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import os
import gradio as gr
import openai
from langdetect import detect
from gtts import gTTS
from pdfminer.high_level import extract_text
openai.api_key = os.environ['OPENAI_API_KEY']
user_db = {os.environ['username1']: os.environ['password1'], os.environ['username2']: os.environ['password2']}
messages = [{"role": "system", "content": 'You are a helpful assistant.'}]
def roleChoice(role):
global messages
messages = [{"role": "system", "content": role}]
return "role:" + role
def audioGPT(audio):
global messages
audio_file = open(audio, "rb")
transcript = openai.Audio.transcribe("whisper-1", audio_file)
messages.append({"role": "user", "content": transcript["text"]})
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
system_message = response["choices"][0]["message"]
messages.append(system_message)
chats = ""
for msg in messages:
if msg['role'] != 'system':
chats += msg['role'] + ": " + msg['content'] + "\n\n"
return chats
def textGPT(text):
global messages
messages.append({"role": "user", "content": text})
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
system_message = response["choices"][0]["message"]
messages.append(system_message)
chats = ""
for msg in messages:
if msg['role'] != 'system':
chats += msg['role'] + ": " + msg['content'] + "\n\n"
return chats
def siriGPT(audio):
global messages
audio_file = open(audio, "rb")
transcript = openai.Audio.transcribe("whisper-1", audio_file)
messages.append({"role": "user", "content": transcript["text"]})
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
system_message = response["choices"][0]["message"]
messages.append(system_message)
lang = detect(system_message['content'])
narrate_ans = gTTS(text=system_message['content'], lang=lang, slow=False)
narrate_ans.save("narrate.wav")
return "narrate.wav"
def fileGPT(prompt, file_obj):
global messages
file_text = extract_text(file_obj.name)
text = prompt + "\n\n" + file_text
messages.append({"role": "user", "content": text})
response = openai.ChatCompletion.create(model="gpt-4", messages=messages)
system_message = response["choices"][0]["message"]
messages.append(system_message)
chats = ""
for msg in messages:
if msg['role'] != 'system':
chats += msg['role'] + ": " + msg['content'] + "\n\n"
return chats
def clear():
global messages
messages = [{"role": "system", "content": 'You are a helpful technology assistant.'}]
return
def show():
global messages
chats = ""
for msg in messages:
if msg['role'] != 'system':
chats += msg['role'] + ": " + msg['content'] + "\n\n"
return chats
with gr.Blocks() as chatHistory:
gr.Markdown("Click the Clear button below to remove all the chat history.")
clear_btn = gr.Button("Clear")
clear_btn.click(fn=clear, inputs=None, outputs=None, queue=False)
gr.Markdown("Click the Display button below to show all the chat history.")
show_out = gr.Textbox()
show_btn = gr.Button("Display")
show_btn.click(fn=show, inputs=None, outputs=show_out, queue=False)
role = gr.Interface(fn=roleChoice, inputs="text", outputs="text", description = "Choose your GPT roles, e.g. You are a helpful technology assistant. 你是一位 IT 架构师。 你是一位开发者关系顾问。你是一位机器学习工程师。你是一位高级 C++ 开发人员 ")
text = gr.Interface(fn=textGPT, inputs="text", outputs="text")
audio = gr.Interface(fn=audioGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs="text")
siri = gr.Interface(fn=siriGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs = "audio")
file = gr.Interface(fn=fileGPT, inputs=["text", "file"], outputs="text", description = "Enter prompt sentences and your PDF. e.g. lets think step by step, summarize this following text: 或者 让我们一步一步地思考，总结以下的内容:")
demo = gr.TabbedInterface([role, text, audio, siri, file, chatHistory], [ "roleChoice", "chatGPT", "audioGPT", "siriGPT", "fileGPT", "ChatHistory"])
if __name__ == "__main__":
demo.launch(enable_queue=False, auth=lambda u, p: user_db.get(u) == p,
auth_message="Welcome to Yichuan GPT!")
#demo.launch()
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