Upload folder using huggingface_hub

app.py

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+from flask import Flask, render_template, request, jsonify
+import os
+from werkzeug.utils import secure_filename
+import subprocess
+from flask import send_file
+from tensorflow import keras
+import edgeimpulse as ei
+import tensorflow as tf
+
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+
+ei.API_KEY = "ei_de71dadcd64f64e06980b32bc1b7c4222f102dd6e3cf373f" 
+
+app = Flask(__name__)
+
+# List to store labels
+labels_list = []
+
+deploy_filename = ""
+deploy_target = ""
+
+# Variable to store the path of the uploaded model
+uploaded_model_path = ""
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/upload', methods=['POST'])
+def upload():
+    label = request.form['label']
+    upload_folder = os.path.join(os.getcwd(), 'data', label)
+    os.makedirs(upload_folder, exist_ok=True)
+
+    for file in request.files.getlist('images'):
+        filename = secure_filename(file.filename)
+        file.save(os.path.join(upload_folder, filename))
+
+    return 'Upload successful!'
+
+@app.route('/train_model', methods=['GET'])
+def train_model():
+    try:
+        # Run the train.py script
+        result = subprocess.run(['python', 'train.py'], capture_output=True, text=True)
+
+        # Check if the command was successful
+        if result.returncode == 0:
+            return result.stdout
+        else:
+            return f"Error: {result.stderr}"
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+    
+@app.route('/download_trained_model')
+def download_trained_model():
+    model_name = request.args.get('model_name', '')  # Get the model name from the query parameters
+    if not model_name.endswith('.h5'):
+        model_name += '.h5'  # Ensure the model name has the .h5 extension
+
+    model_path = os.path.join(r"C://Users//Admin//Downloads//webappml//", model_name)  # Set the correct directory for your models
+    return send_file(model_path, as_attachment=True)
+
+
+
+
+# Add a new route to handle setting deploy file name
+@app.route('/set_deploy_filename', methods=['POST'])
+def set_deploy_filename():
+    global deploy_filename
+    deploy_filename = request.form['deploy_filename']
+    return f'Deploy file name set to: {deploy_filename}'
+
+
+
+@app.route('/run_app1')
+def run_app1():
+    try:
+        subprocess.run(['python', 'app1.py'], check=True)
+        return 'app1.py executed successfully!'
+    except subprocess.CalledProcessError as e:
+        return f'Error executing app1.py: {e}', 500
+
+@app.route('/add_labels', methods=['POST'])
+def add_labels():
+    global labels_list
+    labels_input = request.form['labels']
+    new_labels = [label.strip() for label in labels_input.split(',') if label.strip()]
+    labels_list.extend(new_labels)
+
+    # Print the labels list to the console
+    print("Labels List:", labels_list)
+
+    return 'Labels added successfully!'
+
+@app.route('/get_labels', methods=['GET'])
+def get_labels():
+    global labels_list
+    return jsonify(labels_list)
+
+@app.route('/clear_labels', methods=['GET'])
+def clear_labels():
+    global labels_list
+    labels_list.clear()
+    return 'Labels cleared successfully!'
+
+@app.route('/set_deploy_target', methods=['POST'])
+def set_deploy_target():
+    global deploy_target
+    deploy_target = request.form['deploy_target']
+    return f'Deploy target set to: {deploy_target}'
+
+
+
+
+
+
+@app.route('/upload_model', methods=['POST'])
+def upload_model():
+    global uploaded_model_path
+
+    model_file = request.files['model']
+
+    if model_file:
+        model_filename = secure_filename(model_file.filename)
+        project_path = os.getcwd()  # Get the current project directory
+        model_file_path = os.path.join(project_path, model_filename)
+        model_file.save(model_file_path)
+        model_file_path2 = os.path.join(r"C://Users//Admin//Downloads//webappml//models", model_filename)
+        model_file.save(model_file_path2)
+
+        # Store the path in the global variable
+        uploaded_model_path = model_file_path
+
+        # Print the path to the terminal
+        print("Uploaded Model Path:", uploaded_model_path)
+         #Print the path to the terminal
+        print(labels_list)
+        print(deploy_filename)
+        print(deploy_target)
+
+        return 'Model file uploaded successfully!'
+    else:
+        return 'No model file selected.', 400
+
+# Add a new route to handle model deployment, profiling, and file download
+@app.route('/deploy_and_download', methods=['GET'])
+def deploy_and_download():
+    global uploaded_model_path, deploy_target,labels_list,deploy_filename
+
+    # Load the model
+    loaded_model = tf.keras.models.load_model(uploaded_model_path)
+
+    # Set model information, such as your list of labels
+    model_output_type = ei.model.output_type.Classification(labels=labels_list)
+
+    # Set model input type
+    model_input_type = ei.model.input_type.OtherInput()
+
+    # Estimate the RAM, ROM, and inference time for our model on the target hardware family
+    try:
+        profile = ei.model.profile(model=loaded_model, device=deploy_target)
+        print(profile.summary())
+    except Exception as e:
+        print(f"Could not profile: {e}")
+
+    # Create C++ library with trained model
+    deploy_bytes = None
+    try:
+        deploy_bytes = ei.model.deploy(model=loaded_model, model_output_type=model_output_type,
+                                       model_input_type=model_input_type, deploy_target="arduino")
+    except Exception as e:
+        print(f"Could not deploy: {e}")
+
+    # Write the downloaded raw bytes to a temporary file
+    if deploy_bytes:
+        temp_deploy_filename = deploy_filename
+        with open(temp_deploy_filename, 'wb') as f:
+            f.write(deploy_bytes.getvalue())
+
+        return send_file(temp_deploy_filename, as_attachment=True)
+    else:
+        return "Model deployment failed."
+    
+
+
+@app.route('/update_model', methods=['POST'])
+def update_model():
+    data = request.json
+
+    # Update the train.py file with the new model architecture and training parameters
+    with open('train.py', 'w') as file:
+        file.write(f"""
+from tensorflow.keras import layers, models, optimizers
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import os
+                   
+# Function to load images and labels from folders
+def load_data(folder_path):
+    images = []
+    labels = []
+    
+    for label in os.listdir(folder_path):
+        label_path = os.path.join(folder_path, label)
+        if os.path.isdir(label_path):
+            for filename in os.listdir(label_path):
+                img_path = os.path.join(label_path, filename)
+                img = cv2.imread(img_path)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
+                images.append(img)
+                labels.append(label)
+                
+    return np.array(images), np.array(labels)
+
+
+# Load data from folders
+data_path = r"C://Users//Admin//Downloads//webappml//data"
+images, labels = load_data(data_path)
+
+# Encode labels
+label_encoder = LabelEncoder()
+encoded_labels = label_encoder.fit_transform(labels)
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(images, encoded_labels, test_size=0.2, random_state=42)
+
+# Normalize pixel values to be between 0 and 1
+X_train, X_test = X_train / 255.0, X_test / 255.0
+
+# Data Augmentation
+datagen = ImageDataGenerator(
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+# Fit the ImageDataGenerator on the training data
+datagen.fit(X_train)
+
+                   
+model = models.Sequential()
+model.add(layers.Conv2D({data['conv1_filters']}, (3, 3), activation='{data['activation_function']}', input_shape=({data['input_shape']}, {data['input_shape']}, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D({data['conv2_filters']}, (3, 3), activation='{data['activation_function']}'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D({data['conv3_filters']}, (3, 3), activation='{data['activation_function']}'))
+model.add(layers.Flatten())
+model.add(layers.Dense({data['dense_units']}, activation='{data['activation_function']}'))
+model.add(layers.Dense(len(set(labels)), activation='softmax'))
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model with augmented data
+model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs={data['epochs']}, validation_data=(X_test, y_test))
+
+# Save the model
+model.save("{data['model_name']}.h5")
+
+print("Model trained with data augmentation and saved successfully.")
+    """)
+
+    return 'Model architecture updated successfully!'
+
+
+
+
+if __name__ == '__main__':
+    # Create 'data' and 'models' folders if they don't exist
+    data_folder = os.path.join(os.getcwd(), 'data')
+    os.makedirs(data_folder, exist_ok=True)
+
+    models_folder = os.path.join(os.getcwd(), 'models')
+    os.makedirs(models_folder, exist_ok=True)
+
+    app.run(debug=True)

requirements.txt

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+Flask==2.0.1
+opencv-python==4.5.3.56
+scikit-learn==0.24.2
+tensorflow==2.6.0
+edgeimpulse-sdk>=1.0.8  # Use >= to get the latest version greater than or equal to X.Y.Z

templates/genftr.py

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+import tensorflow as tf
+import numpy as np
+from tensorflow_io import gfile
+import tensorflow_io as tfio
+from tensorflow.python.ops import gen_audio_ops as audio_ops
+from tqdm.notebook import tqdm
+import matplotlib.pyplot as plt
+from tensorflow.python.ops import gen_audio_ops as audio_ops
+
+# The audio is all sampled at 16KHz and should all be 1 second in length - so 1 second is 16000 samples
+EXPECTED_SAMPLES=16000
+# Noise floor to detect if any audio is present
+NOISE_FLOOR=0.1
+# How many samples should be abover the noise floor?
+MINIMUM_VOICE_LENGTH=EXPECTED_SAMPLES/4
+
+# get all the files in a directory
+def get_files(word):
+    return gfile.glob(SPEECH_DATA + '/'+word+'/*.wav')
+
+# get the location of the voice
+def get_voice_position(audio, noise_floor):
+    audio = audio - np.mean(audio)
+    audio = audio / np.max(np.abs(audio))
+    return tfio.audio.trim(audio, axis=0, epsilon=noise_floor)
+
+# Work out how much of the audio file is actually voice
+def get_voice_length(audio, noise_floor):
+    position = get_voice_position(audio, noise_floor)
+    return (position[1] - position[0]).numpy()
+
+# is enough voice present?
+def is_voice_present(audio, noise_floor, required_length):
+    voice_length = get_voice_length(audio, noise_floor)
+    return voice_length >= required_length
+
+# is the audio the correct length?
+def is_correct_length(audio, expected_length):
+    return (audio.shape[0]==expected_length).numpy()
+
+
+def is_valid_file(file_name):
+    # load the audio file
+    audio_tensor = tfio.audio.AudioIOTensor(file_name)
+    # check the file is long enough
+    if not is_correct_length(audio_tensor, EXPECTED_SAMPLES):
+        return False
+    # convert the audio to an array of floats and scale it to betweem -1 and 1
+    audio = tf.cast(audio_tensor[:], tf.float32)
+    audio = audio - np.mean(audio)
+    audio = audio / np.max(np.abs(audio))
+    # is there any voice in the audio?
+    if not is_voice_present(audio, NOISE_FLOOR, MINIMUM_VOICE_LENGTH):
+        return False
+    return True
+
+
+def get_spectrogram(audio):
+    # normalise the audio
+    audio = audio - np.mean(audio)
+    audio = audio / np.max(np.abs(audio))
+    # create the spectrogram
+    spectrogram = audio_ops.audio_spectrogram(audio,
+                                              window_size=320,
+                                              stride=160,
+                                              magnitude_squared=True).numpy()
+    # reduce the number of frequency bins in our spectrogram to a more sensible level
+    spectrogram = tf.nn.pool(
+        input=tf.expand_dims(spectrogram, -1),
+        window_shape=[1, 6],
+        strides=[1, 6],
+        pooling_type='AVG',
+        padding='SAME')
+    spectrogram = tf.squeeze(spectrogram, axis=0)
+    spectrogram = np.log10(spectrogram + 1e-6)
+    return spectrogram
+
+
+# process a file into its spectrogram
+def process_file(file_path):
+    # load the audio file
+    audio_tensor = tfio.audio.AudioIOTensor(file_path)
+    # convert the audio to an array of floats and scale it to betweem -1 and 1
+    audio = tf.cast(audio_tensor[:], tf.float32)
+    audio = audio - np.mean(audio)
+    audio = audio / np.max(np.abs(audio))
+    # randomly reposition the audio in the sample
+    voice_start, voice_end = get_voice_position(audio, NOISE_FLOOR)
+    end_gap=len(audio) - voice_end
+    random_offset = np.random.uniform(0, voice_start+end_gap)
+    audio = np.roll(audio,-random_offset+end_gap)
+    # add some random background noise
+    background_volume = np.random.uniform(0, 0.1)
+    # get the background noise files
+    background_files = get_files('_background_noise_')
+    background_file = np.random.choice(background_files)
+    background_tensor = tfio.audio.AudioIOTensor(background_file)
+    background_start = np.random.randint(0, len(background_tensor) - 16000)
+    # normalise the background noise
+    background = tf.cast(background_tensor[background_start:background_start+16000], tf.float32)
+    background = background - np.mean(background)
+    background = background / np.max(np.abs(background))
+    # mix the audio with the scaled background
+    audio = audio + background_volume * background
+    # get the spectrogram
+    return get_spectrogram(audio)
+
+train = []
+validate = []
+test = []
+
+TRAIN_SIZE=0.8
+VALIDATION_SIZE=0.1
+TEST_SIZE=0.1
+
+def process_files(file_names, label, repeat=1):
+    file_names = tf.repeat(file_names, repeat).numpy()
+    return [(process_file(file_name), label) for file_name in tqdm(file_names, desc=f"{word} ({label})", leave=False)]
+
+# process the files for a word into the spectrogram and one hot encoding word value
+def process_word(word, label, repeat=1):
+    # get a list of files names for the word
+    file_names = [file_name for file_name in tqdm(get_files(word), desc="Checking", leave=False) if is_valid_file(file_name)]
+    # randomly shuffle the filenames
+    np.random.shuffle(file_names)
+    # split the files into train, validate and test buckets
+    train_size=int(TRAIN_SIZE*len(file_names))
+    validation_size=int(VALIDATION_SIZE*len(file_names))
+    test_size=int(TEST_SIZE*len(file_names))
+    # get the training samples
+    train.extend(
+        process_files(
+            file_names[:train_size],
+            label,
+            repeat=repeat
+        )
+    )
+    # and the validation samples
+    validate.extend(
+        process_files(
+            file_names[train_size:train_size+validation_size],
+            label,
+            repeat=repeat
+        )
+    )
+    # and the test samples
+    test.extend(
+        process_files(
+            file_names[train_size+validation_size:],
+            label,
+            repeat=repeat
+        )
+    )
+
+# process all the command words
+for word in tqdm(command_words, desc="Processing words"):
+    if '_' not in word:
+        repeat = 40 if word in ('forward','backward') else 20
+        process_word(word, command_words.index(word), repeat=repeat)
+        
+# all the nonsense words
+for word in tqdm(nonsense_words, desc="Processing words"):
+    if '_' not in word:
+        process_word(word, command_words.index('_invalid'), repeat=1)
+
+print(len(train), len(test), len(validate))
+
+
+def process_problem_noise(file_name, label):
+    samples = []
+    # load the audio file
+    audio_tensor = tfio.audio.AudioIOTensor(file_name)
+    audio = tf.cast(audio_tensor[:], tf.float32)
+    audio_length = len(audio)
+    samples = []
+    for section_start in tqdm(range(0, audio_length-EXPECTED_SAMPLES, 800), desc=file_name, leave=False):
+        section_end = section_start + EXPECTED_SAMPLES
+        section = audio[section_start:section_end]
+        # get the spectrogram
+        spectrogram = get_spectrogram(section)
+        samples.append((spectrogram, label))
+        
+    np.random.shuffle(samples)
+    
+    train_size=int(TRAIN_SIZE*len(samples))
+    validation_size=int(VALIDATION_SIZE*len(samples))
+    test_size=int(TEST_SIZE*len(samples))
+    
+    train.extend(samples[:train_size])
+    validate.extend(samples[train_size:train_size+validation_size])
+    test.extend(samples[train_size+validation_size:])
+
+
+for file_name in tqdm(get_files("_problem_noise_"), desc="Processing problem noise"):
+    process_problem_noise(file_name, command_words.index("_invalid"))
+
+print(len(train), len(test), len(validate))
+
+# randomise the training samples
+np.random.shuffle(train)
+
+X_train, Y_train = zip(*train)
+X_validate, Y_validate = zip(*validate)
+X_test, Y_test = zip(*test)
+
+
+# save the computed data
+np.savez_compressed(
+    "training_spectrogram.npz",
+    X=X_train, Y=Y_train)
+print("Saved training data")
+np.savez_compressed(
+    "validation_spectrogram.npz",
+    X=X_validate, Y=Y_validate)
+print("Saved validation data")
+np.savez_compressed(
+    "test_spectrogram.npz",
+    X=X_test, Y=Y_test)
+print("Saved test data")
+
+# get the width and height of the spectrogram "image"
+IMG_WIDTH=X_train[0].shape[0]
+IMG_HEIGHT=X_train[0].shape[1]
+
+

templates/index.html

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+<!-- v5 -->
+
+<!DOCTYPE html>
+<html lang="en">
+
+<img src="{{ url_for('static', filename='kudsit_logo.jpeg') }}" alt="Logo" id="site-logo">
+
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>EasyTinyML </title>
+    <style>
+       body {
+            font-family: 'Arial', sans-serif;
+            margin: 0;
+            padding: 0;
+            background: url('https://media.giphy.com/media/CVtNe84hhYF9u/giphy.gif') center center fixed;
+            background-size: cover;
+            color: white;
+        }
+
+        #site-logo {
+            position: fixed;
+            top: 10px;
+            right: 10px;
+            width: 120px;
+            height: auto;
+            z-index: 1000;
+        }
+
+
+        
+      header {
+            background: #222;
+            padding: 10px;
+            text-align: center;
+            color: white;
+            
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin: 10px;
+            text-align: center;
+            color: #ffffff;
+            animation: breathe 4s infinite ease-in-out;
+        }
+
+        h3 {
+            font-size: 1.8em;
+            margin: 10px;
+            text-align: center;
+            color: #db7508;
+            animation: breathe 4s infinite ease-in-out;
+        }
+        h4 {
+            font-size: 1em;
+            margin: 10px;
+            text-align: center;
+            color: #f7f5f3;
+            animation: none;
+        }
+
+        @keyframes breathe {
+            0%, 100% {
+                transform: scale(1);
+            }
+            50% {
+                transform: scale(1.1);
+            }
+        }
+
+        p.sub-para {
+            font-size: 1.2em;
+            margin: 10px;
+            text-align: center;
+            color: #a9a5a5;
+            opacity: 0; /* Initialize opacity to 0 for animation */
+        }
+
+        
+
+        @keyframes bounce {
+            0%, 20%, 50%, 80%, 100% {
+                transform: translateY(0);
+            }
+            40% {
+                transform: translateY(-30px);
+            }
+            60% {
+                transform: translateY(-15px);
+            }
+        }
+
+        h2 {
+            font-size: 1.5em;
+            margin: 10px;
+            text-align: center;
+            color: #333;
+        }
+
+        p {
+            margin: 10px;
+            text-align: center;
+            color: #a09898;
+        }
+
+        form {
+            max-width: 600px;
+            margin: 20px auto;
+            padding: 20px;
+            background: white;
+            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+        }
+
+        label {
+            display: block;
+            margin-bottom: 10px;
+            color: #333;
+        }
+
+        input,
+        select,
+        button {
+            width: 100%;
+            padding: 10px;
+            margin-bottom: 10px;
+            font-size: 14px;
+            color: #555;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            box-sizing: border-box;
+        }
+
+        button {
+            background: #333;
+            color: white;
+            cursor: pointer;
+        }
+
+        button:hover {
+            background: #555;
+        }
+
+        #image-gallery,
+        #labels-list {
+            text-align: center;
+            margin: 20px 0;
+        }
+
+        .social-links {
+            margin-top: 20px;
+        }
+
+         /* Deploy, Profile, and Download Button */
+         button#deploy-button {
+            font-size: 14px;
+            padding: 8px 15px;
+        }
+
+        .social-link {
+            margin: 0 10px;
+            font-size: 18px;
+            color: #fbf2f2;
+            text-decoration: none;
+            transition: transform 0.3s ease-in-out;
+            text-align: center;
+        }
+
+        
+
+             
+
+        
+
+        .social-link:hover {
+            transform: scale(1.2);
+        }
+    </style>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/animejs/3.2.1/anime.min.js"></script>
+</head>
+
+<body>
+    <!-- Header Section -->
+    <header>
+        <h1>EasyTinyML</h1>
+         
+        <p>നിങ്ങൾക്കും ചെയ്യാം TinyML</p>
+        <p>Developed by Harisankar R, MSc Electronics, Digital University Kerala</p>
+        
+    </header>
+    
+
+    <!-- Main Content Section -->
+    <div>
+
+        <h3>Object Classification</h3>
+        <p class="sub-para">Try your model now</p>
+
+        <!-- Image Upload Form -->
+        <form action="/upload" method="post" enctype="multipart/form-data">
+        <label for="images">Upload Images or Capture from Camera:</label>
+        <input type="file" name="images" id="images" accept="image/*" capture="camera" multiple>
+        <label for="label">Enter Label:</label>
+        <input type="text" name="label" id="label" placeholder="Enter Label">
+        <button type="submit">Upload Images</button>
+        </form>
+
+
+        <!-- Edit Model Form -->
+        <form action="/update_model" method="post" onsubmit="return updateModel()">
+            <h2>Edit Model Architecture</h2>
+            <label for="conv1_filters">Conv1 Filters:</label>
+            <input type="number" name="conv1_filters" id="conv1_filters" value="32">
+            <label for="conv2_filters">Conv2 Filters:</label>
+            <input type="number" name="conv2_filters" id="conv2_filters" value="64">
+            <label for="conv3_filters">Conv3 Filters:</label>
+            <input type="number" name="conv3_filters" id="conv3_filters" value="64">
+            <label for="dense_units">Dense Units:</label>
+            <input type="number" name="dense_units" id="dense_units" value="64">
+            <label for="input_shape">Input Shape (e.g., 224 for 224x224):</label>
+            <input type="number" name="input_shape" id="input_shape" value="224">
+            <label for="activation_function">Activation Function:</label>
+            <select name="activation_function" id="activation_function">
+                <option value="relu" selected>ReLU</option>
+                <option value="sigmoid">Sigmoid</option>
+                <option value="tanh">Tanh</option>
+            </select>
+            <h2>Training Parameters</h2>
+            <label for="epochs">Epochs:</label>
+            <input type="number" name="epochs" id="epochs" value="10">
+            <label for="model_name">Model Name:</label>
+            <input type="text" name="model_name" id="model_name" placeholder="Enter model name">
+            <button type="submit">Update Model</button>
+
+            <button type="button" onclick="trainModel()">Train Model</button>
+            <button onclick="downloadTrainedModel()">Download Trained Model</button>
+
+        </form>
+        
+
+        <!-- Add this div to your HTML where you want to display the training output -->
+        <div id="train-output" style="max-width: 600px; margin: 20px auto; padding: 20px; background: #0074bc; box-shadow: 0 0 10px rgba(234, 5, 5, 0.1); white-space: pre-line;"></div>
+
+
+        
+
+
+        <!-- Set Deploy File Name Form -->
+        <form action="/set_deploy_filename" method="post">
+            <label for="deploy_filename">Enter Deploy File Name:</label>
+            <input type="text" name="deploy_filename" id="deploy_filename" placeholder="File Name with .zip">
+            <button type="submit">Set Deploy File Name</button>
+        </form>
+
+        <!-- Add Labels Form -->
+        <form action="/add_labels" method="post" onsubmit="return addLabels()">
+            <label for="labels">Enter Labels (comma-separated):</label>
+            <input type="text" name="labels" id="labels" placeholder="ex1,ex2,...">
+            <button type="submit">Add Labels</button>
+        </form>
+
+        <!-- Labels Entered -->
+        <h4>Labels Entered</h4>
+        <div id="labels-list"></div>
+        <button onclick="clearLabels()">Clear Labels</button>
+
+        <!-- Set Deploy Target Form -->
+        <form action="/set_deploy_target" method="post">
+            <label for="deploy_target">Select Deploy Target:</label>
+            <select name="deploy_target" id="deploy_target">
+                <option value="arduino-nano-33-ble">Arduino Nano 33 BLE</option>
+                <option value="espressif-esp32">Espressif ESP32</option>
+            </select>
+            <button type="submit">Set Deploy Target</button>
+        </form>
+
+        <!-- Upload Model Form -->
+        <form action="/upload_model" method="post" enctype="multipart/form-data">
+            <label for="model">Upload Model File:</label>
+            <input type="file" name="model" id="model" accept=".h5">
+            <button type="submit">Upload Model</button>
+        </form>
+
+        <!-- Deploy, Profile, and Download Button -->
+        <button id="deploy-button" onclick="deployAndDownload()">Deploy, Profile, and Download</button>
+
+        <!-- Uploaded Images -->
+        <h4>Uploaded Images</h4>
+        <div id="image-gallery"></div>
+
+        
+    </div>
+
+    <!-- Contact Section -->
+    <section>
+        <h4>Contact</h4>
+        <p>Email: [email protected]</p>
+    </section>
+
+    <!-- Social Links -->
+    <div class="social-links">
+        <a href="https://www.instagram.com/harisankarrj" class="social-link" target="_blank">Instagram</a>
+        <!-- Add more social links as needed -->
+    </div>
+
+    <!-- AI GIF -->
+    
+    <script>
+
+        anime({
+            targets: 'h3',
+            translateY: [-30, 0],
+            opacity: [0, 1],
+            easing: 'easeOutExpo',
+            duration: 1500,
+            delay: 500
+        });
+
+        anime({
+            targets: 'p.sub-para',
+            translateY: [30, 0],
+            opacity: [0, 1],
+            easing: 'easeOutExpo',
+            duration: 1500,
+            delay: 1000
+        });
+        function runApp1() {
+            fetch('/run_app1')
+                .then(response => response.text())
+                .then(data => console.log(data))
+                .catch(error => console.error('Error:', error));
+        }
+
+        function updateLabels() {
+            fetch('/get_labels')
+                .then(response => response.json())
+                .then(data => {
+                    const labelsList = document.getElementById('labels-list');
+                    labelsList.innerHTML = '<strong>Labels Entered:</strong> ' + data.join(', ');
+                })
+                .catch(error => console.error('Error:', error));
+        }
+
+        function clearLabels() {
+            fetch('/clear_labels')
+                .then(response => response.text())
+                .then(data => {
+                    console.log(data);
+                    updateLabels();
+                    location.reload(); // Reload the page
+                })
+                .catch(error => console.error('Error:', error));
+            return false; // Prevent form submission
+        }
+
+        function updateModel() {
+        const conv1_filters = document.getElementById('conv1_filters').value;
+        const conv2_filters = document.getElementById('conv2_filters').value;
+        const conv3_filters = document.getElementById('conv3_filters').value;
+        const dense_units = document.getElementById('dense_units').value;
+        const input_shape = document.getElementById('input_shape').value;
+        const activation_function = document.getElementById('activation_function').value;
+        const epochs = document.getElementById('epochs').value;
+        const model_name = document.getElementById('model_name').value;
+
+        // Send the data to the server using fetch or AJAX
+        fetch('/update_model', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                conv1_filters: conv1_filters,
+                conv2_filters: conv2_filters,
+                conv3_filters: conv3_filters,
+                dense_units: dense_units,
+                input_shape: input_shape,
+                activation_function: activation_function,
+                epochs: epochs,
+                model_name: model_name,
+            }),
+        })
+        .then(response => response.text())
+        .then(data => {
+            console.log(data);
+            alert('Model architecture updated successfully!');
+        })
+        .catch(error => {
+            console.error('Error:', error);
+            alert('Error updating model architecture.');
+        });
+
+        return false; // Prevent form submission
+    }
+
+
+        function deployAndDownload() {
+            fetch('/deploy_and_download')
+                .then(response => response.blob())
+                .then(blob => {
+                    const url = window.URL.createObjectURL(new Blob([blob]));
+                    const a = document.createElement('a');
+                    a.href = url;
+                    a.download = 'EasyTinyMl_model.zip'; // Set the desired filename
+                    document.body.appendChild(a);
+                    a.click();
+                    document.body.removeChild(a);
+                    window.URL.revokeObjectURL(url);
+                })
+                .catch(error => console.error('Error deploying and downloading model:', error));
+        }
+
+        function addLabels() {
+            fetch('/add_labels', {
+                method: 'POST',
+                body: new FormData(document.querySelector('form[action="/add_labels"]'))
+            })
+                .then(response => response.text())
+                .then(data => {
+                    console.log(data);
+                    updateLabels();
+                    location.reload(); // Reload the page
+                })
+                .catch(error => console.error('Error:', error));
+            return false; // Prevent form submission
+        }
+
+        function trainModel() {
+         fetch('/train_model')
+        .then(response => response.text())
+        .then(data => {
+            // Display the output in the train-output element
+            const trainOutput = document.getElementById('train-output');
+            trainOutput.innerHTML = '<strong>Train Output:</strong><br>' + data;
+        })
+        .catch(error => console.error('Error:', error));
+        }
+
+
+        function downloadTrainedModel() {
+        const modelName = document.getElementById('model_name').value;  // Get the model name from the input field
+        const url = `/download_trained_model?model_name=${modelName}`;
+
+        fetch(url)
+            .then(response => {
+                const disposition = response.headers.get('content-disposition');
+                const filenameRegex = /filename[^;=\n]*=((['"]).*?\2|[^;\n]*)/;
+                const matches = filenameRegex.exec(disposition);
+                let filename = matches && matches[1] ? matches[1] : 'download';
+
+                // Ensure the filename has the .h5 extension
+                if (!filename.toLowerCase().endsWith('.h5')) {
+                    filename += '.h5';
+                }
+
+                return response.blob().then(blob => {
+                    const a = document.createElement('a');
+                    a.href = URL.createObjectURL(blob);
+                    a.download = filename;
+                    document.body.appendChild(a);
+                    a.click();
+                    document.body.removeChild(a);
+                });
+            })
+            .catch(error => console.error('Error downloading trained model:', error));
+        }
+        // Update labels on page load
+        updateLabels();
+    </script>
+</body>
+
+</html>

train.py

@@ -0,0 +1,77 @@
+
+from tensorflow.keras import layers, models, optimizers
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import os
+                   
+# Function to load images and labels from folders
+def load_data(folder_path):
+    images = []
+    labels = []
+    
+    for label in os.listdir(folder_path):
+        label_path = os.path.join(folder_path, label)
+        if os.path.isdir(label_path):
+            for filename in os.listdir(label_path):
+                img_path = os.path.join(label_path, filename)
+                img = cv2.imread(img_path)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
+                images.append(img)
+                labels.append(label)
+                
+    return np.array(images), np.array(labels)
+
+
+# Load data from folders
+data_path = r"C://Users//Admin//Downloads//webappml//data"
+images, labels = load_data(data_path)
+
+# Encode labels
+label_encoder = LabelEncoder()
+encoded_labels = label_encoder.fit_transform(labels)
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(images, encoded_labels, test_size=0.2, random_state=42)
+
+# Normalize pixel values to be between 0 and 1
+X_train, X_test = X_train / 255.0, X_test / 255.0
+
+# Data Augmentation
+datagen = ImageDataGenerator(
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+# Fit the ImageDataGenerator on the training data
+datagen.fit(X_train)
+
+                   
+model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(len(set(labels)), activation='softmax'))
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model with augmented data
+model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs=10, validation_data=(X_test, y_test))
+
+# Save the model
+model.save("harisankar.h5")
+
+print("Model trained with data augmentation and saved successfully.")
+    

tranformer.py

@@ -0,0 +1,3 @@
+from transformers.hub import login
+
+login(username="your_username", password="your_password")

webapp.ipynb

@@ -0,0 +1,837 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'google'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display, HTML, Image\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mbase64\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m files\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Function to display HTML\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google'"
+     ]
+    }
+   ],
+   "source": [
+    "from IPython.display import display, HTML, Image\n",
+    "import base64\n",
+    "from google.colab import files\n",
+    "import os\n",
+    "\n",
+    "# Function to display HTML\n",
+    "def display_html(html):\n",
+    "    display(HTML(html))\n",
+    "\n",
+    "# HTML code for a webpage with image folder upload and label entry\n",
+    "html_code = \"\"\"\n",
+    "<!DOCTYPE html>\n",
+    "<html>\n",
+    "<head>\n",
+    "    <title>Object Detection Web App</title>\n",
+    "</head>\n",
+    "<body>\n",
+    "    <h1>Welcome to Object Detection Web App</h1>\n",
+    "    <p>This is a simple web page created in Google Colab.</p>\n",
+    "\n",
+    "    <form action=\"javascript:void(0);\" id=\"upload-form\" enctype=\"multipart/form-data\">\n",
+    "        <input type=\"file\" id=\"folder-input\" webkitdirectory directory multiple>\n",
+    "        <input type=\"text\" id=\"label-input\" placeholder=\"Enter Label\">\n",
+    "        <input type=\"button\" value=\"Upload Images\" onclick=\"uploadImages()\">\n",
+    "    </form>\n",
+    "\n",
+    "    <h2>Uploaded Images</h2>\n",
+    "    <div id=\"image-gallery\"></div>\n",
+    "\n",
+    "    <script>\n",
+    "        function uploadImages() {\n",
+    "            var folderInput = document.getElementById('folder-input');\n",
+    "            var labelInput = document.getElementById('label-input');\n",
+    "            var gallery = document.getElementById('image-gallery');\n",
+    "\n",
+    "            var files = folderInput.files;\n",
+    "            var label = labelInput.value.trim();\n",
+    "\n",
+    "            if (label === \"\") {\n",
+    "                alert(\"Please enter a label for the images.\");\n",
+    "                return;\n",
+    "            }\n",
+    "\n",
+    "            for (var i = 0; i < files.length; i++) {\n",
+    "                var reader = new FileReader();\n",
+    "\n",
+    "                reader.onload = function(){\n",
+    "                    var img = document.createElement('img');\n",
+    "                    img.src = reader.result;\n",
+    "                    gallery.appendChild(img);\n",
+    "                };\n",
+    "\n",
+    "                reader.readAsDataURL(files[i]);\n",
+    "            }\n",
+    "\n",
+    "            // Save images to the Colab environment with labels as folder names\n",
+    "            var formData = new FormData();\n",
+    "            formData.append('label', label);\n",
+    "            for (var i = 0; i < files.length; i++) {\n",
+    "                formData.append('images', files[i]);\n",
+    "            }\n",
+    "\n",
+    "            fetch('/upload', {\n",
+    "                method: 'POST',\n",
+    "                body: formData\n",
+    "            }).then(response => response.text())\n",
+    "            .then(data => console.log(data))\n",
+    "            .catch(error => console.error('Error:', error));\n",
+    "        }\n",
+    "    </script>\n",
+    "</body>\n",
+    "</html>\n",
+    "\"\"\"\n",
+    "\n",
+    "# Save HTML code to an HTML file\n",
+    "html_file_path = '/content/object_detection_web_app.html'\n",
+    "with open(html_file_path, 'w') as f:\n",
+    "    f.write(html_code)\n",
+    "\n",
+    "# Display a link to download the HTML file\n",
+    "display_html(f'<a href=\"{html_file_path}\" target=\"_blank\">Download HTML File and Open Locally</a>')\n",
+    "\n",
+    "# Image upload logic\n",
+    "uploaded = files.upload()\n",
+    "for label, images in uploaded.items():\n",
+    "    label_dir = os.path.join('/content/', label)\n",
+    "    os.makedirs(label_dir, exist_ok=True)\n",
+    "    for image_name, image_data in images.items():\n",
+    "        image_path = os.path.join(label_dir, image_name)\n",
+    "        with open(image_path, 'wb') as f:\n",
+    "            f.write(image_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting edgeimpulse\n",
+      "  Downloading edgeimpulse-1.0.8-py3-none-any.whl (44 kB)\n",
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+      "Requirement already satisfied: requests<3.0.0,>=2.23.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from edgeimpulse) (2.31.0)\n",
+      "Requirement already satisfied: python_dateutil<3.0.0,>=2.5.3 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from edgeimpulse-api==1.40.11->edgeimpulse) (2.8.2)\n",
+      "Collecting aenum<4.0.0,>=3.1.11\n",
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+      "Collecting pydantic<2.0.0,>=1.10.2\n",
+      "  Downloading pydantic-1.10.13-cp310-cp310-win_amd64.whl (2.1 MB)\n",
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+      "     -------------------------------------- - 2.0/2.1 MB 3.1 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 2.1/2.1 MB 3.1 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests<3.0.0,>=2.23.0->edgeimpulse) (3.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests<3.0.0,>=2.23.0->edgeimpulse) (2022.12.7)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests<3.0.0,>=2.23.0->edgeimpulse) (2.1.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.2.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pydantic<2.0.0,>=1.10.2->edgeimpulse-api==1.40.11->edgeimpulse) (4.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python_dateutil<3.0.0,>=2.5.3->edgeimpulse-api==1.40.11->edgeimpulse) (1.16.0)\n",
+      "Installing collected packages: aenum, pydantic, edgeimpulse-api, edgeimpulse\n",
+      "Successfully installed aenum-3.1.15 edgeimpulse-1.0.8 edgeimpulse-api-1.40.11 pydantic-1.10.13\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.0 -> 23.3.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install edgeimpulse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Could not find a version that satisfies the requirement git (from versions: none)\n",
+      "ERROR: No matching distribution found for git\n",
+      "\n",
+      "[notice] A new release of pip is available: 23.0 -> 23.3.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow.keras (from versions: none)\u001b[0m\n",
+      "\u001b[31mERROR: No matching distribution found for tensorflow.keras\u001b[0m\n",
+      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.3.2 is available.\n",
+      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "1/1 [==============================] - 0s 398ms/step - loss: 0.6844 - accuracy: 0.6667 - val_loss: 0.7236 - val_accuracy: 0.5000\n",
+      "Epoch 2/10\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.6936 - accuracy: 0.5000 - val_loss: 0.7161 - val_accuracy: 0.5000\n",
+      "Epoch 3/10\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.6760 - accuracy: 0.6667 - val_loss: 0.7166 - val_accuracy: 0.5000\n",
+      "Epoch 4/10\n",
+      "1/1 [==============================] - 0s 22ms/step - loss: 0.6790 - accuracy: 0.6667 - val_loss: 0.7060 - val_accuracy: 0.5000\n",
+      "Epoch 5/10\n",
+      "1/1 [==============================] - 0s 22ms/step - loss: 0.6759 - accuracy: 0.8333 - val_loss: 0.6996 - val_accuracy: 0.5000\n",
+      "Epoch 6/10\n",
+      "1/1 [==============================] - 0s 23ms/step - loss: 0.6701 - accuracy: 0.6667 - val_loss: 0.6999 - val_accuracy: 0.5000\n",
+      "Epoch 7/10\n",
+      "1/1 [==============================] - 0s 22ms/step - loss: 0.6721 - accuracy: 0.6667 - val_loss: 0.6896 - val_accuracy: 0.5000\n",
+      "Epoch 8/10\n",
+      "1/1 [==============================] - 0s 22ms/step - loss: 0.6599 - accuracy: 0.6667 - val_loss: 0.6745 - val_accuracy: 1.0000\n",
+      "Epoch 9/10\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.6509 - accuracy: 0.8333 - val_loss: 0.6739 - val_accuracy: 0.5000\n",
+      "Epoch 10/10\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.6237 - accuracy: 0.8333 - val_loss: 0.6647 - val_accuracy: 0.5000\n",
+      "Model trained with data augmentation and saved successfully.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/varunsankar/Library/Python/3.9/lib/python/site-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+      "  saving_api.save_model(\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from tensorflow.keras import layers, models, optimizers\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from tensorflow.keras import layers, models, optimizers\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "                   \n",
+    "# Function to load images and labels from folders\n",
+    "def load_data(folder_path):\n",
+    "    images = []\n",
+    "    labels = []\n",
+    "    \n",
+    "    for label in os.listdir(folder_path):\n",
+    "        label_path = os.path.join(folder_path, label)\n",
+    "        if os.path.isdir(label_path):\n",
+    "            for filename in os.listdir(label_path):\n",
+    "                img_path = os.path.join(label_path, filename)\n",
+    "                img = cv2.imread(img_path)\n",
+    "                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB\n",
+    "                images.append(img)\n",
+    "                labels.append(label)\n",
+    "                \n",
+    "    return np.array(images), np.array(labels)\n",
+    "\n",
+    "\n",
+    "# Load data from folders\n",
+    "data_path = \"/Users/varunsankar/webappml/data\"\n",
+    "images, labels = load_data(data_path)\n",
+    "\n",
+    "# Encode labels\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_labels = label_encoder.fit_transform(labels)\n",
+    "\n",
+    "# Split data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(images, encoded_labels, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Normalize pixel values to be between 0 and 1\n",
+    "X_train, X_test = X_train / 255.0, X_test / 255.0\n",
+    "\n",
+    "# Data Augmentation\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rotation_range=20,\n",
+    "    width_shift_range=0.2,\n",
+    "    height_shift_range=0.2,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='nearest'\n",
+    ")\n",
+    "\n",
+    "# Fit the ImageDataGenerator on the training data\n",
+    "datagen.fit(X_train)\n",
+    "\n",
+    "                   \n",
+    "model = models.Sequential()\n",
+    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 3)))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(layers.MaxPooling2D((2, 2)))\n",
+    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(layers.Flatten())\n",
+    "model.add(layers.Dense(64, activation='relu'))\n",
+    "model.add(layers.Dense(len(set(labels)), activation='softmax'))\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Train the model with augmented data\n",
+    "model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs=10, validation_data=(X_test, y_test))\n",
+    "\n",
+    "# Save the model\n",
+    "model.save(\"01objcls.h5\")\n",
+    "\n",
+    "print(\"Model trained with data augmentation and saved successfully.\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from tensorflow.keras import layers, models, optimizers\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flask version: 3.0.0\n",
+      "OpenCV version: 4.9.0\n",
+      "scikit-learn version: 1.2.1\n",
+      "NumPy version: 1.23.5\n",
+      "TensorFlow version: 2.15.0\n",
+      "Edge Impulse SDK version: 1.0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_16592\\2789360100.py:8: DeprecationWarning: The '__version__' attribute is deprecated and will be removed in Flask 3.1. Use feature detection or 'importlib.metadata.version(\"flask\")' instead.\n",
+      "  print(f'Flask version: {flask.__version__}')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import flask\n",
+    "import cv2\n",
+    "import sklearn\n",
+    "import numpy\n",
+    "import tensorflow\n",
+    "import edgeimpulse as ei\n",
+    "\n",
+    "print(f'Flask version: {flask.__version__}')\n",
+    "print(f'OpenCV version: {cv2.__version__}')\n",
+    "print(f'scikit-learn version: {sklearn.__version__}')\n",
+    "print(f'NumPy version: {numpy.__version__}')\n",
+    "print(f'TensorFlow version: {tensorflow.__version__}')\n",
+    "print(f'Edge Impulse SDK version: {ei.__version__}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flask import Flask, render_template, request, jsonify\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from tensorflow.keras import layers, models, optimizers\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from werkzeug.utils import secure_filename\n",
+    "import subprocess\n",
+    "from flask import send_file\n",
+    "from tensorflow import keras\n",
+    "import edgeimpulse as ei\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'hs1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m loaded_model \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mload_model(\u001b[43mhs1\u001b[49m\u001b[38;5;241m.\u001b[39mh5)\n\u001b[0;32m      3\u001b[0m model_output_type \u001b[38;5;241m=\u001b[39m ei\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39moutput_type\u001b[38;5;241m.\u001b[39mClassification(labels\u001b[38;5;241m=\u001b[39mlabels_list)\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;66;03m# Set model input type\u001b[39;00m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'hs1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_model = tf.keras.models.load_model(hs1.h5)\n",
+    "\n",
+    "model_output_type = ei.model.output_type.Classification(labels=labels_list)\n",
+    "\n",
+    "    # Set model input type\n",
+    "model_input_type = ei.model.input_type.OtherInput()\n",
+    "\n",
+    "    # Estimate the RAM, ROM, and inference time for our model on the target hardware family\n",
+    "try:\n",
+    "        profile = ei.model.profile(model=loaded_model, device=deploy_target)\n",
+    "        print(profile.summary())\n",
+    "except Exception as e:\n",
+    "        print(f\"Could not profile: {e}\")\n",
+    "\n",
+    "    # Create C++ library with trained model\n",
+    "deploy_bytes = None\n",
+    "try:\n",
+    "        deploy_bytes = ei.model.deploy(model=loaded_model, model_output_type=model_output_type,\n",
+    "                                       model_input_type=model_input_type, deploy_target=\"arduino\")\n",
+    "except Exception as e:\n",
+    "        print(f\"Could not deploy: {e}\")\n",
+    "\n",
+    "    # Write the downloaded raw bytes to a temporary file\n",
+    "if deploy_bytes:\n",
+    "        temp_deploy_filename = deploy_filename\n",
+    "        with open(temp_deploy_filename, 'wb') as f:\n",
+    "            f.write(deploy_bytes.getvalue())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3bd84c6323547fcbc3a934699437777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8651fd575bab40938bead99198d52e40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "aromal.h5:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc22c64c32b2454eb0d9a9ef2df77b98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "01objclsmodel.h5:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3db37bea27324d8fb86dbd24efe33ca6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "dep12.zip:   0%|          | 0.00/5.62M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94231342fe854ae9b329c0ebd2042af6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "arseny-togulev-MECKPoKJYjM-unsplash.jpg:   0%|          | 0.00/6.58M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7bfb9fa022a4919becfac4a4632998e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 16 LFS files:   0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0086090632e2497bbf4e23c3b2989175",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "fsr.h5:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "392292e08fb64f5c8b2ed26409101610",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "harisankar.h5:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1475a4773b6346b68f49109de799c39c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "harisankarrj.h5:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66766018b29947b485a839cf7bd87705",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
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+    },
+    {
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+     },
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "model_id": "1dfd55327baa4b2d98fb3e1f9e51e5ca",
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+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/spaces/harisankarrj/EasyTinyML/commit/c323141fefd39041db3d6c31ace713a9134b934b', commit_message='Upload folder using huggingface_hub', commit_description='', oid='c323141fefd39041db3d6c31ace713a9134b934b', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "api = HfApi()\n",
+    "\n",
+    "api.upload_folder(\n",
+    "    folder_path=r\"C:\\Users\\Admin\\Downloads\\webappml\",\n",
+    "    repo_id=\"harisankarrj/EasyTinyML\",\n",
+    "    repo_type=\"space\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}