Delete app.py

app.py

@@ -1,239 +0,0 @@
-import streamlit as st
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import warnings
-warnings.filterwarnings('ignore')
-# %matplotlib inline
-
-import tensorflow
-print (tensorflow.__version__)
-
-st.header("Welcome to the Generative Playground")
-
-from tensorflow.keras.datasets import mnist,cifar10
-
-option = st.selectbox(
-    "Which model would you like to get prediction with?",
-    ("None","Auto-Regressor", "Auto-Encoder", "Diffusion-Model","Other"))
-
-st.write("You selected:", option)
-
-if option == "None":
-    st.write("Please Select the model to get the fun prediction.... :)")
-
-if option == "Auto-Encoder":
-    st.write("It is under development")
-    st.write("Stay tune... Comming soon... :)")
-    
-if option == "Other":
-    st.write("Stay tune... Updating soon... :)")
-
-if option == "Diffusion-Model":
-    st.write("It is under development")
-    st.write("Stay tune... Comming soon... :)")
-    
-if option == "Auto-Regressor":
-    if st.button("Run"):
-        st.write("Running Auto-Regressor")
-
-        st.write("trained on --> cifar-10 dataset, RTX-GPU's, 50-epochs")
-        st.write("This is trail model, updated version will be updated consicutively.")
-        
-        (trainX, trainy), (testX, testy) = cifar10.load_data()
-        
-        print('Training data shapes: X=%s, y=%s' % (trainX.shape, trainy.shape))
-        print('Testing data shapes: X=%s, y=%s' % (testX.shape, testy.shape))
-        
-        
-        
-        for k in range(4):
-            fig = plt.figure(figsize=(9,6))
-            for j in range(9):
-                i = np.random.randint(0, 10000)
-                plt.subplot(990 + 1 + j)
-                plt.imshow(trainX[i], cmap='gray_r')
-                # st.pyplot(fig)
-                plt.axis('off')
-                #plt.title(trainy[i])
-            plt.show()
-            st.pyplot(fig)
-        
-        
-        # asdfaf
-        
-        trainX = np.where(trainX < (0.33 * 256), 0, 1)
-        train_data = trainX.astype(np.float32)
-        
-        testX = np.where(testX < (0.33 * 256), 0, 1)
-        test_data = testX.astype(np.float32)
-        
-        train_data = np.reshape(train_data, (50000, 32, 32, 3))
-        test_data = np.reshape(test_data, (10000, 32, 32, 3))
-        
-        print (train_data.shape, test_data.shape)
-        
-        
-        import tensorflow
-        
-        class PixelConvLayer(tensorflow.keras.layers.Layer):
-            def __init__(self, mask_type, **kwargs):
-                super(PixelConvLayer, self).__init__()
-                self.mask_type = mask_type
-                self.conv = tensorflow.keras.layers.Conv2D(**kwargs)
-        
-            def build(self, input_shape):
-                # Build the conv2d layer to initialize kernel variables
-                self.conv.build(input_shape)
-                # Use the initialized kernel to create the mask
-                kernel_shape = self.conv.kernel.get_shape()
-                self.mask = np.zeros(shape=kernel_shape)
-                self.mask[: kernel_shape[0] // 2, ...] = 1.0
-                self.mask[kernel_shape[0] // 2, : kernel_shape[1] // 2, ...] = 1.0
-                if self.mask_type == "B":
-                    self.mask[kernel_shape[0] // 2, kernel_shape[1] // 2, ...] = 1.0
-        
-            def call(self, inputs):
-                self.conv.kernel.assign(self.conv.kernel * self.mask)
-                return self.conv(inputs)
-        
-        
-        # Next, we build our residual block layer.
-        # This is just a normal residual block, but based on the PixelConvLayer.
-        class ResidualBlock(tensorflow.keras.layers.Layer):
-            def __init__(self, filters, **kwargs):
-                super(ResidualBlock, self).__init__(**kwargs)
-                self.conv1 = tensorflow.keras.layers.Conv2D(
-                    filters=filters, kernel_size=1, activation="relu"
-                )
-                self.pixel_conv = PixelConvLayer(
-                    mask_type="B",
-                    filters=filters // 2,
-                    kernel_size=3,
-                    activation="relu",
-                    padding="same",
-                )
-                self.conv2 = tensorflow.keras.layers.Conv2D(
-                    filters=filters, kernel_size=1, activation="relu"
-                )
-        
-            def call(self, inputs):
-                x = self.conv1(inputs)
-                x = self.pixel_conv(x)
-                x = self.conv2(x)
-                return tensorflow.keras.layers.add([inputs, x])
-        
-        inputs = tensorflow.keras.Input(shape=(32,32,3))
-        x = PixelConvLayer(
-            mask_type="A", filters=128, kernel_size=7, activation="relu", padding="same"
-        )(inputs)
-        
-        for _ in range(5):
-            x = ResidualBlock(filters=128)(x)
-        
-        for _ in range(2):
-            x = PixelConvLayer(
-                mask_type="B",
-                filters=128,
-                kernel_size=1,
-                strides=1,
-                activation="relu",
-                padding="valid",
-            )(x)
-        
-        out = tensorflow.keras.layers.Conv2D(
-            filters=3, kernel_size=1, strides=1, activation="sigmoid", padding="valid"
-        )(x)
-        
-        pixel_cnn = tensorflow.keras.Model(inputs, out)
-        pixel_cnn.summary()
-        
-        adam = tensorflow.keras.optimizers.Adam(learning_rate=0.0005)
-        pixel_cnn.compile(optimizer=adam, loss="binary_crossentropy")
-        
-        
-        # %%
-        import os
-        checkpoint_path = "training_1/cp.ckpt"
-        # checkpoint_path = "training_1/cp.weights.h5"
-        checkpoint_dir = os.path.dirname(checkpoint_path)
-        
-        
-        pixel_cnn.load_weights(checkpoint_path)
-        
-        
-        # %% [markdown]
-        # # Display Results 81 images
-        
-        # %%
-        # from IPython.display import Image, display
-        from tqdm import tqdm
-        
-        
-        # Create an empty array of pixels.
-        batch = 1
-        pixels = np.zeros(shape=(batch,) + (pixel_cnn.input_shape)[1:])
-        batch, rows, cols, channels = pixels.shape
-        
-        print(pixels.shape)
-        
-        
-        import time 
-        
-        # progress_text = "Operation in progress. Please wait."
-        # my_bar = st.progress(0, progress_text)
-        st.caption("Generating..... pls.. wait.. :)")
-        my_bar = st.progress(0)
-        
-        
-        # Iterate over the pixels because generation has to be done sequentially pixel by pixel.
-        for row in tqdm(range(rows)):
-            for col in range(cols):
-                for channel in range(channels):
-                    time.sleep(0.01)
-                    # Feed the whole array and retrieving the pixel value probabilities for the next
-                    # pixel.
-                    probs = pixel_cnn.predict(pixels)[:, row, col, channel]
-                    # Use the probabilities to pick pixel values and append the values to the image
-                    # frame.
-                    pixels[:, row, col, channel] = tensorflow.math.ceil(
-                      probs - tensorflow.random.uniform(probs.shape)
-                    )
-            my_bar.progress(int(row*3.125))
-            # if row<rows/2:
-            #     my_bar.progress((rows+1)*2)
-            # else:
-            #     my_bar.progress(row+51)
-            
-        my_bar.progress(100)
-        time.sleep(1)
-
-        
-        from PIL import Image
-        # figout = plt.figure(figsize=(9,6))
-        # st.image(Image.fromarray((pixels[-1] * 255).astype(np.uint8), 'RGB').show(),caption="image")
-        # Convert the generated pixel array to an image
-        generated_image = Image.fromarray((pixels[-1] * 255).astype(np.uint8), 'RGB')
-        
-        # Display the image using Streamlit
-        st.image(generated_image, caption="Generated Image")
-        
-        # counter = 0
-        # for i in range(4):
-        #     figout = plt.figure(figsize=(9,6))
-        #     for j in range(9):
-        #         plt.subplot(990 + 1 + j)
-        #         plt.imshow(pixels[counter,:,:,0])#, cmap='gray_r')
-        #         counter += 1
-        #         plt.axis('off')
-        #     plt.show()
-        #     st.pyplot(figout)
-        
-        # %%
-# else:
-#     st.write("Not Available")
-
-        
-