# server.py
import uvicorn
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
import numpy as np
from fastapi.middleware.cors import CORSMiddleware
import tensorflow as tf
from PIL import Image
from io import BytesIO
from fastapi import Request

# Initializing The App
app = FastAPI()

# Secure Our APP Server
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Mounting
app.mount("/static", StaticFiles(directory="frontend/static"), name="static")
app.mount("/models/yoga_pose/static", StaticFiles(directory='models/yoga_pose/static'), name='static_yoga_pose')
app.mount("/models/weather/static", StaticFiles(directory='models/weather/static'), name='static_weather')
app.mount("/models/sports_ball/static", StaticFiles(directory='models/sports_ball/static'), name='static_sports_ball')
app.mount("/models/mammals/static", StaticFiles(directory='models/mammals/static'), name='static_mammals')
app.mount("/models/flower/static", StaticFiles(directory='models/flower/static'), name='static_flower')
app.mount("/models/card/static", StaticFiles(directory='models/card/static'), name='static_card')
app.mount("/models/dog_breed/static", StaticFiles(directory='models/dog_breed/static'), name='static_dog_breed')
app.mount("/models/chess/static", StaticFiles(directory='models/chess/static'), name='static_chess')
app.mount("/models/bird/static", StaticFiles(directory='models/bird/static'), name='static_bird')

# templates
templates = Jinja2Templates(directory="frontend")
templates1 = Jinja2Templates(directory="models")

# DL or Ml Models (Loading)..
sports_ball_model = tf.keras.models.load_model('models/sports_ball/Sports_ball_prediction_v2.h5')
weather_model = tf.keras.models.load_model('models/weather/weather_prediction_v2.h5')
flower_model = tf.keras.models.load_model('models/flower/flower_prediction.h5')
yoga_pose_model = tf.keras.models.load_model('models/yoga_pose/yoga-modelv2.h5')
mammals_model = tf.keras.models.load_model('models/mammals/Mammals_predictionv1.h5')
card_model = tf.keras.models.load_model('models/card/card_model_v2.h5')
dog_breed_model = tf.keras.models.load_model("models/dog_breed/dog_breedv3.h5")
chess_model = tf.keras.models.load_model("models/chess/chess_prediction_v4.h5")
bird_model = tf.keras.models.load_model("models/bird/bird_modelV2.h5")

# classes For All Models
yoga_class = ['Bridge Pose', 'Child-Pose', 'CobraPose',
              'Downward Dog pose', 'Pigeon pose', 'Standing Mountain Pose',
              'Tree Pose', 'Triangle Pose', 'Warrior Pose']

sports_ball_class = ['american_football', 'baseball', 'basketball', 'billiard_ball', 'bowling_ball', 'cricket_ball',
                     'football', 'golf_ball', 'hockey_ball', 'hockey_puck', 'rugby_ball', 'shuttlecock',
                     'table_tennis_ball', 'tennis_ball', 'volleyball']

flower_class = ['astilbe', 'bellflower', 'black_eyed_susan', 'calendula', 'california_poppy', 'carnation',
                'common_daisy', 'coreopsis', 'dandelion', 'iris', 'rose', 'sunflower', 'tulip', 'water_lily']

mammals_class = ['african_elephant', 'alpaca', 'american_bison', 'anteater', 'arctic_fox', 'armadillo', 'baboon',
                 'badger', 'blue_whale', 'brown_bear', 'camel', 'dolphin', 'giraffe', 'groundhog', 'highland_cattle',
                 'horse', 'jackal', 'kangaroo', 'koala', 'manatee', 'mongoose', 'mountain_goat', 'opossum', 'orangutan',
                 'otter', 'polar_bear', 'porcupine', 'red_panda', 'rhinoceros', 'sea_lion', 'seal', 'snow_leopard',
                 'squirrel', 'sugar_glider', 'tapir', 'vampire_bat', 'vicuna', 'walrus', 'warthog', 'water_buffalo',
                 'weasel', 'wildebeest', 'wombat', 'yak', 'zebra']

cards_class = ['ace of clubs', 'ace of diamonds', 'ace of hearts', 'ace of spades', 'eight of clubs',
               'eight of diamonds', 'eight of hearts', 'eight of spades', 'five of clubs', 'five of diamonds',
               'five of hearts', 'five of spades', 'four of clubs', 'four of diamonds', 'four of hearts',
               'four of spades', 'jack of clubs', 'jack of diamonds', 'jack of hearts', 'jack of spades', 'joker',
               'king of clubs', 'king of diamonds', 'king of hearts', 'king of spades', 'nine of clubs',
               'nine of diamonds', 'nine of hearts', 'nine of spades', 'queen of clubs', 'queen of diamonds',
               'queen of hearts', 'queen of spades', 'seven of clubs', 'seven of diamonds', 'seven of hearts',
               'seven of spades', 'six of clubs', 'six of diamonds', 'six of hearts', 'six of spades', 'ten of clubs',
               'ten of diamonds', 'ten of hearts', 'ten of spades', 'three of clubs', 'three of diamonds',
               'three of hearts', 'three of spades', 'two of clubs', 'two of diamonds', 'two of hearts',
               'two of spades']

weather_class = ['dew', 'fogsmog', 'frost', 'glaze', 'hail', 'lightning', 'rain', 'rainbow', 'rime', 'sandstorm',
                 'snow']

dog_breed_class = ['Afghan','African Wild Dog', 'Airedale', 'American Hairless','American Spaniel', 'Basenji', 'Basset',
                   'Beagle', 'Bearded Collie', 'Bermaise', 'Bichon Frise', 'Blenheim', 'Bloodhound', 'Bluetick',
                   'Border Collie','Borzoi','Boston Terrier', 'Boxer', 'Bull Mastiff', 'Bull Terrier', 'Bulldog',
                   'Cairn', 'Chihuahua', 'Chinese Crested','Chow', 'Clumber','Cockapoo', 'Cocker', 'Collie', 'Corgi',
                   'Coyote', 'Dalmation', 'Dhole', 'Dingo', 'Doberman', 'Elk Hound', 'French Bulldog', 'German Sheperd',
                   'Golden Retriever', 'Great Dane', 'Great Perenees', 'Greyhound', 'Groenendael', 'Irish Spaniel',
                   'Irish Wolfhound', 'Japanese Spaniel', 'Komondor', 'Labradoodle', 'Labrador', 'Lhasa', 'Malinois',
                   'Maltese', 'Mex Hairless', 'Newfoundland', 'Pekinese', 'Pit Bull', 'Pomeranian', 'Poodle', 'Pug',
                   'Rhodesian', 'Rottweiler', 'Saint Bernard', 'Schnauzer', 'Scotch Terrier', 'Shar_Pei', 'Shiba Inu',
                   'Shih-Tzu', 'Siberian Husky', 'Vizsla', 'Yorkie']

chess_class = ['Bishop', 'King', 'Knight', 'Pawn', 'Queen', 'Rook']

bird_class = ['Asian-Green-Bee-Eater', 'Brown-Headed-Barbet', 'Cattle-Egret', 'Common-Kingfisher', 'Common-Myna',
              'Common-Rosefinch', 'Common-Tailorbird', 'Coppersmith-Barbet', 'Forest-Wagtail', 'Gray-Wagtail', 'Hoopoe',
              'House-Crow', 'Indian-Grey-Hornbill', 'Indian-Peacock', 'Indian-Pitta', 'Indian-Roller', 'Jungle-Babbler',
              'Northern-Lapwing', 'Red-Wattled-Lapwing', 'Ruddy-Shelduck', 'Rufous-Treepie', 'Sarus-Crane',
              'White-Breasted-Kingfisher', 'White-Breasted-Waterhen', 'White-Wagtail']


# HTML Responses
@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("main.html", {"request": request})


@app.get("/models/yoga_pose/yoga_pose.html", response_class=HTMLResponse)
async def read_yoga_pose(request: Request):
    return templates1.TemplateResponse("yoga_pose/yoga_pose.html", {"request": request})


@app.get("/models/weather/weather.html", response_class=HTMLResponse)
async def read_weather(request: Request):
    return templates1.TemplateResponse("weather/weather.html", {"request": request})


@app.get("/models/sports_ball/sports_ball.html", response_class=HTMLResponse)
async def read_sports_ball(request: Request):
    return templates1.TemplateResponse("sports_ball/sports_ball.html", {"request": request})


@app.get("/models/mammals/mammals.html", response_class=HTMLResponse)
async def read_mammals(request: Request):
    return templates1.TemplateResponse("mammals/mammals.html", {"request": request})


@app.get("/models/flower/flower.html", response_class=HTMLResponse)
async def read_flower(request: Request):
    return templates1.TemplateResponse("flower/flower.html", {"request": request})


@app.get("/models/card/card.html", response_class=HTMLResponse)
async def read_card(request: Request):
    return templates1.TemplateResponse("card/card.html", {"request": request})


@app.get("/models/dog_breed/dog_breed.html", response_class=HTMLResponse)
async def read_dog_breed(request: Request):
    return templates1.TemplateResponse("dog_breed/dog_breed.html", {"request": request})


@app.get("/models/chess/chess.html", response_class=HTMLResponse)
async def read_chess(request: Request):
    return templates1.TemplateResponse("chess/chess.html", {"request": request})


@app.get("/models/bird/bird.html", response_class=HTMLResponse)
async def read_bird(request: Request):
    return templates1.TemplateResponse("bird/bird.html", {"request": request})


# Function Converting Img --> Array
def read_file_as_image(data):
    img = Image.open(BytesIO(data)).resize((224, 224))
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    return img_array


# Endpoint for Sports Ball Model
@app.post("/predict_sports_ball")
async def predict_sports_ball(file: UploadFile = File(...)):
    print("Sports Ball Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = sports_ball_model.predict(img)
    result = sports_ball_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# EndPoint For Flower Model
@app.post("/predict_flower")
async def predict_flower(file: UploadFile = File(...)):
    print("Flower Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = flower_model.predict(img)
    result = flower_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for Weather Model
@app.post("/predict_weather")
async def weather(file: UploadFile = File(...)):
    print("Weather Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = weather_model.predict(img)
    result = weather_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for Yoga Pose Model
@app.post("/predict_yoga_pose")
async def predict_yoga_pose(file: UploadFile = File(...)):
    print("Yoga Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = yoga_pose_model.predict(img)
    result = yoga_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for Mammals Model
@app.post("/predict_mammals")
async def predict_mammals(file: UploadFile = File(...)):
    print("Mammals Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = mammals_model.predict(img)
    result = mammals_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for card Model
@app.post("/predict_card")
async def predict_card(file: UploadFile = File(...)):
    print("card Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = card_model.predict(img)
    result = cards_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for Dog Breed Model
@app.post("/predict_dog_breed")
async def predict_dog_breed(file: UploadFile = File(...)):
    print("Dog Breed Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = dog_breed_model.predict(img)
    result = dog_breed_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for chess Model
@app.post("/predict_chess")
async def predict_chess(file: UploadFile = File(...)):
    print("Chess Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = chess_model.predict(img)
    result = chess_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }


# Endpoint for bird Model
@app.post("/predict_bird")
async def predict_bird(file: UploadFile = File(...)):
    print("bird Prediction endpoint called")
    file.file.seek(0)
    img = read_file_as_image(await file.read())
    img = np.expand_dims(img, axis=0)

    predicted = bird_model.predict(img)
    result = bird_class[np.argmax(predicted[0])]
    confidence = np.max(predicted[0])

    return {
        'class': result,
        'confidence': round(confidence * 100, 1)
    }

# Run The Server In Localhost via Uvicorn
if __name__ == '__main__':
    uvicorn.run(app, host='localhost', port=8000)