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from transformers import BlipProcessor, BlipForConditionalGeneration | |
from PIL import Image | |
import gradio as gr | |
import torch | |
# Load BLIP model and processor | |
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
model.eval() | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model.to(device) | |
# List of construction-related terms | |
construction_terms = [ | |
"concrete", "scaffolding", "steel rods", "piling", "excavation", | |
"mixer", "cement", "brickwork", "crane", "rebar", "construction", | |
"foundation", "building", "formwork", "drywall", "steel beams", | |
"hammer", "saw", "nails", "jackhammer" | |
] | |
# Inference function | |
def generate_captions(images): | |
captions = [] | |
for image in images: | |
if image.mode != "RGB": | |
image = image.convert("RGB") | |
# Preprocess the image and generate a caption | |
inputs = processor(image, return_tensors="pt").to(device, torch.float16) | |
output = model.generate(**inputs, max_new_tokens=50) | |
caption = processor.decode(output[0], skip_special_tokens=True) | |
# Filter the caption to only include construction-related terms | |
filtered_caption = " ".join([word for word in caption.split() if word.lower() in construction_terms]) | |
# If no construction-related terms are found, return a default message | |
if not filtered_caption: | |
filtered_caption = "No construction-related activities detected." | |
captions.append(filtered_caption) | |
return captions | |
# Gradio interface | |
iface = gr.Interface( | |
fn=generate_captions, | |
inputs=gr.Image(type="pil", label="Upload Site Photos", multiple=True), # Handle batch upload of up to 10 images | |
outputs="text", | |
title="Construction Site Image-to-Text Generator", | |
description="Upload up to 10 site photos. The model will detect and describe construction activities and materials (e.g., concrete pouring, scaffolding, steel rods).", | |
allow_flagging="never" # Disable flagging (optional) | |
) | |
iface.launch() | |