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import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
# Load BLIP model for captioning
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
# Load DETR model for object detection (Detectron)
detr_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
detr_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
# List of objects for dynamic description
objects_of_interest = ["tree", "water", "mountain", "beach"]
def generate_caption(image):
# Process the image for caption generation
inputs = processor(images=image, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
# Object Detection: Detect objects in the image
inputs = detr_processor(images=image, return_tensors="pt")
outputs = detr_model(**inputs)
# Get detected objects and their labels
target_sizes = torch.tensor([image.size[::-1]])
results = detr_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
detected_objects = []
for score, label in zip(results["scores"], results["labels"]):
if label.item() == 23: # label for "tree"
detected_objects.append("trees")
if label.item() == 8: # label for "water"
detected_objects.append("water")
if label.item() == 72: # label for "mountain"
detected_objects.append("mountains")
# Custom dynamic description based on detected objects
description = "This image includes "
if detected_objects:
description += ", ".join(detected_objects)
else:
description += "various elements of nature."
description += ". It provides a beautiful view that invites relaxation and exploration."
return caption + "\n" + description
# Gradio Interface
iface = gr.Interface(fn=generate_caption,
inputs=gr.Image(type="pil"),
outputs=gr.Textbox(),
title="Dynamic Image Caption Generator",
description="Upload any image and get a detailed description of its contents.")
if __name__ == "__main__":
iface.launch()
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