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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)
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