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# %%
# %pip install gradio diffusers
# %%
import gradio as gr
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModelForCausalLM
import io
from diffusers import StableDiffusionPipeline
# %%
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load BLIP model and processor
model_name = "Salesforce/blip-image-captioning-large"
blip_processor = BlipProcessor.from_pretrained(model_name)
blip_model = BlipForConditionalGeneration.from_pretrained(model_name).to(device)
blip_model.config.vision_config.output_attentions = True
# Load Stable Diffusion model
diffusion_model_name = "CompVis/stable-diffusion-v1-4"
diffusion_pipeline = StableDiffusionPipeline.from_pretrained(diffusion_model_name).to(device)
# Load smol model
smol_model_name = "Michaelj1/INSTRUCT_smolLM2-360M-finetuned-wikitext2-raw-v1"
tokenizer = AutoTokenizer.from_pretrained(smol_model_name)
smol_model = AutoModelForCausalLM.from_pretrained(smol_model_name).to(device)
# %%
def generate_caption(image):
inputs = blip_processor(images=image, return_tensors="pt").to(device)
caption_ids = blip_model.generate(**inputs, max_new_tokens=50)
caption = blip_processor.decode(caption_ids[0], skip_special_tokens=True)
return caption, inputs
def generate_gradcam(image, inputs):
with torch.no_grad():
vision_outputs = blip_model.vision_model(**inputs)
attentions = vision_outputs.attentions
last_layer_attentions = attentions[-1]
avg_attention = last_layer_attentions.mean(dim=1)
cls_attention = avg_attention[:, 0, 1:]
num_patches = cls_attention.shape[-1]
grid_size = int(np.sqrt(num_patches))
attention_map = cls_attention.cpu().numpy().reshape(grid_size, grid_size)
attention_map = cv2.resize(attention_map, (image.size[0], image.size[1]))
attention_map = attention_map - np.min(attention_map)
attention_map = attention_map / np.max(attention_map)
img_np = np.array(image)
heatmap = cv2.applyColorMap(np.uint8(255 * attention_map), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
cam = heatmap + np.float32(img_np) / 255
cam = cam / np.max(cam)
cam_image = np.uint8(255 * cam)
return cam_image
def generate_image_from_caption(caption):
image = diffusion_pipeline(caption).images[0]
return image
def explain_word(word):
messages = [{"role": "user", "content": f"Explain the word '{word}' in detail."}]
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = smol_model.generate(
inputs,
max_new_tokens=150,
temperature=0.9,
top_p=0.95,
do_sample=True
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
lines = generated_text.split('\n')
assistant_response = []
collect = False
for line in lines:
line = line.strip()
if line.lower() == 'assistant':
collect = True
continue
elif line.lower() in ['system', 'user']:
collect = False
if collect and line:
assistant_response.append(line)
explanation = '\n'.join(assistant_response).strip()
return explanation
def get_caption_self_attention(caption):
text_inputs = blip_processor.tokenizer(
caption,
return_tensors="pt",
add_special_tokens=True
).to(device)
with torch.no_grad():
outputs = blip_model.text_decoder(
input_ids=text_inputs.input_ids,
attention_mask=text_inputs.attention_mask,
output_attentions=True,
return_dict=True,
)
decoder_attentions = outputs.attentions
return decoder_attentions, text_inputs
def generate_self_attention(decoder_attentions, text_inputs):
last_layer_attentions = decoder_attentions[-1]
avg_attentions = last_layer_attentions.mean(dim=1)
attentions = avg_attentions[0].cpu().numpy()
tokens = blip_processor.tokenizer.convert_ids_to_tokens(text_inputs.input_ids[0])
cls_token = blip_processor.tokenizer.cls_token or "[CLS]"
sep_token = blip_processor.tokenizer.sep_token or "[SEP]"
special_token_indices = [idx for idx, token in enumerate(tokens) if token in [cls_token, sep_token]]
mask = np.ones(len(tokens), dtype=bool)
mask[special_token_indices] = False
filtered_tokens = [token for idx, token in enumerate(tokens) if mask[idx]]
filtered_attentions = attentions[mask, :][:, mask]
return filtered_tokens, filtered_attentions
def process_image(image):
# Ensure input is in the correct format
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
caption, inputs = generate_caption(image)
cam_image = generate_gradcam(image, inputs)
diffusion_image = generate_image_from_caption(caption)
decoder_attentions, text_inputs = get_caption_self_attention(caption)
filtered_tokens, filtered_attentions = generate_self_attention(decoder_attentions, text_inputs)
# Create visualization grid
fig, axs = plt.subplots(2, 2, figsize=(18, 18))
axs[0][0].imshow(image)
axs[0][0].axis('off')
axs[0][0].set_title('Original Image')
axs[0][1].imshow(cam_image)
axs[0][1].axis('off')
axs[0][1].set_title('Grad-CAM Overlay')
axs[1][0].imshow(diffusion_image)
axs[1][0].axis('off')
axs[1][0].set_title('Generated Image (Stable Diffusion)')
ax = axs[1][1]
im = ax.imshow(filtered_attentions, cmap='viridis')
ax.set_xticks(range(len(filtered_tokens)))
ax.set_yticks(range(len(filtered_tokens)))
ax.set_xticklabels(filtered_tokens, rotation=90, fontsize=8)
ax.set_yticklabels(filtered_tokens, fontsize=8)
ax.set_title('Caption Self-Attention')
plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
plt.tight_layout()
# Save visualization to a buffer for display
buffer = io.BytesIO()
plt.savefig(buffer, format='png')
plt.close(fig)
buffer.seek(0)
visualization_image = Image.open(buffer)
# Generate word options for dropdown
words = caption.split()
return caption, visualization_image, gr.Dropdown(label="Select a Word from Caption", choices=words, interactive=True)
def get_word_explanation(word):
explanation = explain_word(word)
return f"Explanation for '{word}':\n\n{explanation}"
# %%
# Define Gradio interface
with gr.Blocks() as interface:
gr.Markdown("# Image Captioning and Visualization with Word Explanation")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload an Image")
process_button = gr.Button("Process Image")
with gr.Column():
caption_output = gr.Textbox(label="Generated Caption")
visualization_output = gr.Image(type="pil", label="Visualization (Original, Grad-CAM, Stable Diffusion)")
word_dropdown = gr.Dropdown(label="Select a Word from Caption", choices=[], interactive=True)
word_explanation = gr.Textbox(label="Word Explanation")
# Bind functions to components
process_button.click(
process_image,
inputs=image_input,
outputs=[caption_output, visualization_output, word_dropdown]
)
word_dropdown.change(
get_word_explanation,
inputs=word_dropdown,
outputs=word_explanation
)
# %%
# Launch the Gradio app
interface.launch()
# %%
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