import streamlit as st
import torch
import torchvision
import cv2
import numpy as np
import torch.nn as nn
from torchvision.ops import box_iou
from PIL import Image
import albumentations as A
from albumentations.pytorch import ToTensorV2

# apply nms algorithm
def apply_nms(orig_prediction, iou_thresh=0.3):
        # torchvision returns the indices of the bboxes to keep
        keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh)
        final_prediction = orig_prediction
        final_prediction['boxes'] = final_prediction['boxes'][keep]
        final_prediction['scores'] = final_prediction['scores'][keep]
        final_prediction['labels'] = final_prediction['labels'][keep]

        return final_prediction

# Draw the bounding box
def plot_img_bbox(img, target):
        h,w,c = img.shape
        for box in (target['boxes']):
            xmin, ymin, xmax, ymax  = int((box[0].cpu()/1024)*w), int((box[1].cpu()/1024)*h), int((box[2].cpu()/1024)*w),int((box[3].cpu()/1024)*h)
            cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
            label = "palm"
            # Add the label and confidence score
            label = f'{label}'
            cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

        # Display the image with detections
        filename = 'pred.jpg'
        cv2.imwrite(filename, img)

# transform image
test_transforms = A.Compose([
        A.Resize(height=1024, width=1024, always_apply=True),
        A.Normalize(always_apply=True),
        ToTensorV2(always_apply=True),])

# select device (whether GPU or CPU)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

# model loading
model = torch.load('pickel.pth',map_location=torch.device('cpu'))
model = model.to(device)

st.title("🌴Palm trees detection🌴")

file_name = st.file_uploader("Upload oil palm tree image")

if file_name is not None:
    col1, col2 = st.columns(2)

    image = np.array(Image.open(file_name))
    col1.image(image, use_column_width=True)
    transformed = test_transforms(image= image)
    image_transformed = transformed["image"]
    image_transformed = image_transformed.unsqueeze(0)
    image_transformed = image_transformed.to(device)
    # inference
    model.eval()
    with torch.no_grad():
        predictions = model(image_transformed)[0]

    nms_prediction = apply_nms(predictions, iou_thresh=0.1)

    plot_img_bbox(image, nms_prediction)
    pred = np.array(Image.open("pred.jpg"))
    col2.image(pred, use_column_width=True)
    word = "Number of palm trees detected : "+str(len(nms_prediction["boxes"]))
    st.write(word)