app.py

#!/usr/bin/env python3
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

# 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 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('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 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, visual_context_label, context_prob):
    caption_man = caption_man  
    caption_woman = caption_woman
    visual_context_label = visual_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(visual_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 (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()