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import torch
import pandas as pd
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification, TrainingArguments, Trainer
import gradio as gr
from gradio.mix import Parallel, Series
#import torch.nn.functional as F
from aitextgen import aitextgen
#IMPLEMENT FINE TUNING
#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#from datasets import load_dataset
#dataset = load_dataset("bananabot/engMollywoodSummaries")
#tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
#tokenizer.pad_token = tokenizer.eos_token
#def tokenize_function(examples):
# return tokenizer(examples["text"], padding="max_length", truncation=True)
#tokenized_datasets = dataset.map(tokenize_function, batched=True)
#model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B").to(device)
#training_args = TrainingArguments(output_dir="test_trainer")
#small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
#small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
#def compute_metrics(eval_pred):
# logits, labels = eval_pred
# predictions = np.argmax(logits, axis=-1)
# return metric.compute(predictions=predictions, references=labels)
#trainer = Trainer(
# model=model,
# args=training_args,
# train_dataset=small_train_dataset,
# eval_dataset=small_eval_dataset,
# compute_metrics=compute_metrics,
#)
#trainer.train()
ai = aitextgen(model="EleutherAI/gpt-neo-125M")
#model_name = "EleutherAI/gpt-neo-125M"
#tokenizer = AutoTokenizer.from_pretrained(model_name)
#max_length=123
#input_txt = "This malayalam movie is about"
#n_steps = 8
#input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device)
#output = model.generate(input_ids, max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37, top_k=69, top_p=0.96)
#print(tokenizer.decode(output[0]))
#def generate(input_txt):
# output = model.generate(input_ids, max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37, top_k=69, top_p=0.96)
# print (output)
def ai_text(inp):
generated_text = ai.generate_one(max_length=234, prompt = inp, no_repeat_ngram_size=3, num_beams=7, do_sample=True, temperature=1.37, top_k=69, top_p=0.96)
print(type(generated_text))
return generated_text
#inputs= gr.inputs.Textbox(lines=7, placeholder="Enter the beginning of your mollywood movie idea and the നിർമ്മിത ബുദ്ധി will fill in the rest...")
#IMPLEMENT KURIAN BENOY
#generator = output
#translator = gr.Interface.load("models/Helsinki-NLP/opus-mt-en-ml")
#gr.Series(generator, translator, inputs=gr.inputs.Textbox(lines=7, label="Input Text")).launch() # this demo generates text, then translates it to Malayalam, and outputs the final result.
#interface = gr.Interface(fn=generate,
# inputs=inputs,
# outputs='text',
# title='AI based Mollywood movie idea generator')
#interface.launch()
output_text = gr.outputs.Textbox()
gr.Interface(ai_text,"textbox", output_text, title="AI based Mollywood movie idea generator",
description="Enter the beginning of your malayalam movie idea and the നിർമ്മിത ബുദ്ധി will fill in the rest...").launch() |