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#!/usr/bin/env python3
from doctest import OutputChecker
import sys
import torch
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 huggingface_hub import login
#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
print(os.getenv('HF_token'))
hf_api_token = os.getenv("HF_token") # For sensitive secrets
#app_mode = os.getenv("APP_MODE") # For public variables
access_token = hf_api_token
#print(login(token = access_token))
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
#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_G, caption_B, caption_VR, visual_context_label, visual_context_prob):
caption_G = caption_G
caption_B = caption_B
caption_VR = caption_VR
visual_context_label= visual_context_label
visual_context_prob = visual_context_prob
caption_emb_G = model_sts.encode(caption_G, convert_to_tensor=True)
caption_emb_B = model_sts.encode(caption_B, convert_to_tensor=True)
caption_emb_VR = model_sts.encode(caption_VR, convert_to_tensor=True)
visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)
sim_1 = cosine_scores = util.pytorch_cos_sim(caption_emb_G, visual_context_label_emb)
sim_1 = sim_1.cpu().numpy()
sim_1 = get_sim(sim_1)
sim_2 = cosine_scores = util.pytorch_cos_sim(caption_emb_B, visual_context_label_emb)
sim_2 = sim_2.cpu().numpy()
sim_2 = get_sim(sim_2)
sim_3 = cosine_scores = util.pytorch_cos_sim(caption_emb_VR, visual_context_label_emb)
sim_3 = sim_3.cpu().numpy()
sim_3 = get_sim(sim_3)
LM_1 = sentence_prob_mean(caption_G)
LM_2 = sentence_prob_mean(caption_B)
LM_3 = sentence_prob_mean(caption_VR)
#LM = scorer.sentence_score(caption, reduce="mean")
score_1 = pow(float(LM_1),pow((1-float(sim_1))/(1+ float(sim_1)),1-float(visual_context_prob)))
score_2 = pow(float(LM_2),pow((1-float(sim_2))/(1+ float(sim_2)),1-float(visual_context_prob)))
score_3 = pow(float(LM_3),pow((1-float(sim_3))/(1+ float(sim_3)),1-float(visual_context_prob)))
#return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
return {"Greedy": float(score_1)/1, "Best-Beam-5": float(score_2)/1, "Visual_re-Ranker": float(score_3)/1 }
#return LM, sim, score
demo = gr.Interface(
fn=Visual_re_ranker,
#description="Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information",
description="Demo for Caption Re-ranker with Visual Semantic Information",
#inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"), gr.Textbox(value="0.7458009")],
# a baby is eating in front of a birthday cake /a baby sitting in front of a giant cake
inputs=[gr.Textbox(value="baby is eating in front of a birthday cake") , gr.Textbox(value="a baby sitting in front of a cake"), gr.Textbox(value="a baby sitting in front of a birthday cake"), gr.Textbox(value="candle wax light"), gr.Textbox(value="0.958")],
#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() |