app.py

import gradio as gr
import torch
import yolov5
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="PranomVignesh/Police-vs-Public")

# from transformers import AutoFeatureExtractor, AutoModelForImageClassification

# extractor = AutoFeatureExtractor.from_pretrained("PranomVignesh/Police-vs-Public")
# model = AutoModelForImageClassification.from_pretrained("PranomVignesh/Police-vs-Public")

# Images
# torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
# torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')

def yolov5_inference(
    image
):
    """
    YOLOv5 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """
    model = yolov5.load('./best.pt', device="cpu")
    results = model([image], size=224)

    # outputs = model(**inputs)
    # logits = outputs.logits
    # probabilities = torch.softmax(logits, dim=1).tolist()[0]

    # classes = ['Police/Authorized Personnel', 'Public/Unauthorized Person']

    # output = {name: float(prob) for name, prob in zip(classes, probabilities)}

    probabilities = pipeline(image)
    output = {p["label"]: p["score"] for p in probabilities}

    return results.render()[0],output
        

inputs = gr.Image(type="pil")
outputs = [
    gr.Image(type="pil"),
    gr.Label()
]
title = "Detection"
description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."

# examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]]
demo_app = gr.Interface(
    fn=yolov5_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    # examples=examples,
    # cache_examples=True,
    # live=True,
    # theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)