from transformers import DetrImageProcessor, DetrForObjectDetection
from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
from PIL import Image
import requests
import gradio as gr

box_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
box_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")

def predict_bounding_boxes(imageurl:str):
    try:
        response = requests.get(imageurl, stream=True)
        response.raise_for_status() 

        image_data = Image.open(response.raw)
        inputs = box_processor(images=image_data, return_tensors="pt")
        outputs = box_model(**inputs)

        target_sizes = torch.tensor([image_data.size[::-1]])
        results = box_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.70)[0]

        detections = [{"score": score.item(), "label": box_model.config.id2label[label.item()], "box": box.tolist()} for score, label, box in zip(results["scores"], results["labels"], results["boxes"])]

        raw_image = image_data.convert('RGB')
        inputs = caption_processor(raw_image, return_tensors="pt")
        out = caption_model.generate(**inputs)
        label = caption_processor.decode(out[0], skip_special_tokens=True)
        return {"image label": label, "detections": detections}
    
    except Exception as e:
        
        return {"error": str(e)}

app = gr.Interface(fn=predict_bounding_boxes, inputs="text", outputs="json")
app.api = True
app.launch()