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from transformers import AutoTokenizer, AutoModelForCausalLM | |
import gradio as gr | |
import torch | |
#from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
#import pickle | |
title = "🤖Deployment GUVI GPT Model using Hugging Face" | |
description = "Building open-domain chatbots is a challenging area for machine learning research." | |
examples = [["Guvi Details"]] | |
model_name = "fine_tuned_model123" | |
#model = GPT2LMHeadModel.from_pretrained(model_name) | |
#tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
# Load the tokenizer and model from Hugging Face Hub | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
model = AutoModelForCausalLM.from_pretrained(model_name) | |
def predict(input, history=[]): | |
# tokenize the new input sentence | |
new_user_input_ids = tokenizer.encode( | |
input + tokenizer.eos_token, return_tensors="pt" | |
) | |
# append the new user input tokens to the chat history | |
bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
# generate a response | |
history = model.generate( | |
bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id | |
).tolist() | |
# convert the tokens to text, and then split the responses into lines | |
response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
# print('decoded_response-->>'+str(response)) | |
response = [ | |
(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2) | |
] # convert to tuples of list | |
# print('response-->>'+str(response)) | |
return response, history | |
gr.Interface( | |
fn=predict, | |
title=title, | |
description=description, | |
examples=examples, | |
inputs=["text", "state"], | |
outputs=["chatbot", "state"], | |
theme="finlaymacklon/boxy_violet", | |
).launch(share=True) | |