# Motivation-Letter-Generator

from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, AutoModelForSeq2SeqLM, set_seed, pipeline
import gradio as gr

import torch
torch.set_default_tensor_type(torch.cuda.FloatTensor)

### need more GPU power to call better models !!!!!!

# model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", use_cache=True) # 11B param
# tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp")

model = AutoModelForCausalLM.from_pretrained('EleutherAI/gpt-neo-1.3B', use_cache=True)
tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neo-1.3B')

set_seed(424242)

def generate(Name, Employer, Position, Organization, Hard_skills, Soft_skills, max_length=500, top_k=1, temperature=0.9, repetition_penalty = 2.0):
  prompt = f'im {Name} and i want to write a motivation letter to {Employer} about the position {Position} at {Organization} mentioning the hard skills {Hard_skills} and soft skills {Soft_skills} you have acquired'
  input_ids = tokenizer(prompt, return_tensors="pt").to(0)
  sample = model.generate(**input_ids, max_length=max_length,  top_k=top_k, temperature=temperature, repetition_penalty = repetition_penalty)
  return tokenizer.decode(sample[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])

title = "Motivation Letter Generator w/ GPT-Neo-1.3B"
article = "Impress your employer"

gr = gr.Interface(fn=generate, inputs=["text", "text", "text", "text", "text", "text"], outputs="text", title=title, article=article, share=True)

gr.launch()