_type_2:
model_select = "dall-e-2"
if model_type == dall_e_model_type_3:
model_select = "dall-e-3"
response = client.images.generate(
model=model_select,
prompt=complete_text,
size="1024x1024",
quality="standard",
n=1,
)
image_url = response.data[0].url
return image_url
def whisper(audio_file):
a_key = "c2stRFo3ZzRadXlzOEhMd2RWZnFYVWJUM0JsYmtGSkFPTm9PNHdPMmJZTTc5Tm5DTVQ5"
client = OpenAI(api_key=str(base64.b64decode(a_key), encoding="utf-8"))
audio_file = open(audio_file, "rb")
transcript = client.audio.transcriptions.create(model="whisper-1", file=audio_file, response_format="text")
return transcript
tts_model_type_1 = "tts-1：较快 | 较差"
tts_model_type_1_hd = "tts-1-hd：较慢 | 较好"
tts_gender_male = "男生声音：Alloy"
tts_gender_female = "女生声音：Nova"
def tts(input_text, model_type, gender):
a_key = "c2stRFo3ZzRadXlzOEhMd2RWZnFYVWJUM0JsYmtGSkFPTm9PNHdPMmJZTTc5Tm5DTVQ5"
client = OpenAI(api_key=str(base64.b64decode(a_key), encoding="utf-8"))
timestamp = time.time()
speech_file_path = "tts_" + str(timestamp) + ".mp3"
if model_type == "tts-1：较快 | 较差":
model_select = "tts-1"
if model_type == "tts-1-hd：较慢 | 较好":
model_select = "tts-1-hd"
if gender == "男生声音：Alloy":
voice_select = "alloy"
if gender == "女生声音：Nova":
voice_select = "nova"
response = client.audio.speech.create(
model=model_select,
voice=voice_select,
input=input_text,
)
response.stream_to_file(speech_file_path)
return speech_file_path, whisper(speech_file_path)
def demo():
article_thanks = "<br> 🔅Thanks Python. 🔅Thanks OpenAI. 🔅Thanks Gradio. 🔅Thanks Myself. 🔅Thank you ！"
chat_demo = gradio.Interface(fn=chatgpt,
inputs=[gradio.Textbox(label="💢(必须) ⚡你的对话", lines=3, placeholder="输入内容", show_copy_button=True),
gradio.Radio([chatgpt_model_type_normal, chatgpt_model_type_memory, chatgpt_model_type_gemini],
label="⚡选择GPT模型", value=chatgpt_model_type_normal),
gradio.Textbox(label="⚡人物角色 / 学术领域（选择 #记忆对话型 生效）", lines=1,
placeholder="例如：马斯克；人工智能")],
outputs=gradio.Textbox(label="⚡ GPT 对话生成", lines=11,
show_copy_button=True,
placeholder="ChatGPT - 是未成熟的AGI智能工具 - 无法保证回答正确 ！仅供参考 😊"),
title="<br>MyDIY_Resume | Gradio：🤖 Chat-with-GPT3.5",
description="✅ 操作指南：在左边的文本框中输入内容，再选择对话的GPT模型类型，如果选择为角色型则可以为GPT设定一种角色或领域作为回答的方向 ！😁",
article=article_thanks)
image_demo = gradio.Interface(fn=dall_e,
inputs=[gradio.Textbox(label="💢(必须) ⚡描述图片", lines=2, placeholder="输入内容", show_copy_button=True),
gradio.Textbox(label="⚡描述细节", lines=1, placeholder="例如：蔚蓝；科幻", show_copy_button=True),
gradio.Radio([dall_e_model_type_2, dall_e_model_type_3],
label="⚡选择Dall-E模型", value=dall_e_model_type_2)],
outputs=gradio.Image(label="⚡Dell-E 图片生成"),
title="<br>MyDIY_Resume | Gradio：🤖 Dall-E-Generate-Image",
description="✅ 操作指南：在左边的文本框中输入需要生成图片的内容，再选择生成图片的Dall-E模型，需要注意Dall-E-3模型的请求更容易受到限制 ！😁",
article=article_thanks)
stt_demo = gradio.Interface(fn=whisper,
inputs=[gradio.Audio(label="💢(必须) ⚡上传音频文件", type="filepath")],
outputs=gradio.Textbox(label="⚡Whisper 文字生成", lines=10,
placeholder="Whisper-v1 - 是初代版本模型 - 无法保证转换正确 ！仅供参考 😊",
show_copy_button=True),
title="<br>MyDIY_Resume | Gradio：🤖 Whisper-Speech-to-Text",
description="✅ 操作指南：点击左边的音频框进行本地音频文件选取，或者直接将音频文件拖拉到当前音频框，默认的音频转文本模型是Whisper-v1 ！😁",
article=article_thanks)
tts_demo = gradio.Interface(fn=tts,
inputs=[gradio.Textbox(label="💢(必须) ⚡你的文本", lines=3, placeholder="输入内容", show_copy_button=True),
gradio.Radio([tts_model_type_1, tts_model_type_1_hd], label="选择TTS模型", value=tts_model_type_1),
gradio.Radio([tts_gender_male, tts_gender_female], label="选择声音性别", value=tts_gender_male)],
outputs=[gradio.Audio(label="⚡TTS 音频生成"),
gradio.Textbox(label="生成的音频再经过 Whisper 二次检验", lines=5,
placeholder="生成的音频再次经过 Whisper 转换用于验证TTS的准确性 ！仅供参考 😊",
show_copy_button=True)],
title="<br>MyDIY_Resume | Gradio：🤖 TTS-Text-to-Speech",
description="✅ 操作指南：在左边的文本框中输入需要转音频的内容，再选择生成音频的TTS模型，两个模型实际差异不大，最后为模型选择输出男声还是女声 ！😁",
article=article_thanks)
GUI = gradio.TabbedInterface([chat_demo, image_demo, stt_demo, tts_demo],
["1️⃣ Chat-with-GPT：对话学习", "2️⃣ Generate-Image：生成图片", "3️⃣ Speech-to-Text：语音转文本", "4️⃣ Text-to-Speech：文本转语音"],
css="footer {visibility: hidden}")
GUI.launch(debug=True, share=False)
if __name__ == "__main__":
demo() |