app.py

import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
import random

def detect_objects(image):
    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    draw = ImageDraw.Draw(image)
    labels = []
    for i, (score, label, box) in enumerate(zip(results["scores"], results["labels"], results["boxes"])):
        box = [round(i, 2) for i in box.tolist()]
        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
        draw.rectangle(box, outline=color, width=3)
        label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}"
        draw.text((box[0], box[1]), label_text, fill=color,)
        labels.append(label_text)

    return image, labels

def upload_image(file):
    image = Image.open(file.name)
    image_with_boxes, labels = detect_objects(image)
    return image_with_boxes, labels

def show_labels(labels):
    return "\n".join(labels)

# Interface to display the image with bounding boxes
iface_objects = gr.Interface(
    fn=upload_image,
    inputs="file",
    outputs=["image", "text"],
    title="Object Detection",
    description="Upload an image and detect objects using DETR model.",
    allow_flagging=False
)

# Interface to display the detected labels
iface_labels = gr.Interface(
    fn=show_labels,
    inputs="text",
    outputs="text",
    title="Detected Labels",
    description="Displays the labels detected in the uploaded image.",
    allow_flagging=False
)

# Combine interfaces with a tapped interface
interface = gr.Interface(
    [iface_objects, iface_labels],
    inputs="text",
    outputs="text",
    title="Object Detection with Labels",
    description="Upload an image and view detected objects and labels.",
    allow_flagging=False
)

interface.launch()