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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)
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