app.py

import gradio as gr
import yolov7
import subprocess
import tempfile
import time
from pathlib import Path
import uuid
import cv2
import gradio as gr


    
def image_fn(
    image: gr.inputs.Image = None,
    model_path: gr.inputs.Dropdown = None,
    image_size: gr.inputs.Slider = 640,
    conf_threshold: gr.inputs.Slider = 0.25,
    iou_threshold: gr.inputs.Slider = 0.45,
):
    """
    YOLOv7 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 = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
    model.conf = conf_threshold
    model.iou = iou_threshold
    results = model([image], size=image_size)
    return results.render()[0]
  
  
        

image_interface = gr.Interface(
    fn=image_fn,
    inputs=[
    gr.inputs.Image(type="pil", label="Input Image"),
    gr.inputs.Dropdown(
        choices=[
            "Aalaa/Yolov7_Visual_Pollution_Detection",
        ],
        default="Aalaa/Yolov7_Visual_Pollution_Detection",
        label="Model",
    )
    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
],
    outputs=gr.outputs.Image(type="filepath", label="Output Image"),
    #title="Visual P",
    #examples=[['image1.jpg', 'Aalaa/Yolov7_Visual_Pollution_Detection', 640, 0.25, 0.45], ['image2.jpg', 'Aalaa/Yolov7_Visual_Pollution_Detection', 640, 0.25, 0.45], ['image3.jpg', 'Aalaa/Yolov7_Visual_Pollution_Detection', 640, 0.25, 0.45]],
    #cache_examples=True,
    #theme='huggingface',
)



if __name__ == "__main__":
    gr.TabbedInterface(
        [image_interface],
        ["Run on Images"],
    ).launch()