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app.py

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+import streamlit as st
+from tensorflow import keras
+import os
+import matplotlib.pyplot as plt
+from io import BytesIO
+from NNVisualiser import NNVisualiser
+import glob
+import inspect
+from tensorflow.keras.models import save_model
+import tempfile
+import re
+import zipfile
+import io
+
+# Function to create a ZIP file of all PNG files
+def create_zip_of_png_files():
+    # Get current working directory
+    cwd = os.getcwd()
+    png_files = [f for f in os.listdir(cwd) if f.endswith('.png')]
+
+    # Create a BytesIO object to hold the ZIP file in memory
+    zip_buffer = io.BytesIO()
+
+    with zipfile.ZipFile(zip_buffer, 'w') as zip_file:
+        for png_file in png_files:
+            zip_file.write(os.path.join(cwd, png_file), arcname=png_file)
+
+    zip_buffer.seek(0)  # Seek to the beginning of the BytesIO buffer
+    return zip_buffer
+
+def generate_title_from_method_name(method_name):
+    # Remove the "plot" prefix if it exists
+    if method_name.startswith("plot"):
+        method_name = method_name[4:]  # Remove the first 4 characters ("plot")
+    
+    # Split the string at camel case boundaries
+    words = re.findall(r'[A-Z][a-z]*', method_name)
+    
+    # Join the words with spaces and format the final string
+    title = "Plotting " + " ".join(words[:]) + " Plot "
+    
+    return title
+
+def downloadKerasModel():
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".keras") as tmp_file:
+        save_model(model, tmp_file.name)
+        tmp_file.seek(0)
+        model_data = tmp_file.read()
+    return model_data   
+
+# Function to build folder hierarchy up to the 6th level (excluding files and hidden folders)
+@st.cache_data
+def generate_folder_hierarchy(root_folder, max_depth=6):
+    folder_dict = {}
+
+    # Traverse through the directory tree
+    for dirpath, dirnames, filenames in os.walk(root_folder):
+        # Get the relative path from the root folder
+        rel_path = os.path.relpath(dirpath, root_folder)
+        depth = rel_path.count(os.sep) + 1  # Calculate the depth level
+
+        # Only include directories up to the max_depth (7th level)
+        if depth > max_depth:
+            continue
+
+        # Filter out directories that start with a dot (e.g., .git)
+        dirnames[:] = [d for d in dirnames if not d.startswith('.') and d != '1']
+
+        sub_dict = folder_dict
+        # Split the relative path into parts to create a nested structure
+        for part in rel_path.split(os.sep):
+            if part == '.' or part.startswith('.'):
+                continue
+            if part not in sub_dict:
+                sub_dict[part] = {}
+            sub_dict = sub_dict[part]
+
+    return folder_dict
+
+@st.cache_data
+def getPlotMethods():
+    return [name for name, func in inspect.getmembers(NNVisualiser, inspect.isfunction) if name.startswith('plot')]
+
+# Example usage
+root_folder = os.getcwd();  # Replace with your folder path
+folder_hierarchy = generate_folder_hierarchy(root_folder)
+
+# Streamlit app
+st.title("Repository : Simple ANN Models with UAT Architecture")
+st.write(f"A Collection of ANN Models with a 1-xReLU-1 Architecture for Basic 1D Functions on Bounded Intervals")
+#Commented
+
+# col1, col2, col3 = st.columns([4, 3, 3])
+
+# with col1:
+#     # Level 1: Initialisation dropdown
+#     initialisation = st.selectbox("Select Initialisation", list(folder_hierarchy.keys()))
+
+# with col2:
+#     # Level 2: Sample size dropdown, based on selected initialisation
+#     sampleSize = st.selectbox("Select Sample Size", list(folder_hierarchy[initialisation].keys()))
+
+# with col3:
+#     # Level 3: Batch size dropdown, based on selected sample size
+#     batchSize = st.selectbox("Select Batch Size", list(folder_hierarchy[initialisation][sampleSize].keys()))
+
+
+# col4, col5, col6 = st.columns([3, 4, 3])
+
+# with col4:
+#     # Level 4: Epochs count dropdown, based on selected batch size
+#     epochs = st.selectbox("Select Epochs Count", list(folder_hierarchy[initialisation][sampleSize][batchSize].keys()))
+
+# with col5:
+#     # Level 5: Functions list dropdown, based on selected epochs count
+#     functions = st.selectbox("Select Neurons Count", list(folder_hierarchy[initialisation][sampleSize][batchSize][epochs].keys()))
+
+# with col6:
+#     # Level 6: Neurons count dropdown, based on selected function
+#     neurons = st.selectbox("Select Neurons Count", list(folder_hierarchy[initialisation][sampleSize][batchSize][epochs][functions].keys()))
+
+
+initialisation = st.sidebar.selectbox("Select Initialisation", list(folder_hierarchy.keys()))
+sampleSize = st.sidebar.selectbox("Select Sample Size", list(folder_hierarchy[initialisation].keys()))
+batchSize = st.sidebar.selectbox("Select Batch Size", list(folder_hierarchy[initialisation][sampleSize].keys()))
+epochs = st.sidebar.selectbox("Select Epochs Count", list(folder_hierarchy[initialisation][sampleSize][batchSize].keys()))
+functions = st.sidebar.selectbox("Select Neurons Count", list(folder_hierarchy[initialisation][sampleSize][batchSize][epochs].keys()))
+neurons = st.sidebar.selectbox("Select Neurons Count", list(folder_hierarchy[initialisation][sampleSize][batchSize][epochs][functions].keys()))
+
+# Display the selected values
+st.write(f"You selected: {initialisation} : {sampleSize} : {batchSize} : {epochs} : {functions} : {neurons}")
+
+modelPath = os.path.join(os.getcwd(), initialisation, sampleSize, batchSize, epochs, functions, neurons);
+model = keras.models.load_model(modelPath);
+
+visualiser = NNVisualiser(model);
+visualiser.setSavePlots(True);
+
+# Function to get layer and neuron information
+def get_layer_info(model):
+    layer_info = []
+    for layer in model.layers:
+        layer_info.append({
+            'index': len(layer_info),
+            'type': layer.__class__.__name__,
+            'units': getattr(layer, 'units', None),  # Number of neurons
+        })
+    return layer_info
+
+layer_info = get_layer_info(model)
+
+# Extract layer indices and neuron counts
+layer_indices = [layer['index'] for layer in layer_info]
+neuron_counts = [layer['units'] for layer in layer_info]
+
+# Dropdown for selecting layer index
+#selected_layer_index = st.sidebar.selectbox("Select Layer Index", layer_indices)
+
+# Find the number of neurons for the selected layer
+#selected_layer_units = neuron_counts[selected_layer_index]
+
+# Dropdown for selecting neuron index in the selected layer
+#neuron_indices = list(range(selected_layer_units))
+#selected_neuron_index = st.sidebar.selectbox("Select Neuron Index", neuron_indices)
+
+# Dropdown for selecting plots from NNVisualiser
+plotMethods = getPlotMethods()
+selectedPlotMethod = st.sidebar.selectbox("Select Plot", plotMethods)
+
+#Removing earlier plots
+image_files = glob.glob("*.png")
+for file in image_files:
+    try:
+        os.remove(file)
+    except Exception as e:
+        st.write("Error in removing previous plots")
+
+st.session_state.title_text = generate_title_from_method_name(selectedPlotMethod)
+st.title(st.session_state.title_text)
+
+# Call your package's plot method (which directly plots without returning a figure)
+visualiser.setSavePlots(True);
+method = getattr(visualiser, selectedPlotMethod, None)
+
+if method is not None:
+    if 'Neuron' in selectedPlotMethod:
+        selected_layer_index = st.sidebar.selectbox("Select Layer Index", layer_indices)
+        # Find the number of neurons for the selected layer
+        selected_layer_units = neuron_counts[selected_layer_index]
+        # Dropdown for selecting neuron index in the selected layer
+        neuron_indices = list(range(selected_layer_units))
+        selected_neuron_index = st.sidebar.selectbox("Select Neuron Index", neuron_indices)
+        params = (selected_layer_index, selected_neuron_index)
+        method(*params)
+    elif 'Layer' in selectedPlotMethod:
+        selected_layer_index = st.sidebar.selectbox("Select Layer Index", layer_indices)
+        params = (selected_layer_index,)
+        method(*params)
+    else:
+        method()
+
+st.session_state.kerasModelToDownload = downloadKerasModel()
+st.session_state.plotsToDownload = create_zip_of_png_files()
+
+@st.fragment()
+def downloads():
+    st.download_button(
+            label="Download Model",
+            data = downloadKerasModel(),
+            file_name="model.keras",
+            mime="application/octet-stream"
+        );
+    
+    st.download_button(
+        label="Download Plots",
+        data=create_zip_of_png_files(),
+        file_name="images.zip",
+        mime="application/zip"
+        );
+    # column = st.columns (2)
+    
+    # column[0].download_button(
+    #         label="Download Model",
+    #         data = downloadKerasModel(),
+    #         file_name="model.keras",
+    #         mime="application/octet-stream"
+    #     );
+    
+    # column[1].download_button(
+    #     label="Download Plots",
+    #     data=create_zip_of_png_files(),
+    #     file_name="images.zip",
+    #     mime="application/zip"
+    #     );
+
+with st.sidebar:
+    downloads()
+
+# visualiser.plotFlowForNetwork();
+
+image_files = glob.glob("*.png")
+
+# Use Streamlit to display the image from the buffer
+st.image(image_files)
+
+# if st.sidebar.button("Download Keras model"):
+#     downloadKerasModel()
+    
+
+# if st.sidebar.download_button(
+#             label="Download Keras Model",
+#             data = downloadKerasModel(),
+#             file_name="model.keras",
+#             mime="application/octet-stream"
+#         ):
+#     st.sidebar.success(f"Model Downloaded Successfully")
+
+# # Button to create and download the ZIP file
+# if st.sidebar.download_button(
+#         label="Download Plots",
+#         data=create_zip_of_png_files(),
+#         file_name="images.zip",
+#         mime="application/zip"
+#     ):
+#     st.sidebar.success(f"Plots Downloaded Successfully")

requirements.txt

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+numpy==1.23.5
+keras==2.14.0
+matplotlib==3.7.1
+tensorflow==2.14.0
+NeuralNetworkCoordinates==1.0.0
+NNVisualiser==1.0.0