app.py

# !pip install timm
# !pip install transformers
# !pip install pillow
from PIL import Image 
import gradio as gr

# Use a pipeline as a high-level helper


# object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")

# raw_image = Image .open("/content/dogwithman.jpg")

# output = object_detector(raw_image)
# print(output)


from PIL import Image
import gradio as gr
from transformers import pipeline

# Initialize the object detection pipeline
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")

def detect_objects(image):
    # Perform object detection on the input image
    results = object_detector(image)
    
    # Prepare the output string
    output = []
    for result in results:
        label = result['label']
        score = result['score']
        box = result['box']
        output.append(f"Label: {label}, Score: {score:.2f}, Box: {box}")
    
    return "\n".join(output)

# Create the Gradio interface
interface = gr.Interface(
    fn=detect_objects, 
    inputs=gr.Image(type="pil"), 
    outputs="text",
    title="Image Object Detector",
    description="Upload an image to detect objects using the DETR model."
)
 

# Launch the app
interface.launch()