from google.colab import auth
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
import gspread
import pandas as pd
from google.auth import default
import pandas as pd

# Authenticate the Google sheet

auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)
worksheet = gc.open('dl model').sheet1

# Construct Data frame using Rows and columns

rows = worksheet.get_all_values()
df = pd.DataFrame(rows[1:], columns=rows[0])
df.head()
df=df.astype({'X':'float'})
df=df.astype({'Y':'float'})
X=df[['X']].values
Y=df[['Y']].values

# Split the testing and training data

x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.33,random_state=50)
scaler=MinMaxScaler()
scaler.fit(x_train)
x_t_scaled = scaler.transform(x_train)
x_t_scaled

# Build the Deep learning Model

ai_brain = Sequential([
    Dense(3,activation='relu'),
    Dense(2,activation='relu'),
    Dense(1,activation='relu')
])
ai_brain.compile(optimizer='rmsprop',loss='mse')
ai_brain.fit(x=x_t_scaled,y=y_train,epochs=2000)

loss_df = pd.DataFrame(ai_brain.history.history)
loss_df.plot()

# Evaluate the Model

scal_x_test=scaler.transform(x_test)
ai_brain.evaluate(scal_x_test,y_test)
input=[[105]]
input_scaled=scaler.transform(input)
ai_brain.predict(input_scaled)