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FROM python:3

COPY wallet.py ./
COPY requirements.txt ./
COPY dataTraining.csv ./

RUN pip install -r requirements.txt

CMD ["python", "wallet.py", "/data/input.csv"]

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import sys
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

import math
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression

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import pandas as pd
import matplotlib.pyplot as plt
from scipy import special
import numpy as np


# Read the data
data = pd.read_csv('raw_scores.list1',sep=' ', header=None)
x = data[1]

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from django.urls import path
from . import views
urlpatterns = [
    path('', views.index, name='index'),
]

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from django.http import HttpResponse
def index(request):
    return HttpResponse("Salam World - This is my firts Django in Docker and accesible from AWS")

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from django.shortcuts import render

# Create your views here.
from django.http import HttpResponse
def index(request):
    return HttpResponse(" Salam World - this is my first Django on Docker accesible from AWS ")

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor

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import pandas as pd
sf = pd.read_csv('final_data.csv')
sf.head()
#columns not needed will be deleted
sf.drop(sf.columns[[0, 2, 3, 15, 17, 18]], axis=1, inplace=True)
#display dataset structure
sf.info()
# Display min and max sold date / display min,max,mean,deviation,25% .. and some other statics
print(sf.lastsolddate.min())
print(sf.lastsolddate.max())

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import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt        # For plotting graphs
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")

dataset=pd.read_csv("cancer.csv")
X = dataset.iloc[:, 1:31].values
Y = dataset.iloc[:, 31].values

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import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt        # For plotting graphs
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")

train=pd.read_csv("training.csv")
test=pd.read_csv("test.csv")
train_original=pd.read_csv("training.csv")