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| import gradio as gr | |
| from utils.api.GPT import generate_answers_openai, Session | |
| import numpy as np | |
| import os | |
| import time | |
| def generate_interface(prompt, description=None): | |
| session = Session(prompt) | |
| def _fun(customer_input): | |
| print("(Q:) ", customer_input) | |
| response = generate_answers_openai(customer_input, session) | |
| print("(A:) ", response) | |
| return session.to_conversation_pair() | |
| interface = gr.Interface(description=description, fn=_fun, | |
| inputs=[gr.Textbox(lines=1, label="input")], | |
| outputs=[gr.Chatbot(label="conversation")]) | |
| return interface | |
| prompts = [ | |
| {"name": "Summary", "prompt": "Based on the language of human in the conversation, you will generate a report to assess the English level, summarize the conversation, and provide suggestions to improve the language. Conservation:{}", "description": "generate report."}, | |
| ] | |
| interfaces = [] | |
| names = [] | |
| for function in prompts: | |
| interface = generate_interface(function['prompt'], function['description']) | |
| interfaces.append(interface) | |
| names.append(function['name']) | |
| demo = gr.TabbedInterface(interfaces, names,title="Role Play Bot") | |
| if __name__ == '__main__': | |
| demo.launch() |