import gradio as gr
import torch
import cv2
import os
import torch.nn as nn
import numpy as np
import torchvision
from torchvision.ops import box_iou
from PIL import Image
import albumentations as A
from albumentations.pytorch import ToTensorV2

from timeit import default_timer as timer
from typing import Tuple, Dict

# 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)
        return 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)

#-> Tuple[Dict, float]
def predict(img) :
    
    # Start a timer
    start_time = timer()
    
    # Transform the target image and add a batch dimension
    #image_transformed = test_transforms()
    transformed = test_transforms(image= np.array(img))
    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)

    pred = plot_img_bbox(np.array(img), nms_prediction)

    #pred = np.array(Image.open("pred.jpg"))
    word = "Number of palm trees detected : "+str(len(nms_prediction["boxes"]))
    
    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)
    
    # Return the prediction dictionary and prediction time 
    return pred,word
    
image = gr.components.Image()
out_im = gr.components.Image()
out_lab = gr.components.Label()

### 4. Gradio app ###
# Create title, description and article strings
title = "🌴Palm trees detection🌴"
description = "Faster r-cnn model to detect oil palm trees in drones images."
article = "Created by data354."

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
#[gr.Label(label="Predictions"), # what are the outputs?
#gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
# Create examples list from "examples/" directory
# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
                    inputs= image, #gr.Image(type="pil"), # what are the inputs?
                    outputs=[out_im,out_lab],
                    examples=example_list, 
                    title=title,
                    description=description,
                    article=article
                   )
# Launch the demo!
demo.launch(debug = False)