File size: 3,960 Bytes
934577c 49731b0 934577c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from PIL import Image
import gdown
import zipfile
import pathlib
# Define the Google Drive shareable link
gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
# Extract the file ID from the URL
file_id = gdrive_url.split('/d/')[1].split('/view')[0]
direct_download_url = f'https://drive.google.com/uc?id={file_id}'
# Define the local filename to save the ZIP file
local_zip_file = 'file.zip'
# Download the ZIP file
gdown.download(direct_download_url, local_zip_file, quiet=False)
# Directory to extract files
extracted_path = 'extracted_files'
# Verify if the downloaded file is a ZIP file and extract it
try:
with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:
zip_ref.extractall(extracted_path)
print("Extraction successful!")
except zipfile.BadZipFile:
print("Error: The downloaded file is not a valid ZIP file.")
# Optionally, you can delete the ZIP file after extraction
os.remove(local_zip_file)
# Convert the extracted directory path to a pathlib.Path object
data_dir = pathlib.Path(extracted_path)
# Print the directory structure to debug
for root, dirs, files in os.walk(extracted_path):
level = root.replace(extracted_path, '').count(os.sep)
indent = ' ' * 4 * (level)
print(f"{indent}{os.path.basename(root)}/")
subindent = ' ' * 4 * (level + 1)
for f in files:
print(f"{subindent}{f}")
import pathlib
# Path to the dataset directory
data_dir = pathlib.Path('extracted_files/Pest_Dataset')
data_dir = pathlib.Path(data_dir)
img_height,img_width=180,180
batch_size=32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
num_classes = 12
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes,activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
import gradio as gr
import numpy as np
import tensorflow as tf
def predict_image(img):
img = np.array(img)
img_resized = tf.image.resize(img, (180, 180))
img_4d = tf.expand_dims(img_resized, axis=0)
prediction = model.predict(img_4d)[0]
return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
image = gr.Image()
label = gr.Label(num_top_classes=5)
gr.Interface(
fn=predict_image,
inputs=image,
outputs=label,
title="Pest Classification",
description="Upload an image of a pest to classify it into one of the predefined categories.",
css=custom_css
).launch(debug=True) |