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from transformers import AutoModelForCausalLM, AutoTokenizer ,T5ForConditionalGeneration ,T5Tokenizer
import re
import torch
torch.set_default_tensor_type(torch.cuda.FloatTensor)
import os
import io
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
import gradio as gr
def generate_post(model,tokenizer,company_name , description , example1 ,example2 ,example3):
prompt = f""" {company_name} {description}, {example1}.
{company_name} {description}, {example2}.
{company_name} {description}, {example3}.
{company_name} {description}, """
input_ids = tokenizer(prompt, return_tensors="pt").to(0)
sample = model.generate(**input_ids, top_k=0, temperature=0.7, do_sample = True , max_new_tokens = 70, repetition_penalty= 5.4)
outputs = tokenizer.decode(sample[0])
res = outputs.split(f""" {company_name} {description}, {example1}.
{company_name} {description}, {example2}.
{company_name} {description}, {example3}.
{company_name} {description}, """)[1]
res = re.sub('[#]\w+' , " ", res)
res = re.sub('@[^\s]\w+',' ', res)
res = re.sub(r'http\S+', ' ', res)
res = res.replace("\n" ," ")
res = re.sub(' +', ' ',res)
return res
def generate_caption(model , text_body ,tokenizer ,max_length):
test_sent = 'generate: ' + text_body
input = tokenizer.encode(test_sent , return_tensors="pt")#.to('cuda')
outs = model.generate(input ,
max_length = max_length,
do_sample = True ,
temperature = 0.7,
min_length = 8,
repetition_penalty = 5.4,
max_time = 12,
top_p = 1.0,
top_k = 50)
sent = tokenizer.decode(outs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
return sent
def demo_smg(company_name ,description , example1 , example2 , example3):
access_token = "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"
model_cp= T5ForConditionalGeneration.from_pretrained("Abdelmageed95/caption_model" , use_auth_token = access_token )
tokenizer = T5Tokenizer.from_pretrained('t5-base')
model_bm = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m" , use_auth_token = access_token)
tokenizer_bm = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
res = generate_post( model_bm , tokenizer_bm, company_name , description , example1 , example2 , example3)
generated_caption = generate_caption( model_cp ,
res,
tokenizer ,
30)
os.environ['STABILITY_HOST'] = "grpc.stability.ai:443"
os.environ['STABILITY_KEY'] = "sk-t4x1wv6WFgTANF7O1TkWDJZzxXxQZeU6X7oybl6rdCOOiHIk"
stability_api = client.StabilityInference(
key=os.environ['STABILITY_KEY'],
verbose=True)
generated_caption = generated_caption + ", intricate, highly detailed, smooth , sharp focus, 8k"
answers = stability_api.generate( prompt= generated_caption ,
#seed=34567,
steps= 70 )
for resp in answers:
for artifact in resp.artifacts:
if artifact.finish_reason == generation.FILTER:
warnings.warn(
"Your request activated the API's safety filters and could not be processed."
"Please modify the prompt and try again.")
if artifact.type == generation.ARTIFACT_IMAGE:
img = Image.open(io.BytesIO(artifact.binary))
return res, generated_caption ,img
company_name = "ADES Group"
description = "delivers full-scale petroleum services; from onshore and offshore drilling to full oil & gas projects and services, with emphasis on the HSE culture while maintaining excellence in operation targets."
example1 = """Throwback to ADM 680 Team during their Cyber-chair controls Course in August,
Our development strategy at ADES does not only focus on enriching the technical expertise of our teams in their specialization in Jack- up rigs,
but also in providing access to latest operational models"""
example2 = """With complexity of oil & gas equipment and the seriousness of failure and its consequences confronting our people,
it has become a necessity to equip our Asset Management Team with leading methodologies and techniques that enable them to think and act proactively"""
example3 = """ Part of our people development strategy is providing our senior leadership with the latest industry technologies
and world class practices and standards"""
# txt , generated_caption , im = demo_smg( company_name, description , example1 , example2 , example3)
# print(txt)
# print(generated_caption)
demo = gr.Interface(
fn= demo_smg,
inputs=["text","text" , "text" ,"text" ,"text"],
outputs=["text", "text", "image" ]
)
demo.launch() |