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()
    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="Cashew Disease Detection",
    examples=path,
    cache_examples=False,
)

interface_image.launch()