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from transformers import GPTNeoForCausalLM, GPT2Tokenizer | |
import gradio as gr | |
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M") | |
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-125M") | |
prompt = """This is a discussion between a person and an entrepreneur. | |
person: What is your name? | |
entrepreneur: Mr. Patel | |
person: Where are you working? | |
entrepreneur: It's like one of these fancy adjustable height desk | |
person: What will you work on? | |
entrepreneur: The international development hackathon | |
person: What are you working on? | |
entrepreneur: Developping an classification web app | |
person: """ | |
def my_split(s, seps): | |
res = [s] | |
for sep in seps: | |
s, res = res, [] | |
for seq in s: | |
res += seq.split(sep) | |
return res | |
# input = "Who are you?" | |
def chat_base(input): | |
p = prompt + input | |
input_ids = tokenizer(p, return_tensors="pt").input_ids | |
gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.7, max_length=150,) | |
gen_text = tokenizer.batch_decode(gen_tokens)[0] | |
# print(gen_text) | |
result = gen_text[len(p):] | |
# print(">", result) | |
result = my_split(result, [']', '\n'])[1] | |
# print(">>", result) | |
result = result[14:] | |
# print(">>>", result) | |
return result | |
import gradio as gr | |
def chat(message): | |
history = gr.get_state() or [] | |
print(history) | |
response = chat_base(message) | |
history.append((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.Textbox(label="Ask Hassan a Question"), "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=False, allow_flagging=False) | |
iface.launch() |