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from flask import Flask, render_template, request, redirect, url_for
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import os
from PIL import Image
# Initialize the Flask app
app = Flask(__name__)
# Load trained model
MODEL_PATH = 'my_model.h5'
model = load_model(MODEL_PATH)
# List of class names (from LabelEncoder's `classes_`)
class_names = ['Acacia', 'Acer', 'Alnus', 'Anadenanthera', 'Betula', 'Celtis', 'Chamaerops',
'Corylus', 'Eucalyptus', 'Fagus', 'Fraxinus', 'Juglans', 'Laurus', 'Morus',
'Pinus', 'Platanus', 'Populus', 'Quercus', 'Salix', 'Tamarix', 'Tilia',
'Ulmus', 'Zea']
# Home route
@app.route('/')
def index():
return render_template('index.html')
# Predict route
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file']
if file.filename == '':
return redirect(request.url)
if file:
# Save the uploaded file
filepath = os.path.join('static', file.filename)
file.save(filepath)
# Load image
img = Image.open(filepath).convert("RGB")
img = img.resize((128, 128))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Predict
predictions = model.predict(img_array)
class_index = np.argmax(predictions)
predicted_label = class_names[class_index]
confidence = round(100 * np.max(predictions), 2)
return render_template('result.html', label=predicted_label, confidence=confidence, image_path=filepath)
return redirect(url_for('index'))
# Run the app
if __name__ == '__main__':
app.run(debug=True)
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