Delete app.py

app.py

@@ -1,116 +0,0 @@
-
-import pandas as pd
-import math
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import seaborn as sns
-import keras
-import tensorflow as tf
-from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense
-from tensorflow.keras.layers import LSTM
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_absolute_error as MAE
-
-
-pwd = os.getcwd()
-filepath = os.path.join(pwd,r"DATA/try_data.csv")
-
-df = pd.read_csv(filepath)
-
-df1 = df.reset_index()['cases']
-
-scaler = MinMaxScaler(feature_range=(0,1))
-df1 = scaler.fit_transform(np.array(df1).reshape(-1,1))
-
-training_size = int(len(df1)*0.8)
-test_size= len(df1)-training_size
-
-train_data,test_data = df1[0:training_size,:],df1[training_size:len(df1),:1]
-
-def create_dataset (dataset,time_step=1):
-    dataX, dataY = [], []
-    for i in range(len(dataset)-time_step-1):
-        a = dataset[i:(i+time_step),0]
-        dataX.append(a)
-        dataY.append(dataset[i+time_step, 0])
-    return np.array(dataX), np.array(dataY)
-
-
-time_step = 2
-X_train, y_train = create_dataset(train_data, time_step)
-X_test, y_test = create_dataset(test_data, time_step)
-
-
-X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
-X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
-
-
-model = Sequential()
-model.add(LSTM(11,return_sequences=True, input_shape=(2,1)))
-model.add(LSTM(11,return_sequences=True))
-model.add(LSTM(11))
-model.add(Dense(1))
-model.compile(loss='mean_squared_error', optimizer = "adam")
-
-lin_model=model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=400, batch_size=38, verbose=1)
-
-train_predict=model.predict(X_train)
-test_predict=model.predict(X_test)
-
-
-
-look_back=2
-trainPredictPlot = np.empty_like(df1)
-trainPredictPlot[:, :] = np.nan
-trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
-
-
-# shift test predictions for plotting
-testPredictPlot = np.empty_like(df1)
-testPredictPlot[:, :] = np.nan
-testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
-
-
-# plot baseline and predictions
-plt.figure(figsize = (15,6))
-plt.plot(scaler.inverse_transform(df1),'r',label='Original Data')
-plt.plot(trainPredictPlot,'g',label='Train Data')
-plt.plot(testPredictPlot,'b',label='Test Data')
-plt.xlabel('Months')
-plt.ylabel('Cases')
-plt.show()
-
-
-
-import gradio as gr
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-def plot(df):
-    
-
-    plt.plot(scaler.inverse_transform(df1),'r',label='Original Data')
-    plt.title("Original data that is used for analysis")
-    plt.savefig("original_data.png")
-    plt.plot(trainPredictPlot,'g',label='Train Data')
-    plt.title("Predicting the Trained data from the original data")
-    plt.savefig("train_prediction.png") 
-    plt.plot(testPredictPlot,'b',label='Test Data')
-    plt.title("Predicting the case coutn of the year 2020 and 2021")
-    plt.savefig("test_prediction.png")
-    
-   
-    plots = ["Case_count.png","Rainfall_pattern.png","original_data.png","train_prediction.png", "test_prediction.png"]
-    return plots
-
-inputs = [gr.Dataframe(label="Dengue Data")]
-outputs = [gr.Gallery(label="Inference from the data").style(grid=(5,1))]
-description = "We are given with the dataset of dengue cases with respect to the state Kerala with a span ranging from the year 2011 to 2021.The goal is to predict the cases in a monthly fashion, for the upcoming years.The bar graph indicated the year-wise dengue case count.The Time series plot indicates the rainfall pattern across the period 2011-2021.The red part of the graph is the input data that we have .The green part of the graph is the data with which we trained our model.The blue part of the graph indicates the predicted output(test data) against the red part which is the original data"
-
-gr.Interface(plot, inputs=inputs, outputs=outputs, title="Dengue Cases Prediction Dashboard",description=description,allow_flagging="never").launch()
-
-