update data

.gitignore

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+
+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coveage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+pytestdebug.log
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+doc/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pythonenv*
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# profiling data
+.prof
+
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+.Python
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
+pyvenv.cfg
+.venv
+pip-selfcheck.json
+
+
+# End of https://www.toptal.com/developers/gitignore/api/python

.vscode/settings.json

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+{
+    "liveServer.settings.port": 9001
+}

Procfile

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+web: gunicorn main:app

app.py

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+# import os
+# from flask import Flask, request, render_template, send_from_directory
+# from sklearn import model_selection
+# from werkzeug.utils import secure_filename
+# from tensorflow.keras.applications.imagenet_utils import preprocess_input
+# from tensorflow.keras.models import load_model, model_from_json
+# from tensorflow.keras.preprocessing.image import load_img, img_to_array
+# import numpy as np
+# from PIL import Image
+# import imghdr
+# import logging
+# import io
+
+# app = Flask(__name__)
+
+# MODEL_ARCHITECTURE = 'model/clean721.json'
+# MODEL_WEIGHTS = 'model/clean721.h5'
+
+# PREDICTION_CLASSES = {
+#     0: ('Gambar Tidak Cocok', 'klasifikasi-noklasifikasi.html'),
+#     1: ('Tanaman Padimu Terkena', 'klasifikasi-bl.html'),
+#     2: ('Tanaman Padimu Terkena', 'klasifikasi-blb.html'),
+#     3: ('Tanaman Padimu Terkena', 'klasifikasi-bw.html'),
+#     4: ('Tanaman', 'klasifikasi-sh.html'),
+# }
+
+# def load_model_from_file():
+#     try:
+#         with open(MODEL_ARCHITECTURE, 'r') as json_file:
+#             loaded_model_json = json_file.read()
+#         model = model_from_json(loaded_model_json)
+#         model.load_weights(MODEL_WEIGHTS)
+#         return model
+#     except Exception as e:
+#         logging.error(f"Error loading model: {str(e)}")
+#         return None
+    
+# model = load_model_from_file()
+
+# def model_predict(img_path, model):
+#     try:
+#         TARGET_IMAGE_SIZE = (224, 224)
+#         test_image = load_img(img_path, target_size=TARGET_IMAGE_SIZE)
+#         logging.info("Got Image for prediction")
+
+#         test_image = img_to_array(test_image) / 255
+#         test_image = np.expand_dims(test_image, axis=0)
+
+#         result = model.predict(test_image)
+#         pred_class = np.argmax(result, axis=1)[0]
+#         pred_prob = result[0][pred_class] * 100
+
+#         return PREDICTION_CLASSES[pred_class][0], PREDICTION_CLASSES[pred_class][1], pred_prob
+#     except Exception as e:
+#         logging.error(f"Error predicting: {str(e)}")
+#         return 'Error', 'error.html', 0
+    
+# @app.route("/", methods=['GET'])
+# def home():
+#     return render_template('index.html')
+
+# @app.route("/blog", methods=['GET'])
+# def blog():
+#     return render_template('blog.html')
+
+# @app.route("/contact", methods=['GET'])
+# def contact():
+#     return render_template('contact.html')
+
+# @app.route("/klasifikasirgb", methods=['GET', 'POST'])
+# def klasifikasirgb():
+#     return render_template('klasifikasirgb.html')
+
+# @app.route('/process_image', methods=['POST'])
+# def process_image():
+#     file = request.files['image']
+    
+#     original_image = Image.open(io.BytesIO(file.read()))
+#     resized_image = original_image.resize((4, 4))
+
+#     pixel_values = np.array(resized_image)
+
+#     blue_values = pixel_values[:, :, 2]
+#     green_values = pixel_values[:, :, 1]
+#     red_values = pixel_values[:, :, 0]
+
+#     print("Blue values:")
+#     print(blue_values)
+
+#     print("\nGreen values:")
+#     print(green_values)
+
+#     print("\nRed values:")
+#     print(red_values)
+
+#     return render_template('result.html', blue_values=blue_values, green_values=green_values, red_values=red_values)
+
+# @app.route("/klasifikasi", methods=['GET', 'POST'])
+# def klasifikasi():
+#     return render_template('klasifikasi.html')
+
+# @app.route("/predict", methods=['GET', 'POST'])
+# def predict():
+#     if request.method == 'POST':
+#         # Memeriksa apakah file yang dipilih adalah gambar
+#         file = request.files['image']
+#         if file.filename == '':
+#             flash('Tidak ada file yang dipilih')
+#             return redirect(request.url)
+#         if file and imghdr.what(file) in ['png', 'jpeg', 'gif']:
+#             filename = secure_filename(file.filename)
+#             logging.info("@@ Input posted =", filename)
+
+#             file_path = os.path.join('static', 'user_uploaded', filename)
+#             file.save(file_path)
+
+#             logging.info("@@ Predicting class...")
+#             pred_class, output_page, pred_prob = model_predict(file_path, model)
+
+#             return render_template(output_page, pred_output=pred_class, pred_prob=pred_prob, user_image=file_path)
+#         else:
+#             flash('Format file tidak didukung')
+#             return redirect(request.url)
+
+#     # Menambahkan pesan flash untuk menampilkan pesan kesalahan
+#     flash('Metode permintaan tidak valid')
+#     return redirect(request.url)
+    
+# if __name__ == "__main__":
+#     app.run(debug=True)
+import os
+import io
+import logging
+from flask import Flask, request, render_template
+from werkzeug.utils import secure_filename
+from tensorflow.keras.models import model_from_json
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+from PIL import Image
+
+app = Flask(__name__)
+app.secret_key = "secret!@8102"
+
+MODEL_ARCHITECTURE = 'model/clean721.json'
+MODEL_WEIGHTS = 'model/clean721.h5'
+
+PREDICTION_CLASSES = {
+    0: ('Gambar Tidak Cocok', 'klasifikasi-noklasifikasi.html'),
+    1: ('Tanaman Padimu Terkena', 'klasifikasi-bl.html'),
+    2: ('Tanaman Padimu Terkena', 'klasifikasi-blb.html'),
+    3: ('Tanaman Padimu Terkena', 'klasifikasi-bw.html'),
+    4: ('Tanaman', 'klasifikasi-sh.html'),
+}
+def load_model_from_file():
+    try:
+        with open(MODEL_ARCHITECTURE, 'r') as json_file:
+            loaded_model_json = json_file.read()
+        model = model_from_json(loaded_model_json)
+        model.load_weights(MODEL_WEIGHTS)
+        return model
+    except Exception as e:
+        logging.error(f"Error loading model: {str(e)}")
+        return None
+
+model = load_model_from_file()
+
+def model_predict(img_path, model):
+    try:
+        TARGET_IMAGE_SIZE = (224, 224)
+        test_image = load_img(img_path, target_size=TARGET_IMAGE_SIZE)
+        logging.info("@@ Got Image for prediction")
+
+        test_image = img_to_array(test_image) / 255
+        test_image = np.expand_dims(test_image, axis=0)
+
+        result = model.predict(test_image)
+        pred_class = np.argmax(result, axis=1)[0]
+        pred_prob = result[0][pred_class] * 100
+
+        return PREDICTION_CLASSES[pred_class][0], PREDICTION_CLASSES[pred_class][1], pred_prob
+    except Exception as e:
+        logging.error(f"Error predicting: {str(e)}")
+        return 'Error', 'error.html', 0
+
+@app.route("/", methods=['GET'])
+def home():
+    return render_template('index.html')
+
+@app.route("/blog", methods=['GET'])
+def blog():
+    return render_template('blog.html')
+
+@app.route("/contact", methods=['GET'])
+def contact():
+    return render_template('contact.html')
+
+@app.route("/klasifikasirgb", methods=['GET', 'POST'])
+def klasifikasirgb():
+    return render_template('klasifikasirgb.html')
+
+@app.route('/process_image', methods=['POST'])
+def process_image():
+    file = request.files['image']
+
+    original_image = Image.open(io.BytesIO(file.read()))
+    resized_image = original_image.resize((4, 4))
+
+    pixel_values = np.array(resized_image)
+
+    blue_values = pixel_values[:, :, 2]
+    green_values = pixel_values[:, :, 1]
+    red_values = pixel_values[:, :, 0]
+
+    return render_template('result.html', blue_values=blue_values, green_values=green_values, red_values=red_values)
+
+@app.route("/klasifikasi", methods=['GET', 'POST'])
+def klasifikasi():
+    return render_template('klasifikasi.html')
+
+@app.route("/predict", methods=['POST'])
+def predict():
+    if request.method == 'POST':
+        file = request.files['image']
+        filename = secure_filename(file.filename)
+        logging.info("@@ Input posted =", filename)
+
+        file_path = os.path.join('static', 'user_uploaded', filename)
+        file.save(file_path)
+
+        logging.info("@@ Predicting class...")
+        pred_class, output_page, pred_prob = model_predict(file_path, model)
+
+        return render_template(output_page, pred_output=pred_class, pred_prob=pred_prob, user_image=file_path)
+
+if __name__ == "__main__":
+    app.run(debug=True)

main.py

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+import os
+import io
+import logging
+from flask import Flask, request, render_template
+from werkzeug.utils import secure_filename
+from tensorflow.keras.models import model_from_json
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+from PIL import Image
+
+app = Flask(__name__)
+app.secret_key = "secret!@8102"
+
+MODEL_ARCHITECTURE = 'model/clean721.json'
+MODEL_WEIGHTS = 'model/clean721.h5'
+
+PREDICTION_CLASSES = {
+    0: ('Gambar Tidak Cocok', 'klasifikasi-noklasifikasi.html'),
+    1: ('Tanaman Padimu Terkena', 'klasifikasi-bl.html'),
+    2: ('Tanaman Padimu Terkena', 'klasifikasi-blb.html'),
+    3: ('Tanaman Padimu Terkena', 'klasifikasi-bw.html'),
+    4: ('Tanaman', 'klasifikasi-sh.html'),
+}
+def load_model_from_file():
+    try:
+        with open(MODEL_ARCHITECTURE, 'r') as json_file:
+            loaded_model_json = json_file.read()
+        model = model_from_json(loaded_model_json)
+        model.load_weights(MODEL_WEIGHTS)
+        return model
+    except Exception as e:
+        logging.error(f"Error loading model: {str(e)}")
+        return None
+
+model = load_model_from_file()
+
+def model_predict(img_path, model):
+    try:
+        TARGET_IMAGE_SIZE = (224, 224)
+        test_image = load_img(img_path, target_size=TARGET_IMAGE_SIZE)
+        logging.info("@@ Got Image for prediction")
+
+        test_image = img_to_array(test_image) / 255
+        test_image = np.expand_dims(test_image, axis=0)
+
+        result = model.predict(test_image)
+        pred_class = np.argmax(result, axis=1)[0]
+        pred_prob = result[0][pred_class] * 100
+
+        return PREDICTION_CLASSES[pred_class][0], PREDICTION_CLASSES[pred_class][1], pred_prob
+    except Exception as e:
+        logging.error(f"Error predicting: {str(e)}")
+        return 'Error', 'error.html', 0
+
+@app.route("/", methods=['GET'])
+def home():
+    return render_template('index.html')
+
+@app.route("/blog", methods=['GET'])
+def blog():
+    return render_template('blog.html')
+
+@app.route("/contact", methods=['GET'])
+def contact():
+    return render_template('contact.html')
+
+@app.route("/klasifikasirgb", methods=['GET', 'POST'])
+def klasifikasirgb():
+    return render_template('klasifikasirgb.html')
+
+@app.route('/process_image', methods=['POST'])
+def process_image():
+    file = request.files['image']
+
+    original_image = Image.open(io.BytesIO(file.read()))
+    resized_image = original_image.resize((4, 4))
+
+    pixel_values = np.array(resized_image)
+
+    blue_values = pixel_values[:, :, 2]
+    green_values = pixel_values[:, :, 1]
+    red_values = pixel_values[:, :, 0]
+
+    return render_template('result.html', blue_values=blue_values, green_values=green_values, red_values=red_values)
+
+@app.route("/klasifikasi", methods=['GET', 'POST'])
+def klasifikasi():
+    return render_template('klasifikasi.html')
+
+@app.route("/predict", methods=['POST'])
+def predict():
+    if request.method == 'POST':
+        file = request.files['image']
+        filename = secure_filename(file.filename)
+        logging.info("@@ Input posted =", filename)
+
+        file_path = os.path.join('static', 'user_uploaded', filename)
+        file.save(file_path)
+
+        logging.info("@@ Predicting class...")
+        pred_class, output_page, pred_prob = model_predict(file_path, model)
+
+        return render_template(output_page, pred_output=pred_class, pred_prob=pred_prob, user_image=file_path)
+
+if __name__ == "__main__":
+    app.run(debug=True)

model/clean721.txt

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+{0: 'no padi', 1: 'padi bl', 2: 'padi blb', 3: 'padi bw', 4: 'padi sehat'}

requirements.txt

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+click==7.1.2
+Flask==1.1.4
+gunicorn==20.0.4
+itsdangerous==1.1.0
+Jinja2==2.11.3
+MarkupSafe==1.1.1
+Werkzeug==1.0.1
+gevent==23.9.1
+grpcio==1.59.0
+h5py==3.10.0
+Pillow==10.0.1
+scikit-learn==1.3.1
+tensorflow==2.12.0

static/js/upload.js

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+document.addEventListener("DOMContentLoaded", function () {
+    const dragDropArea = document.getElementById("drag-drop-area");
+    const imageUpload = document.getElementById("image-upload");
+    const uploadedImage = document.getElementById("uploaded-image");
+
+    function displayImage(file) {
+        if (file) {
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                uploadedImage.src = e.target.result;
+                uploadedImage.style.display = "block";
+            };
+            reader.readAsDataURL(file);
+        }
+    }
+
+    function resizeImage(file, callback) {
+        const reader = new FileReader();
+        reader.onload = (e) => {
+            const img = new Image();
+            img.onload = () => {
+                const canvas = document.createElement("canvas");
+                const ctx = canvas.getContext("2d");
+
+                canvas.width = 224;
+                canvas.height = 224;
+
+                ctx.drawImage(img, 0, 0, 224, 224);
+
+                canvas.toBlob((blob) => {
+                    callback(blob);
+                }, "image/jpeg", 0.8);
+            };
+            img.src = e.target.result;
+        };
+        reader.readAsDataURL(file);
+    }
+
+    dragDropArea.addEventListener("dragover", (e) => {
+        e.preventDefault();
+        dragDropArea.style.backgroundColor = "#ddd";
+    });
+
+    dragDropArea.addEventListener("dragleave", () => {
+        dragDropArea.style.backgroundColor = "#f1f1f1";
+    });
+
+    dragDropArea.addEventListener("drop", (e) => {
+        e.preventDefault();
+        dragDropArea.style.backgroundColor = "#f1f1f1";
+        const file = e.dataTransfer.files[0];
+        resizeImage(file, (resizedBlob) => {
+            displayImage(resizedBlob);
+        });
+    });
+
+    dragDropArea.addEventListener("click", () => {
+        imageUpload.click();
+    });
+
+    imageUpload.addEventListener("change", () => {
+        const file = imageUpload.files[0];
+        resizeImage(file, (resizedBlob) => {
+            displayImage(resizedBlob);
+        });
+    });
+
+    document.getElementById("image-upload").addEventListener("change", function () {
+        var input = this;
+
+        if (input.files && input.files[0]) {
+            resizeImage(input.files[0], (resizedBlob) => {
+                displayImage(resizedBlob);
+            });
+        }
+    });
+});

static/wp-content/plugins/allow-webp-image/public/js/allow-webp-image-publicf269.js

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+(function($){'use strict';})(jQuery);

static/wp-content/plugins/elementor/assets/js/webpack.runtime.minafc7.js

@@ -0,0 +1,2 @@
+/*! elementor - v3.16.0 - 20-09-2023 */
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static/wp-content/plugins/jeg-elementor-kit/assets/js/elements/testimonials61da.js

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static/wp-content/plugins/jeg-elementor-kit/assets/js/elements/video-button61da.js

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static/wp-content/plugins/jeg-elementor-kit/assets/js/tiny-slider/tiny-sliderf342.css

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static/wp-content/plugins/metform/public/assets/js/htme5ca.js

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static/wp-content/plugins/template-kit-export/public/assets/css/template-kit-export-public.min365c.css

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+.template-kit-preview__name{text-decoration:none;text-align:center;padding:10px}
+@media(min-width:400px){.template-kit-preview__grid{-webkit-column-count:2;-moz-column-count:2;column-count:2}
+}@media(min-width:800px){.template-kit-preview{padding:1em 2em}
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static/wp-content/plugins/template-kit-export/public/assets/js/template-kit-export-public.min365c.js

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static/wp-content/themes/hello-elementor/assets/js/hello-frontend.min8a54.js

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