Delete app.py

app.py

@@ -1,160 +0,0 @@
-#!/usr/bin/env python3
-from doctest import OutputChecker
-import sys
-#import torch
-import re
-import os
-import gradio as gr
-import requests
-
-#url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models"
-#resp = requests.get(url)
-
-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')
-
-#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  =  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')
-
-
-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, visual_context_label, visual_context_prob):
-    caption_man = caption_man  
-    caption_woman = caption_woman
-    visual_context_label= visual_context_label
-    visual_context_prob = visual_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)
-    visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)
-
-    sim_m =  cosine_scores = util.pytorch_cos_sim(caption_emb_man, visual_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, visual_context_label_emb) 
-    sim_w = sim_w.cpu().numpy()
-    sim_w = get_sim(sim_w)
-
-
-    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(visual_context_prob)))
-    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(visual_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",
-    inputs=[gr.Textbox(value="a man sitting on a surfboard in the ocean") , gr.Textbox(value="a man sitting on a surfboard in the ocean"), gr.Textbox(value="paddle"),  gr.Textbox(value="0.5283")],
-    #outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"),  gr.Textbox(value="Belief revision score via visual context")],
-    outputs="label",
-)
-demo.launch()
-