# main.py
import numpy as np
from flask import Flask, jsonify, request, render_template

import pyttsx3

import base64

import numpy as np
from io import BytesIO
from PIL import Image
import tensorflow as tf
import requests
import json
import os

from dotenv import load_dotenv

load_dotenv()


app = Flask(__name__)

upload_folder = os.path.join('static', 'img')

app.config['UPLOAD'] = upload_folder


HUGGINGFACEHUB_APT_TOKEN = os.getenv("HUGGINGFACEHUB_APT_TOKEN")

ANIMAL_API_TOKEN = os.getenv("ANIMAL_API_TOKEN")


Animal_danger_classification = {
    "Antelope": "Vulnerable",
    "Badger": "Least Concern",
    "Bat": "Vulnerable",
    "Bear": "Vulnerable",
    "Bee": "Least Concern",
    "Beetle": "Least Concern",
    "Bison": "Least Concern",
    "Boar": "Least Concern",
    "Butterfly": "Vulnerable",
    "Cat": "Least Concern",
    "Caterpillar": "Least Concern",
    "Chimpanzee": "Endangered",
    "Cockroach": "Least Concern",
    "Cow": "Least Concern",
    "Coyote": "Least Concern",
    "Crab": "Least Concern",
    "Crow": "Least Concern",
    "Deer": "Least Concern",
    "Dog": "Least Concern",
    "Dolphin": "Least Concern",
    "Donkey": "Least Concern",
    "Dragonfly": "Vulnerable",
    "Duck": "Least Concern",
    "Eagle": "Least Concern",
    "Elephant": "Endangered",
    "Flamingo": "Vulnerable",
    "Fly": "Least Concern",
    "Fox": "Least Concern",
    "Goat": "Least Concern",
    "Goldfish": "Least Concern",
    "Goose": "Least Concern",
    "Gorilla": "Endangered",
    "Grasshopper": "Least Concern",
    "Hamster": "Least Concern",
    "Hare": "Least Concern",
    "Hedgehog": "Least Concern",
    "Hippopotamus": "Vulnerable",
    "Hornbill": "Vulnerable",
    "Horse": "Least Concern",
    "Hummingbird": "Least Concern",
    "Hyena": "Least Concern",
    "Jellyfish": "Least Concern",
    "Kangaroo": "Least Concern",
    "Koala": "Vulnerable",
    "Ladybugs": "Least Concern",
    "Leopard": "Vulnerable",
    "Lion": "Vulnerable",
    "Lizard": "Least Concern",
    "Lobster": "Least Concern",
    "Mosquito": "Least Concern",
    "Moth": "Least Concern",
    "Mouse": "Least Concern",
    "Octopus": "Least Concern",
    "Okapi": "Endangered",
    "Orangutan": "Endangered",
    "Otter": "Vulnerable",
    "Owl": "Least Concern",
    "Ox": "Least Concern",
    "Oyster": "Least Concern",
    "Panda": "Endangered",
    "Parrot": "Least Concern",
    "Pelecaniformes": "Least Concern",
    "Penguin": "Vulnerable",
    "Pig": "Least Concern",
    "Pigeon": "Least Concern",
    "Porcupine": "Least Concern",
    "Possum": "Least Concern",
    "Raccoon": "Least Concern",
    "Rat": "Least Concern",
    "Reindeer": "Least Concern",
    "Rhinoceros": "Vulnerable",
    "Sandpiper": "Least Concern",
    "Seahorse": "Least Concern",
    "Seal": "Vulnerable",
    "Shark": "Least Concern",
    "Sheep": "Least Concern",
    "Snake": "Least Concern",
    "Sparrow": "Least Concern",
    "Squid": "Least Concern",
    "Squirrel": "Least Concern",
    "Starfish": "Least Concern",
    "Swan": "Least Concern",
    "Tiger": "Vulnerable",
    "Turkey": "Least Concern",
    "Turtle": "Vulnerable",
    "Whale": "Vulnerable",
    "Wolf": "Least Concern",
    "Wombat": "Least Concern",
    "Woodpecker": "Least Concern",
    "Zebra": "Least Concern"
}


ANIMAL_NAMES = ['Antelope',
                'Badger',
                'Bat',
                'Bear',
                'Bee',
                'Beetle',
                'Bison',
                'Boar',
                'Butterfly',
                'Cat',
                'Caterpillar',
                'Chimpanzee',
                'Cockroach',
                'Cow',
                'Coyote',
                'Crab',
                'Crow',
                'Deer',
                'Dog',
                'Dolphin',
                'Donkey',
                'Dragonfly',
                'Duck',
                'Eagle',
                'Elephant',
                'Flamingo',
                'Fly',
                'Fox',
                'Goat',
                'Goldfish',
                'Goose',
                'Gorilla',
                'Grasshopper',
                'Hamster',
                'Hare',
                'Hedgehog',
                'Hippopotamus',
                'Hornbill',
                'Horse',
                'Hummingbird',
                'Hyena',
                'Jellyfish',
                'Kangaroo',
                'Koala',
                'Ladybugs',
                'Leopard',
                'Lion',
                'Lizard',
                'Lobster',
                'Mosquito',
                'Moth',
                'Mouse',
                'Octopus',
                'Okapi',
                'Orangutan',
                'Otter',
                'Owl',
                'Ox',
                'Oyster',
                'Panda',
                'Parrot',
                'Pelecaniformes',
                'Penguin',
                'Pig',
                'Pigeon',
                'Porcupine',
                'Possum',
                'Raccoon',
                'Rat',
                'Reindeer',
                'Rhinoceros',
                'Sandpiper',
                'Seahorse',
                'Seal',
                'Shark',
                'Sheep',
                'Snake',
                'Sparrow',
                'Squid',
                'Squirrel',
                'Starfish',
                'Swan',
                'Tiger',
                'Turkey',
                'Turtle',
                'Whale',
                'Wolf',
                'Wombat',
                'Woodpecker',
                'Zebra']


MODEL = tf.keras.models.load_model("animal_classification_model.h5")


@app.route('/', methods=['GET'])
def home():
    return render_template("home.html")


@app.route('/generate_speech', methods=['POST'])
def generate_speech():
    information = request.form.get('information')
    print(information)

    # Generate speech using pyttsx3
    talk(information)

    return jsonify({"message": "Speech generated successfully"})


def read_file_as_image(data) -> np.ndarray:
    image = Image.open(BytesIO(data))
    return image


def animal_data(predicted_class):
    api_url = 'https://api.api-ninjas.com/v1/animals?name={}'.format(
        predicted_class)
    response = requests.get(
        api_url, headers={'X-Api-Key': ANIMAL_API_TOKEN})
    if response.status_code == requests.codes.ok:
        pass

    else:
        print("Error:", response.status_code, response.text)

    data = response.text
    dict = json.loads(data)

    data = response.text
    dict = json.loads(data)
    # print(type(dict), "knowing")
    data = [i for i in dict if i["name"].lower() ==
            predicted_class.lower()]

    return data[0]


def talk(text):
    engine = pyttsx3.init()
    newVoiceRate = 120
    engine.setProperty('rate', newVoiceRate)
    voices = engine.getProperty('voices')
    engine.setProperty('voice', voices[0].id)
    print("started speech")
    engine.say(text)
    engine.runAndWait()
    print("make speech stop")
    engine.stop()


@app.route("/predict", methods=["POST"])
def predict():
    if "file" not in request.files:
        return jsonify({"error": "No file provided"}), 400

    file = request.files["file"]

    if file.filename == "":
        return jsonify({"error": "No selected file"}), 400

    bytes = file.read()

    IMAGE_SIZE = (256, 256)

    # pass
    image = read_file_as_image(bytes)
    print(image, "hey")

    data = BytesIO()
    image.save(data, "JPEG")
    encoded_img_data = base64.b64encode(data.getvalue())

    if file:

        # Convert the file contents to a TensorFlow tensor

        img_array = tf.keras.preprocessing.image.img_to_array(image)
        img_array = tf.expand_dims(img_array, 0)

        # resized_image.shape
        resized_image = tf.image.resize(img_array, IMAGE_SIZE)

        # model prediction
        predictions = MODEL.predict(resized_image)

        # processing predicted output to give valid result
        predicted_class = ANIMAL_NAMES[np.argmax(predictions[0])]
        confidence = round(100 * (np.max(predictions[0])), 2)

        print(predicted_class)

        data = animal_data(predicted_class)
        classification = Animal_danger_classification[predicted_class]
        # print(data)
        # print(type(data))
        information = f'It is a {predicted_class} with {confidence} percent accuracy, It belongs to {data["taxonomy"]["kingdom"]} kingdom and {data["taxonomy"]["family"]} family , It can be found in {data["locations"]}, Its lifespan is mostly {data["characteristics"]["lifespan"]}, skin type {data["characteristics"]["skin_type"]} , and it is a {data["characteristics"]["diet"]}, An interesting fact about {predicted_class}: {data["characteristics"]["slogan"]} by danger classification of extinction it is {classification}'

        return render_template("index.html", information=information,  img_data=encoded_img_data.decode('utf-8'),  classes=data["taxonomy"]["class"], family=data["taxonomy"]["family"], kingdom=data["taxonomy"]["kingdom"], locations=data["locations"], lifespan=data["characteristics"]["lifespan"], skin_type=data["characteristics"]["skin_type"], diet=data["characteristics"]["diet"], fun_fact=data["characteristics"]["slogan"], accuracy=confidence, name=predicted_class, classification=classification)

        # return jsonify({
        #     "class": predicted_class,
        #     "confidence": float(confidence),
        #     "response": data,
        #     "Danger Classification": classification
        # })


if __name__ == '__main__':
    app.run(host="0.0.0.0", debug=True)

'''
These are some of the animals whose exact data is not available in the api

{'name': 'boar', 'present': 0}
{'name': 'cat', 'present': 0}
{'name': 'dog', 'present': 0}
{'name': 'ladybugs', 'present': 0}
{'name': 'orangutan', 'present': 0}
{'name': 'panda', 'present': 0}
{'name': 'pelecaniformes', 'present': 0}
{'name': 'sandpiper', 'present': 0}
{'name': 'turtle', 'present': 0}
{'name': 'whale', 'present': 0}
'''