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import os
import gradio as gr
from openai import OpenAI
OPEN_AI_KEY = os.getenv("OPEN_AI_KEY")
OPEN_AI_CLIENT = OpenAI(api_key=OPEN_AI_KEY)
def generate_topics(model, max_tokens, sys_content, scenario, eng_level, user_generate_topics_prompt):
"""
根据系统提示和用户输入的情境及主题,调用OpenAI API生成相关的主题句。
"""
user_content = f"""
scenario is {scenario}
english level is {eng_level}
{user_generate_topics_prompt}
"""
messages = [
{"role": "system", "content": sys_content},
{"role": "user", "content": user_content}
]
request_payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
}
response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
content = response.choices[0].message.content.strip()
return content
def generate_points(model, max_tokens, sys_content, scenario, eng_level, topic, user_generate_points_prompt):
"""
根据系统提示和用户输入的情境、主题,调用OpenAI API生成相关的主题句。
"""
user_content = f"""
scenario is {scenario}
english level is {eng_level}
topic is {topic}
{user_generate_points_prompt}
"""
messages = [
{"role": "system", "content": sys_content},
{"role": "user", "content": user_content}
]
request_payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
}
response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
content = response.choices[0].message.content.strip()
return content
def generate_topic_sentences(model, max_tokens, sys_content, scenario, eng_level, topic, points, user_generate_topic_sentences_prompt):
"""
根据系统提示和用户输入的情境及要点,调用OpenAI API生成相关的主题句及其合理性解释。
"""
user_content = f"""
scenario is {scenario}
english level is {eng_level}
topic is {topic}
points is {points}
{user_generate_topic_sentences_prompt}
"""
messages = [
{"role": "system", "content": sys_content},
{"role": "user", "content": user_content}
]
request_payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
}
response = OPEN_AI_CLIENT.chat.completions.create(**request_payload)
content = response.choices[0].message.content.strip()
return content
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
# basic inputs
gr.Markdown("## 1. Basic Inputs")
model = gr.Radio(["gpt-4-1106-preview", "gpt-3.5-turbo"], label="Model", value="gpt-4-1106-preview")
max_tokens = gr.Slider(minimum=50, maximum=4000, value=1000, label="Max Tokens")
sys_content_input = gr.Textbox(label="System Prompt", value="You are an English teacher who is practicing with me to improve my English writing skill.")
scenario_input = gr.Textbox(label="Scenario")
eng_level_input = gr.Radio(["beginner", "intermediate", "advanced"], label="English Level", value="beginner")
gr.Markdown("## 2. Generate Topic")
default_generate_topics_prompt = """
Give me 10 topics relevant to Scenario,
for a paragraph. Just the topics, no explanation, use simple English language.
Make sure the vocabulary you use is at english level.
"""
user_generate_topics_prompt = gr.Textbox(label="Topics Prompt", value=default_generate_topics_prompt)
generate_topics_button = gr.Button("Generate Topic Sentences")
gr.Markdown("## 3. Generate Points")
topic_input = gr.Textbox(label="Topic")
default_generate_points_prompt = """
Please provide main points to develop in a paragraph about topic in the context of scenario,
use simple English language and make sure the vocabulary you use is at eng_level.
No more explanation either no developing these points into a simple paragraph.
"""
user_generate_points_prompt = gr.Textbox(label="Points Prompt", value=default_generate_points_prompt)
generate_points_button = gr.Button("Generate Points")
gr.Markdown("## 4. Generate Topic Sentences")
points_input = gr.Textbox(label="Points")
default_generate_topic_sentences_prompt = """
Please provide one appropriate topic sentence that aptly introduces the subject for the given scenario and topic.
Additionally, provide two topic sentences that, while related to the topic,
would be considered inappropriate or less effective for the specified context.
Those sentences must include the three main points:".
Use English language and each sentence should not be too long.
For each sentence, explain the reason in Traditional Chinese, Taiwan, 繁體中文 zh-TW.
Make sure the vocabulary you use is at level.
Only return the result in JSON format starting as:
{{
"0": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ],
"1": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ],
"2": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ]
}}
"""
user_generate_topic_sentences_prompt = gr.Textbox(label="Topic Sentences Prompt", value=default_generate_topic_sentences_prompt)
generate_topic_sentences_button = gr.Button("Generate Topic Sentences")
with gr.Column():
topic_output = gr.Textbox(label="Generated Topic Sentences")
points_output = gr.Textbox(label="Generated Points")
generate_topics_button.click(
fn=generate_topics,
inputs=[
model,
max_tokens,
sys_content_input,
scenario_input,
eng_level_input,
user_generate_topics_prompt
],
outputs=topic_output
)
generate_points_button.click(
fn=generate_points,
inputs=[
model,
max_tokens,
sys_content_input,
scenario_input,
eng_level_input,
topic_input,
user_generate_points_prompt
],
outputs=points_output
)
generate_topic_sentences_button.click(
fn=generate_topic_sentences,
inputs=[
model,
max_tokens,
sys_content_input,
scenario_input,
eng_level_input,
topic_input,
points_input,
user_generate_topic_sentences_prompt
],
outputs=topic_output
)
demo.launch()
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