Update app.py

app.py

@@ -1,10 +1,31 @@
-import keras
-import numpy as nb
-from keras.models import load_model
-model = load_model('/content/drive/MyDrive/moodel.keras')
-print("Model loaded successfully")
-test_ip = nb.array([12,13,14])
-ws = 3
-test_ip = test_ip.reshape((1,ws,1))
-y_pred = model.predict(test_ip,verbose = 0)
-print(f"Predicted number : {y_pred[0][0]:.2f}")
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import SimpleRNN, Dense
+import matplotlib.pyplot as plt
+seq = np.array([i for i in range(1,101)])
+window_size = 3
+x=[]
+y=[]
+for i in range(len(seq) - window_size):
+  x.append(seq[i:i+window_size])
+  y.append(seq[i+window_size])
+x=np.array(x)
+y=np.array(y)
+x = x.reshape(x.shape[0],x.shape[1],1)
+model = Sequential()
+model.add(LSTM(units=32,input_shape=(window_size,1)))
+model.add(Dense(1))
+model.compile(optimizer='adam',loss='mse')
+model.fit(x,y,epochs=500,verbose=1)
+print("training comple")
+test_input = np.array([1,2,3])
+test_input = test_input.reshape(1,window_size,1)
+predicted = model.predict(test_input,verbose=0)
+print(f"predicted next number :{predicted[0][0]:.2f}")
+model.fit(x,y)
+predictions = model.predict(x)
+plt.plot(y,label="True Values")
+plt.plot(predictions,label="predicted")
+plt.legend()
+plt.title("truevalues vs predictedvalues")
+plt.show()