import gradio as gr
import os
import openai
import requests
import json
openai.api_key = os.environ.get("OPENAI_API_KEY")
prompt_templates = {"Default ChatGPT": ""}
def get_empty_state():
return {"total_tokens": 0, "messages": []}
def download_prompt_templates():
url = "https://raw.githubusercontent.com/f/awesome-chatgpt-prompts/main/prompts.csv"
response = requests.get(url)
for line in response.text.splitlines()[1:]:
act, prompt = line.split('","')
prompt_templates[act.replace('"', '')] = prompt.replace('"', '')
choices = list(prompt_templates.keys())
return gr.update(value=choices[0], choices=choices)
def on_token_change(user_token):
openai.api_key = user_token or os.environ.get("OPENAI_API_KEY")
def on_prompt_template_change(prompt_template):
if not isinstance(prompt_template, str): return
return prompt_templates[prompt_template]
def submit_message(user_token, prompt, prompt_template, temperature, max_tokens, state):
history = state['messages']
if not prompt:
return gr.update(value='', visible=state['total_tokens'] < 1_000), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']} / 3000", state
prompt_template = prompt_templates[prompt_template]
system_prompt = []
if prompt_template:
system_prompt = [{ "role": "system", "content": prompt_template }]
prompt_msg = { "role": "user", "content": prompt }
try:
completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history + [prompt_msg], temperature=temperature, max_tokens=max_tokens)
history.append(prompt_msg)
history.append(completion.choices[0].message.to_dict())
state['total_tokens'] += completion['usage']['total_tokens']
except Exception as e:
history.append(prompt_msg)
history.append({
"role": "system",
"content": f"Error: {e}"
})
total_tokens_used_msg = f"Total tokens used: {state['total_tokens']} / 3000" if not user_token else ""
chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)]
input_visibility = user_token or state['total_tokens'] < 3000
return gr.update(value='', visible=input_visibility), chat_messages, total_tokens_used_msg, state
def clear_conversation():
return gr.update(value=None, visible=True), None, "", get_empty_state()
css = """
#col-container {max-width: 80%; margin-left: auto; margin-right: auto;}
#chatbox {min-height: 400px;}
#header {text-align: center;}
#prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}
#total_tokens_str {text-align: right; font-size: 0.8em; color: #666; height: 1em;}
#label {font-size: 0.8em; padding: 0.5em; margin: 0;}
"""
with gr.Blocks(css=css) as demo:
state = gr.State(get_empty_state())
with gr.Column(elem_id="col-container"):
gr.Markdown("""## OpenAI ChatGPT Demo
Using the ofiicial API (gpt-3.5-turbo model)
Prompt templates from [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts).
Current limit is 3000 tokens per conversation.""",
elem_id="header")
with gr.Row():
with gr.Column():
chatbot = gr.Chatbot(elem_id="chatbox")
input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False)
total_tokens_str = gr.Markdown(elem_id="total_tokens_str")
btn_clear_conversation = gr.Button("🔃 Start New Conversation")
with gr.Column():
prompt_template = gr.Dropdown(label="Set a custom insruction for the chatbot:", choices=list(prompt_templates.keys()))
prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview")
gr.Markdown("Enter your own OpenAI API Key to remove the 3000 token limit. You can get it [here](https://platform.openai.com/account/api-keys).", elem_id="label")
user_token = gr.Textbox(placeholder="OpenAI API Key", type="password", show_label=False)
with gr.Accordion("Advanced parameters", open=False):
temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, interactive=True, label="Temperature (higher = more creative/chaotic)")
max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, interactive=True, label="Max tokens per response")
gr.HTML('''