finalize

app.py

@@ -69,56 +69,9 @@ if st.button('Generate Samples'):  # Generate the samples
     # plt.colorbar(cax=cax, shrink=0.1)
     st.pyplot(plt.figure(2))
 
-
-
 ########################################################################################################################
-# Output Generated Examples
-
-    # plt.figure(2)
-    # for j in range(5):  # shows  5 random images to the users to view samples of the dataset
-    #     i = np.random.randint(0, len(result))
-    #     plt.subplot(550 + 1 + j)
-    #     plt.imshow(result[i], cmap='gray', vmin=0, vmax=1)
-    # plt.figure(2)
-    # st.pyplot(plt.figure(2))
-
-
-'''
-# Testing
-image_size = 100
-densities = [1]
-
-boxes = make_boxes(image_size, densities)
-
-desired_density = 1
-# desired_thickness = 0
-
-desired_basic_box_thickness = 1
-desired_forward_slash_box_thickness = 2
-desired_back_slash_box_thickness = 0
-desired_hot_dog_box_thickness = 0
-desired_hamburger_box_thickness = 0
-
-
-box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\
-    = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3], list(zip(*boxes))[4], list(zip(*boxes))[5], list(zip(*boxes))[6]
-# print(np.shape(box_arrays))
-# print(np.shape(box_shape))
-# print(np.shape(box_density))
-
-indices = [i for i in range(len(box_arrays)) if box_density[i] == desired_density
-           and basic_box_thickness[i] == desired_basic_box_thickness
-           and forward_slash_box_thickness[i] == desired_forward_slash_box_thickness
-           and back_slash_box_thickness[i] == desired_back_slash_box_thickness
-           and hot_dog_box_thickness[i] == desired_hot_dog_box_thickness
-           and hamburger_box_thickness[i] == desired_hamburger_box_thickness]
-plt.imshow(box_arrays[indices[0]], cmap='gray', vmin=0, vmax=1)
-plt.show()
-'''
-# # Testing
-# image_size = 8
-# densities = [1]
-
+# Output Entire Dataset
+st.write("Click 'Generate Dataset' to generate the dataset based on the conditions set previously:")
 if st.button('Generate Dataset'):  # Generate the samples
     boxes = make_boxes(image_size, densities)
     box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\
@@ -130,67 +83,8 @@ if st.button('Generate Dataset'):  # Generate the samples
     # Rename the columns to the desired outputs
     dataframe = dataframe.rename(columns={0: "Array", 1: "Density", 2:"Basic Box Thickness", 3:"Forward Slash Strut Thickness", 4:"Back Slash Strut Thickness", 5:"Vertical Strut Thickness", 6:"Horizontal Strut Thickness"})
 
-    csv = dataframe.to_csv('2D_Lattice.csv')
-
+    csv = dataframe.to_csv()
+    st.write("Here is what the generated data looks like (double click on the 'Array' cells to view the full array):")
+    st.write(dataframe)  # Display the data generated
+    st.write("Click 'Download' to download a CSV file of the dataset:")
     st.download_button("Download Dataset", csv, file_name='2D_Lattice.csv')
-'''
-
-# food = load_dataset("cmudrc/2d-lattices", split="train[:15]")  # Loads the training data samples
-food = load_dataset("cmudrc/2d-lattices", split="train+test")  # Loads all of the data, for use after training
-
-# checks to see if the dataset has been assigned a class label
-# if type(food.features["label"]) != 'datasets.features.features.ClassLabel': # Cast to ClassLabel
-#     food = food.class_encode_column('label')
-print(food)
-desired_label = 'x_plus_box'
-desired_thickness = 3
-desired_density = 1
-
-data_frame = pd.DataFrame(food)
-# print(data_frame)
-
-shape_rows = data_frame['Shape'] == desired_label
-# print(shape_rows)
-
-thickness_rows = data_frame['Thickness'] == desired_thickness
-# print(thickness_rows)
-
-density_rows = data_frame['Density'] == desired_density
-# print(density_rows)
-
-desired_output = data_frame.loc[shape_rows & thickness_rows & density_rows].iloc[0]['Array']
-print(desired_output)
-print(type(desired_output))
-
-
-example_point = numpy.array(json.loads(desired_output))
-
-plt.imshow(example_point)
-plt.show()
-
-
-all_shapes = [basic_box, diagonal_box_split, horizontal_vertical_box_split, back_slash_box, forward_slash_box,
-                 back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, x_hamburger_box,
-                 x_hot_dog_box, x_plus_box]
-
-base_shapes = [basic_box, back_slash_box, forward_slash_box, hot_dog_box, hamburger_box]
-image_size = 256
-density = [1]
-
-boxes = make_boxes(image_size, density, all_shapes)
-
-
-box_arrays, box_shape, box_density, box_thickness,  = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3]
-
-# indices_1 = [i for i in range(len(boxes)) if boxes[1][i] == str(base_shapes[0]) and boxes[2][i] == density[0] and boxes[3][i] == desired_thickness]
-indices_1 = [i for i in range(len(box_arrays)) if box_shape[i] == desired_label and box_density[i] == desired_density and box_thickness[i] == desired_thickness]
-print(indices_1)
-# indices_1 = random.randint(0, len(box_arrays))
-
-
-# plt.imshow(box_arrays[indices_1])
-plt.imshow(box_arrays[indices_1[0]])
-plt.show()
-'''
-
-'''trainer.push_to_hub()''' # Need to figure out how to push the model to the hub