app.py

#!/usr/bin/env python3
from doctest import OutputChecker
import sys
import torch
import re
import os
import gradio as gr
import requests

# just for the sake of this demo, we use cloze prob to initialize the hypothesis 

#url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models"
#resp = requests.get(url)

from sentence_transformers import SentenceTransformer, util

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

#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
#tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')



tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model = GPT2LMHeadModel.from_pretrained('distilgpt2')

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()



def cloze_prob(text):

	whole_text_encoding = tokenizer.encode(text)
	text_list = text.split()
	stem = ' '.join(text_list[:-1])
	stem_encoding = tokenizer.encode(stem)
	cw_encoding = whole_text_encoding[len(stem_encoding):]
	tokens_tensor = torch.tensor([whole_text_encoding])
	
	with torch.no_grad():
		outputs = model(tokens_tensor)
		predictions = outputs[0]   

	logprobs = []
	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)))
			
	
	conditional_probs = []
	for cw,prob in zip(cw_encoding,logprobs):
			conditional_probs.append(prob[cw])


	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_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_man = cloze_prob(caption_man)
    #LM_woman = cloze_prob(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 (distilbert)",
    inputs=[gr.Textbox(value="a man riding a motorcycle on a road") , gr.Textbox(value="a woman riding a motorcycle on a road"), gr.Textbox(value="motor scooter"),  gr.Textbox(value="0.2183")],
  
  
   
    outputs="label",
)
demo.launch()