from fastai.vision.all import *
from io import BytesIO
import requests
import streamlit as st
import os
import random
import numpy as np
import torch
import time
import cv2
from numpy import random
from models.experimental import attempt_load
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box

# Function to randomly choose a default image
def choose_default_image():
    default_images_path = "./default_images"  # Path to folder containing default images
    default_images = os.listdir(default_images_path)
    default_image_path = os.path.join(default_images_path, random.choice(default_images))
    return default_image_path

# Function to detect on default image
def detect_default_image(model, conf=0.4, imgsz=640, conf_thres=0.25, iou_thres=0.45):
    default_image_path = choose_default_image()
    img = PILImage.create(default_image_path)
    detect_modify(img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)

    
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
    # Resize and pad image while meeting stride-multiple constraints
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    if not scaleup:  # only scale down, do not scale up (for better test mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = (new_shape[1], new_shape[0])
        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return img, ratio, (dw, dh)

def detect_modify(img0, model, conf=0.4, imgsz=640, conf_thres = 0.25, iou_thres=0.45):
    st.image(img0, caption="Your image", use_column_width=True)

    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size

    # Padded resize
    img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_RGB2BGR)
    img = letterbox(img0, imgsz, stride=stride)[0]
    # Convert
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
    img = np.ascontiguousarray(img)

    
    
    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    old_img_w = old_img_h = imgsz
    old_img_b = 1

    t0 = time.time()
    img = torch.from_numpy(img).to(device)
    # img /= 255.0  # 0 - 255 to 0.0 - 1.0
    img = img/255.0
    if img.ndimension() == 3:
        img = img.unsqueeze(0)

    # Inference
    # t1 = time_synchronized()
    with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
        pred = model(img)[0]
    # t2 = time_synchronized()

    # Apply NMS
    pred = non_max_suppression(pred, conf_thres, iou_thres)
    # t3 = time_synchronized()

    # Process detections
    # for i, det in enumerate(pred):  # detections per image
        
    gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]  # normalization gain whwh

    det = pred[0]
    if len(det):
        # Rescale boxes from img_size to im0 size
        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()

        # Print results
        s = ''
        for c in det[:, -1].unique():
            n = (det[:, -1] == c).sum()  # detections per class
            s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

        # Write results
        for *xyxy, conf, cls in reversed(det):
            label = f'{names[int(cls)]} {conf:.2f}'
            plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=1)

    f"""
    ### Prediction result:
    """
    img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_BGR2RGB)
    st.image(img0, caption="Prediction Result", use_column_width=True)

#set paramters
weight_path = './best.pt'
imgsz = 640
conf = 0.4
conf_thres = 0.25
iou_thres=0.45
device = torch.device("cpu")
path = "./"

# Load model
model = attempt_load(weight_path, map_location=torch.device('cpu'))  # load FP32 model

"""
# Rat Detection using YOLOv7
Rats are common pests in urban and rural environments, posing threats to public health and causing damage to property. Effective rat detection is crucial for pest control and management. However, manual rat detection can be time-consuming and labor-intensive. Therefore, we developed an object detection model using YOLOv7 specifically tailored for rat detection. This model aims to automate the process of rat detection, making it faster, more efficient, and accessible.

Usage Instructions:
1.Upload Image: Users can upload their own images containing rats for detection.

2.Image URL: Users can input the URL of an image containing rats for detection.

3.Use Random Default Image: Users can select a default image provided by the system for detection. The system will randomly choose one of the default images and perform detection on it.

Upon selecting an option, the model will perform rat detection on the chosen image and display the results, including bounding boxes around detected rats. Users can then analyze the results to identify rat presence in the image.
"""
# Modify Streamlit app code
option = st.radio("", ["Upload Image", "Image URL", "Use Random Default Image"])

if option == "Upload Image":
    uploaded_file = st.file_uploader("Please upload an image.")
    if uploaded_file is not None:
        img = PILImage.create(uploaded_file)
        detect_modify(img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
elif option == "Image URL":
    url = st.text_input("Please input a URL.")
    if url != "":
        try:
            response = requests.get(url)
            pil_img = PILImage.create(BytesIO(response.content))
            detect_modify(pil_img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
        except:
            st.text("Problem reading image from", url)
else:
    detect_default_image(model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)