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(source=image_path)
    # results = outputs[0].cpu().numpy()
    results = model(image_path)
    results.xyxy[0]  # img1 predictions (tensor)
    results.pandas().xyxy[0]  # img1 predictions (pandas)
    predictions = results.pred[0]
    boxes = predictions[:, :4] # x1, y1, x2, y2
    scores = predictions[:, 4]
    categories = predictions[:, 5]

    # 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 results.show()



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

interface_image.launch()