Create app.py

app.py

@@ -0,0 +1,83 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import Sequential
+from sklearn.preprocessing import MinMaxScaler
+from keras.layers.core import Dense,Dropout,Activation
+from keras.layers.recurrent import LSTM
+import gradio as gr
+
+df_data=pd.read_csv("C:/Users/Joyson/Downloads/Kaggle datasets/jena_climate_2009_2016.csv/jena_climate_2009_2016.csv",parse_dates=["Date Time"],index_col=0)
+df=pd.DataFrame(df_data.loc[:,"T (degC)"])
+df_fil=df["T (degC)"].resample("D").max()
+df1=pd.DataFrame(df_fil)
+min_max_scaler=MinMaxScaler(feature_range=(0,1))
+dataset=min_max_scaler.fit_transform(df1['T (degC)'].values.reshape(-1,1))
+
+train_size=int(len(dataset)*0.7)
+
+test_size=len(dataset)-train_size
+
+train=dataset[0:train_size,:]
+
+test=dataset[train_size:len(dataset),:]
+
+def create_dataset(dataset,time_step=6):
+    x_ind,y_dep =[],[]
+    for i in range(len(dataset)-time_step-1):
+        a=dataset[i:(i+time_step),0]
+        x_ind.append(a)
+        y_dep.append(dataset[i+time_step,0])
+    return np.array(x_ind),np.array(y_dep)
+x_train,y_train=create_dataset(train,time_step=6)
+
+x_test,y_test=create_dataset(test,time_step=6)
+
+x_train=np.reshape(x_train,(x_train.shape[0],1,x_train.shape[1]))
+
+x_test=np.reshape(x_test,(x_test.shape[0],1,x_test.shape[1]))
+
+time_step=6
+model_lstm=Sequential()
+
+model_lstm.add(LSTM(20,input_shape=(1,time_step)))
+model_lstm.add(Dense(1))
+
+
+model_lstm.compile(loss="mean_squared_error",optimizer='adam')
+
+model_lstm.fit(x_train,y_train,epochs=150)
+y_pred=model_lstm.predict(x_test)
+
+y_pred=min_max_scaler.inverse_transform(y_pred)
+df2=pd.DataFrame(df_fil)
+
+def forecast_temp(no_of_samples):
+  a= int(no_of_samples)*6
+  new_data = df2[-(a+1):-1]
+  forecast_dataset=np.array(new_data)
+  forecast_dataset=forecast_dataset.reshape(-1, 1)
+  X=min_max_scaler.transform(forecast_dataset)
+  TimeSteps=int(6)
+  NumFeatures=int(1)
+  no_of_samples=int(no_of_samples)
+  X=X.reshape(no_of_samples,NumFeatures,TimeSteps)
+  predicted_temp = model_lstm.predict(X)
+  predicted_temp = min_max_scaler.inverse_transform(predicted_temp)
+  predict_df=pd.DataFrame(list(map(lambda x: x[0], predicted_temp)),columns=["predictions"])
+  predict_df.reset_index(inplace=True)
+  predict_df = predict_df.rename(columns = {'index':'DAYS'})
+  plt.figure(figsize=(15, 6))
+  range_history = len(new_data)
+  range_future = list(range(range_history, range_history +len(predicted_temp)))
+  plt.plot(np.arange(range_history), np.array(new_data),label='History')
+  plt.plot(range_future, np.array(predicted_temp),label='Forecasted for RNN')
+  plt.legend(loc='upper right')
+  plt.xlabel('Time step (hour)')
+  plt.ylabel('Stock Price')
+  return predict_df,plt.gcf()
+
+interface = gr.Interface(fn = forecast_temp, 
+                         inputs = gr.inputs.Slider(minimum=0, maximum=50, step=1, default=5, label="Number of Sample to Predict"), 
+                         outputs = ["dataframe","plot"],description="Max Temp Prediction of a day")
+interface.launch()