app.py

import gradio as gr
import cv2
import requests
import os

import torch
import ultralytics
 

model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7_best.pt",
                       force_reload=False) 
model.conf = 0.20  # NMS confidence threshold

path  = [['img/test-image.jpg'], ['img/test-image-2.jpg']]

# def show_preds_image(image_path):
#     image = cv2.imread(image_path)
#     outputs = model.predict(source=image_path)
#     results = outputs[0].cpu().numpy()
#     for i, det in enumerate(results.boxes.xyxy):
#         cv2.rectangle(
#             image,
#             (int(det[0]), int(det[1])),
#             (int(det[2]), int(det[3])),
#             color=(0, 0, 255),
#             thickness=2,
#             lineType=cv2.LINE_AA
#         )
#     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

def show_preds_image(image_path):
    # perform inference
    image_path = path
    results = model(image_path, size=640)
    # Results
    results.print()

    results.xyxy[0]  # img1 predictions (tensor)
    results.pandas().xyxy[0]  # img1 predictions (pandas)

    # parse results
    predictions = results.pred[0]
    boxes = predictions[:, :4] # x1, y1, x2, y2
    scores = predictions[:, 4]
    categories = predictions[:, 5]

    return results.show()

    

inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Pothole detector",
    examples=path,
    cache_examples=False,
)

interface_image.launch()