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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 Hassan Kane, an entrepreneur.
person: What are you working on?
Hassan: This new AI community building the future of Africa
person: Where are you?
Hassan: In Lagos for a week, then Paris or London.
person: How's it going?
Hassan: Not bad.. Just trying to hit EV (escape velocity) with my startup
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)
if "Hassan: " in result:
result = result.split("Hassan: ")[-1]
# 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 response
iface = gr.Interface(chat_base, gr.inputs.Textbox(label="Ask Hassan a Question"), "text", allow_screenshot=False, allow_flagging=False)
iface.launch()
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