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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)
# Inference function
def generate_caption(image):
if image.mode != "RGB":
image = image.convert("RGB")
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)
return caption
# Gradio interface
iface = gr.Interface(
fn=generate_caption,
inputs=gr.Image(type="pil"),
outputs="text",
title="Construction Site Image-to-Text Generator",
description="Upload a site photo. The model will detect and describe construction activities."
)
iface.launch()
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