from feast import FeatureStore
from IPython.core.display import display, HTML
import json
from json2html import *
import warnings
warnings.filterwarnings('ignore')

class FeastSchema:
    def __init__(self, repo_path: str):
        self.store = FeatureStore(repo_path=repo_path)
    
    def show_schema(self, skip_meta: bool= False):
        feast_schema=self.__project_show_schema(skip_meta)        
        display(HTML(json2html.convert(json = feast_schema)))

    def show_table_schema(self, table: str, skip_meta: bool= False):
        feasture_tables_dictionary=self.__project_show_schema(skip_meta)
        display(HTML(json2html.convert(json = {table:feasture_tables_dictionary[table]})))

    def __project_show_schema(self, skip_meta: bool= False):
        entities_dictionary={}
        feast_entities=self.store.list_entities()
        for entity in feast_entities:
            entity_dictionary=entity.to_dict()
            entity_spec=entity_dictionary['spec']
            entities_dictionary[entity_spec['name']]=entity_spec
        
        feasture_tables_dictionary={}
        feast_feature_tables=self.store.list_feature_views()
        for feature_table in feast_feature_tables:
            feature_table_dict=json.loads(str(feature_table))
            feature_table_spec=feature_table_dict['spec']
            feature_table_name=feature_table_spec['name']
            feature_table_spec.pop('name',None)
            if 'entities' in feature_table_spec:
                feature_table_entities=[]
                for entity in feature_table_spec['entities']:
                    feature_table_entities.append(entities_dictionary[entity])
                feature_table_spec['entities']=feature_table_entities
                
            if not skip_meta:
                feature_table_spec['meta']=feature_table_dict['meta']
            else:
                feature_table_spec.pop('input',None)
                feature_table_spec.pop('ttl',None)
                feature_table_spec.pop('online',None)
                
            feasture_tables_dictionary[feature_table_name]=feature_table_spec
        
        return feasture_tables_dictionary
    

        
    
FeastSchema(".").show_schema()
#FeastSchema(".").show_schema(skip_meta=True)
#FeastSchema(".").show_table_schema('driver_hourly_stats')
#FeastSchema().show_tables()

bucket_name="propensity"
filename="training_sample"

store = FeatureStore(repo_path=".")

s3 = fs.S3FileSystem(endpoint_override=os.environ.get("FEAST_S3_ENDPOINT_URL"))
entity_df=pd.read_parquet(f'{bucket_name}/{filename}_entities.parquet', filesystem=s3)
entity_df["event_timestamp"]=datetime.now()


training_df = store.get_historical_features(
    entity_df=entity_df, 
    feature_refs = [
        'propensity_data:basket_icon_click',
        'propensity_data:basket_add_list',
        'propensity_data:basket_add_detail',
        'propensity_data:sort_by',
        'propensity_data:image_picker',
        'propensity_data:account_page_click',
        'propensity_data:promo_banner_click',
        'propensity_data:detail_wishlist_add',
        'propensity_data:list_size_dropdown',
        'propensity_data:closed_minibasket_click',
        'propensity_data:checked_delivery_detail',
        'propensity_data:checked_returns_detail',
        'propensity_data:sign_in',
        'propensity_data:saw_checkout',
        'propensity_data:saw_sizecharts',
        'propensity_data:saw_delivery',
        'propensity_data:saw_account_upgrade',
        'propensity_data:saw_homepage',
        'propensity_data:device_mobile',
        'propensity_data:device_computer',
        'propensity_data:device_tablet',
        'propensity_data:returning_user',
        'propensity_data:loc_uk',
        'propensity_data:ordered'
    ],
).to_df()

predictors = training_df.drop(['propensity_data__ordered','UserID','event_timestamp'], axis=1)
targets = training_df['propensity_data__ordered']

X_train, X_test, y_train, y_test = train_test_split(predictors, targets, test_size=.3)

classifier=GaussianNB(var_smoothing=input_params['var_smoothing'])
classifier=classifier.fit(X_train,y_train)

predictions=classifier.predict(X_test)

conf_matrix=sklearn.metrics.confusion_matrix(y_test,predictions)
ac_score=sklearn.metrics.accuracy_score(y_test, predictions)

propensity_model_path = 'propensity.joblib'
joblib.dump(classifier, propensity_model_path)

artifacts = {
    "propensity_model": propensity_model_path,
    "feature_store": "feature_store.yaml"
}

import mlflow.pyfunc
class PropensityWrapper(mlflow.pyfunc.PythonModel):
    
    def load_context(self, context):
        import joblib
        from feast import FeatureStore
        import pandas as pd 
        import os
        
        self.model = joblib.load(context.artifacts["propensity_model"])
        self.store = FeatureStore(repo_path=os.environ.get("FEAST_REPO_PATH"))
        
    def predict(self, context, model_input):
        users=list(model_input.to_dict()["UserID"].values())
        
        feature_vector = self.store.get_online_features(
            feature_refs=[
                'propensity_data:basket_icon_click',
                'propensity_data:basket_add_list',
                'propensity_data:basket_add_detail',
                'propensity_data:sort_by',
                'propensity_data:image_picker',
                'propensity_data:account_page_click',
                'propensity_data:promo_banner_click',
                'propensity_data:detail_wishlist_add',
                'propensity_data:list_size_dropdown',
                'propensity_data:closed_minibasket_click',
                'propensity_data:checked_delivery_detail',
                'propensity_data:checked_returns_detail',
                'propensity_data:sign_in',
                'propensity_data:saw_checkout',
                'propensity_data:saw_sizecharts',
                'propensity_data:saw_delivery',
                'propensity_data:saw_account_upgrade',
                'propensity_data:saw_homepage',
                'propensity_data:returning_user',
                'propensity_data:loc_uk'
            ],
            entity_rows=[{"UserID": uid} for uid in users]
        ).to_dict()
        
        data=pd.DataFrame.from_dict(feature_vector)
        merged_data = pd.merge(model_input,data, how="inner", on=["UserID"], suffixes=('_x', '')).drop(['UserID'], axis=1)
        return self.model.predict(merged_data)

import warnings
import sys

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from urllib.parse import urlparse
import mlflow
import mlflow.sklearn
import mlflow.pyfunc

#conda_env=mlflow.pyfunc.get_default_conda_env()

with mlflow.start_run():
    
    #mlflow.log_param("var_smoothing", input_params['var_smoothing'])
    mlflow.log_metric("accuracy_score", ac_score)
    
    tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme

    if tracking_url_type_store != "file":
        mlflow.pyfunc.log_model("model",
                                 registered_model_name="propensity_model",
                                 python_model=PropensityWrapper(),
                                 artifacts=artifacts,
                                 conda_env=conda_env)
    else:
        mlflow.pyfunc.log_model("model",
                                 path=my_model_path,
                                 python_model=PropensityWrapper(),
                                 artifacts=artifacts,
                                 conda_env=conda_env)

import requests
import json

url="http://mlflow-serving.qooba.svc.cluster.local:5000/invocations"

headers={
    'Content-Type': 'application/json; format=pandas-records'
}

data=[
    {"UserID": "a720-6b732349-a720-4862-bd21-644732",
     'propensity_data:device_mobile': 1.0,
     'propensity_data:device_computer': 0.0,
     'propensity_data:device_tablet': 0.0
    }
]

response=requests.post(url, data=json.dumps(data), headers=headers)
response.text

import fastai
from fastai.vision import *
from fastai.callbacks import *
from fastai.utils.mem import *
from torchvision.models import vgg16_bn
from pathlib import Path

path = Path('/opt/notebooks/faces')
path_hr = path/'high_resolution'
path_lr = path/'small-96'

il = ImageList.from_folder(path_hr)

def resize_one(fn, i, path, size):
    dest = path/fn.relative_to(path_hr)
    dest.parent.mkdir(parents=True, exist_ok=True)
    img = PIL.Image.open(fn)
    targ_sz = resize_to(img, size, use_min=True)
    img = img.resize(targ_sz, resample=PIL.Image.BILINEAR).convert('RGB')
    img.save(dest, quality=60)

sets = [(path_lr, 96)]
for p,size in sets:
    if not p.exists(): 
        print(f"resizing to {size} into {p}")
        parallel(partial(resize_one, path=p, size=size), il.items)

bs,size=32,128
arch = models.resnet34
src = ImageImageList.from_folder(path_lr).split_by_rand_pct(0.1, seed=42)

def get_data(bs,size):
    data = (src.label_from_func(lambda x: path_hr/x.name)
           .transform(get_transforms(max_zoom=2.), size=size, tfm_y=True)
           .databunch(bs=bs,num_workers=0).normalize(imagenet_stats, do_y=True))

    data.c = 3
    return data

data = get_data(bs,size)

def gram_matrix(x):
    n,c,h,w = x.size()
    x = x.view(n, c, -1)
    return (x @ x.transpose(1,2))/(c*h*w)

base_loss = F.l1_loss

vgg_m = vgg16_bn(True).features.cuda().eval()
requires_grad(vgg_m, False)

blocks = [i-1 for i,o in enumerate(children(vgg_m)) if isinstance(o,nn.MaxPool2d)]

class FeatureLoss(nn.Module):
    def __init__(self, m_feat, layer_ids, layer_wgts):
        super().__init__()
        self.m_feat = m_feat
        self.loss_features = [self.m_feat[i] for i in layer_ids]
        self.hooks = hook_outputs(self.loss_features, detach=False)
        self.wgts = layer_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'gram_{i}' for i in range(len(layer_ids))]

    def make_features(self, x, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input, target):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [base_loss(input,target)]
        self.feat_losses += [base_loss(f_in, f_out)*w
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()

feat_loss = FeatureLoss(vgg_m, blocks[2:5], [5,15,2])

learn = unet_learner(data, arch, wd=wd, loss_func=feat_loss, callback_fns=LossMetrics,
                     blur=True, norm_type=NormType.Weight)