Update app.py

app.py

@@ -4,7 +4,6 @@ import os
 from huggingface_hub import list_liked_repos
 from diffusers import DiffusionPipeline
 import torch
-from transformers import AutoProcessor, Blip2ForConditionalGeneration
 
 def image_mod(prompt: str, model: str, image_0: gr.Image, image_1: gr.Image) -> list[gr.Image]:
     images = [image_0, image_1]
@@ -44,28 +43,6 @@ def change_model(choice):
     return images
 
 
-def give_dei_score(image_0: gr.Image, image_1: gr.Image):
-    textbox = []
-    
-    processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
-    model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-
-    question = "Does this image present any racial or gender prejudices?"
-    prompt = f"Question: {question} Answer:"
-
-    images = [image_0, image_1]
-    for i, image in enumerate(images):
-        image_to_analyze = images[i]
-        inputs = processor(image_to_analyze, text=prompt, return_tensors="pt").to(device, torch.float16)
-        generated_ids = model.generate(**inputs, max_new_tokens=10)
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-        textbox.append(generated_text)
-    
-    return textbox
-
-
 with gr.Blocks() as demo:
     gr.Markdown(
     """
@@ -94,12 +71,4 @@ with gr.Blocks() as demo:
         image_mod, [textbox, dropdown, image_0, image_1], [image_0, image_1]
     )
 
-    
-    button = gr.Button()
-    image_0_textbox = gr.Textbox(label="Image 1")
-    image_1_textbox = gr.Textbox(label="Image 2")
-    button.click(
-        give_dei_score, [image_0, image_1], [image_0_textbox, image_1_textbox]
-    )
-
 demo.launch()