app.py

#!/usr/bin/env python3
from doctest import OutputChecker
import sys
import torch
from transformers import *
import re
import os
import gradio as gr
import requests
from doctest import OutputChecker
import sys
import torch
import re
import os
import gradio as gr
import requests
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from torch.nn.functional import softmax
import numpy as np

from sentence_transformers import SentenceTransformer, util
#url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models"
#resp = requests.get(url)
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import T5Tokenizer, AutoModelForCausalLM


from arabert import ArabertPreprocessor
from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel

from transformers import AutoTokenizer, AutoModel
from arabert.preprocess import ArabertPreprocessor


from sentence_transformers import SentenceTransformer, util
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity
#from lm_scorer.models.auto import AutoLMScorer as LMScorer
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity


#model_sts = gr.Interface.load('huggingface/sentence-transformers/stsb-distilbert-base') 

#model_sts = SentenceTransformer('stsb-distilbert-base')
#model_sts = SentenceTransformer('roberta-large-nli-stsb-mean-tokens')
model_sts = SentenceTransformer('distiluse-base-multilingual-cased-v1')

#batch_size = 1
#scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)

#import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import numpy as np
import re




# def Sort_Tuple(tup):  
  
# 	# (Sorts in descending order)  
# 	tup.sort(key = lambda x: x[1])  
# 	return tup[::-1]


# def softmax(x):
# 	exps = np.exp(x)
# 	return np.divide(exps, np.sum(exps))


def get_sim(x):
    x =  str(x)[1:-1]
    x =  str(x)[1:-1]
    return x
 
	
# Load pre-trained model 

#model = GPT2LMHeadModel.from_pretrained('distilgpt2', output_hidden_states = True, output_attentions = True)
#model = GPT2LMHeadModel.from_pretrained('gpt2', output_hidden_states = True, output_attentions = True)
#model  =  gr.Interface.load('huggingface/distilgpt2', output_hidden_states = True, output_attentions = True)

#model.eval()
#tokenizer =  gr.Interface.load('huggingface/distilgpt2')
device = "mps" if torch.has_mps else "cpu"
#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')


model_name = "aubmindlab/aragpt2-base"
#model_name = "aubmindlab/aragpt2-medium"
arabert_prep = ArabertPreprocessor(model_name=model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = GPT2LMHeadModel.from_pretrained(model_name, output_hidden_states=True, output_attentions=True) 
tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
#model.eval()



# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# model = GPT2LMHeadModel.from_pretrained('gpt2')



def sentence_prob_mean(text):
    # Tokenize the input text and add special tokens
    input_ids = tokenizer.encode(text, return_tensors='pt')

    # Obtain model outputs
    with torch.no_grad():
        outputs = model(input_ids, labels=input_ids)
        logits = outputs.logits  # logits are the model outputs before applying softmax

    # Shift logits and labels so that tokens are aligned:
    shift_logits = logits[..., :-1, :].contiguous()
    shift_labels = input_ids[..., 1:].contiguous()

    # Calculate the softmax probabilities
    probs = softmax(shift_logits, dim=-1)

    # Gather the probabilities of the actual token IDs
    gathered_probs = torch.gather(probs, 2, shift_labels.unsqueeze(-1)).squeeze(-1)

    # Compute the mean probability across the tokens
    mean_prob = torch.mean(gathered_probs).item()

    return mean_prob




# def cloze_prob(text):

# 	whole_text_encoding = tokenizer.encode(text)
# 	# Parse out the stem of the whole sentence (i.e., the part leading up to but not including the critical word)
# 	text_list = text.split()
# 	stem = ' '.join(text_list[:-1])
# 	stem_encoding = tokenizer.encode(stem)
# 	# cw_encoding is just the difference between whole_text_encoding and stem_encoding
# 	# note: this might not correspond exactly to the word itself
# 	cw_encoding = whole_text_encoding[len(stem_encoding):]
# 	# Run the entire sentence through the model. Then go "back in time" to look at what the model predicted for each token, starting at the stem.
# 	# Put the whole text encoding into a tensor, and get the model's comprehensive output
# 	tokens_tensor = torch.tensor([whole_text_encoding])
	
# 	with torch.no_grad():
# 		outputs = model(tokens_tensor)
# 		predictions = outputs[0]   

# 	logprobs = []
# 	# start at the stem and get downstream probabilities incrementally from the model(see above)
# 	start = -1-len(cw_encoding)
# 	for j in range(start,-1,1):
# 			raw_output = []
# 			for i in predictions[-1][j]:
# 					raw_output.append(i.item())
	
# 			logprobs.append(np.log(softmax(raw_output)))
			
# 	# if the critical word is three tokens long, the raw_probabilities should look something like this:
# 	# [ [0.412, 0.001, ... ] ,[0.213, 0.004, ...], [0.002,0.001, 0.93 ...]]
# 	# Then for the i'th token we want to find its associated probability
# 	# this is just: raw_probabilities[i][token_index]
# 	conditional_probs = []
# 	for cw,prob in zip(cw_encoding,logprobs):
# 			conditional_probs.append(prob[cw])
# 	# now that you have all the relevant probabilities, return their product.
# 	# This is the probability of the critical word given the context before it.

# 	return np.exp(np.sum(conditional_probs))





def cos_sim(a, b):
    return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))


  
#def Visual_re_ranker(caption, visual_context_label, visual_context_prob):

def Visual_re_ranker(caption_man, caption_woman, context_label, context_prob):
    caption_man = caption_man  
    caption_woman = caption_woman
    context_label= context_label
    context_prob = context_prob
    caption_emb_man = model_sts.encode(caption_man, convert_to_tensor=True)
    caption_emb_woman = model_sts.encode(caption_woman, convert_to_tensor=True)
    context_label_emb = model_sts.encode(context_label, convert_to_tensor=True)

    sim_m =  cosine_scores = util.pytorch_cos_sim(caption_emb_man, context_label_emb)
    sim_m = sim_m.cpu().numpy()
    sim_m = get_sim(sim_m)

    sim_w = cosine_scores = util.pytorch_cos_sim(caption_emb_woman, context_label_emb) 
    sim_w = sim_w.cpu().numpy()
    sim_w = get_sim(sim_w)


    LM_man = sentence_prob_mean(caption_man)
    LM_woman = sentence_prob_mean(caption_woman)
    #LM  = scorer.sentence_score(caption, reduce="mean")
    score_man     = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(context_prob)))
    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(context_prob)))


    #return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
    return {"Man": float(score_man)/1, "Woman": float(score_woman)/1}
    #return LM, sim, score 




demo = gr.Interface(
    fn=Visual_re_ranker,
    description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender -Arabic",
    #inputs=[gr.Textbox(value="a man sitting on a surfboard in the ocean") , gr.Textbox(value="a woman sitting on a surfboard in the ocean"), gr.Textbox(value="paddle"),  gr.Textbox(value="0.5283")],
    
     inputs=[gr.Textbox(value="أول عربيي يقطع البحر الأحمر سباحة من السعودية إلى مصر") , gr.Textbox(value="أول عربية تقطع البحر الأحمر سباحة من السعودية إلى مصر"), gr.Textbox(value="سباحة"),  gr.Textbox(value="0.5374")],
    
    outputs="label",
)
demo.launch()