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from flask import Flask, request, render_template, jsonify
from flask_cors import CORS
import numpy as np
from PIL import Image
import io
from tensorflow.keras.models import load_model
# Load the model
model = load_model('ecosort.h5')
# Define a dictionary to map class numbers to class names
class_mapping = {
0: 'battery',
1: 'biological',
2: 'brown-glass',
3: 'cardboard',
4: 'clothes',
5: 'green-glass',
6: 'metal',
7: 'paper',
8: 'plastic',
9: 'shoes',
10: 'trash',
11: 'white-glass'
}
app = Flask(__name__)
CORS(app)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return "No file uploaded"
file = request.files['file']
if file.filename == '':
return "No selected file"
img = Image.open(io.BytesIO(file.read()))
if img is None:
return "Invalid image file"
# Preprocess the image
img = img.resize((224, 224))
img_array = np.asarray(img) / 255.0
# Make predictions using your model
prediction = model.predict(np.expand_dims(img_array, axis=0))
predicted_class = np.argmax(prediction)
# Get the class name from the dictionary
class_name = class_mapping.get(predicted_class, 'Unknown Class')
# Return the prediction result as JSON
return jsonify({'prediction': class_name})
if __name__ == '__main__':
app.run(debug=True)
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