Update app.py

app.py

@@ -7,7 +7,7 @@ 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
 
@@ -16,19 +16,11 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 from huggingface_hub import login
 
-# 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
@@ -40,21 +32,6 @@ def get_sim(x):
     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')
-
-
 import os
 #print(os.getenv('HF_token'))
 hf_api_token = os.getenv("HF_token")  # For sensitive secrets
@@ -69,9 +46,6 @@ 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):
@@ -144,7 +118,7 @@ def Visual_re_ranker(caption_man, caption_woman, visual_context_label, context_p
 
 demo = gr.Interface(
     fn=Visual_re_ranker,
-    description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender (distilbert)",
+    description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender (LLAMA-3.2-1B with 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")],