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import gradio as gr
import random
import requests
# Template
title = "A conversation with Gandalf (GPTJ-6B) 🧙"
description = ""
article = """
<p> To reset you <b>need to reload the page.</b> </p>
<p> If you liked don't forget to 💖 the project 🥰 </p>
<h2> Parameters: </h2>
<ul>
<li><i>top_p</i>: control how deterministic the model is in generating a response.</li>
<li><i>temperature</i>: (sampling temperature) higher values means the model will take more risks.</li>
<li><i>max_new_tokens</i>: Max number of tokens in generation.</li>
</ul>
<img src='http://www.simoninithomas.com/test/gandalf.jpg', alt="Gandalf"/>"""
theme="huggingface"
examples = [[0.9, 1.1, 50, "Hey Gandalf! How are you?"], [0.9, 1.1, 50, "Hey Gandalf, why you didn't use the great eagles to fly Frodo to Mordor?"]]
# GPT-J-6B API
API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
def query(payload):
response = requests.post(API_URL, json=payload)
return response.json()
context_setup = "The following is a conversation with Gandalf, the mage of 'the Lord of the Rings'"
context=context_setup
interlocutor_names = ["Human", "Gandalf"]
# Builds the prompt from what previously happened
def build_prompt(conversation, context):
prompt = context + "\n"
for user_msg, resp_msg in conversation:
line = "\n- " + interlocutor_names[0] + ":" + user_msg
prompt += line
line = "\n- " + interlocutor_names[1] + ":" + resp_msg
prompt += line
prompt += ""
return prompt
# Attempt to recognize what the model said, if it used the correct format
def clean_chat_output(txt, prompt):
delimiter = "\n- "+interlocutor_names[0]
output = txt.replace(prompt, '')
output = output[:output.find(delimiter)]
return output
def chat(top_p, temperature, max_new_tokens, message):
history = gr.get_state() or []
history.append((message, ""))
gr.set_state(history)
conversation = history
prompt = build_prompt(conversation, context)
# Build JSON
json_ = {"inputs": prompt,
"parameters":
{
"top_p": top_p,
"temperature": temperature,
"max_new_tokens": max_new_tokens,
"return_full_text": False
}}
output = query(json_)
output = output[0]['generated_text']
answer = clean_chat_output(output, prompt)
response = answer
history[-1] = (message, response)
gr.set_state(history)
html = "<div class='chatbot'>"
for user_msg, resp_msg in history:
html += f"<div class='user_msg'>{user_msg}</div>"
html += f"<div class='resp_msg'>{resp_msg}</div>"
html += "</div>"
return html
iface = gr.Interface(
chat,
[
gr.inputs.Slider(minimum=0.5, maximum=1, step=0.05, default=0.9, label="top_p"),
gr.inputs.Slider(minimum=0.5, maximum=1.5, step=0.1, default=1.1, label="temperature"),
gr.inputs.Slider(minimum=20, maximum=250, step=10, default=50, label="max_new_tokens"),
"text",
],
"html", css="""
.chatbox {display:flex;flex-direction:column}
.user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
.user_msg {background-color:cornflowerblue;color:white;align-self:start}
.resp_msg {background-color:lightgray;align-self:self-end}
""", allow_screenshot=True,
allow_flagging=True,
title=title,
article=article,
theme=theme,
examples=examples)
if __name__ == "__main__":
iface.launch()
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