File size: 2,075 Bytes
ce9ec08 | 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 | from flask import Flask, render_template, request
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import os
import uuid
import tensorflow as tf
import random
# Fix randomness for reproducibility
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.random.set_seed(42)
np.random.seed(42)
random.seed(42)
app = Flask(__name__)
# Load the model (only one model now)
model = load_model("model/cat_dog_neither_classifier_new.h5", compile=False)
# <-- your model file
class_names = ['cat', 'dog', 'neither']
UPLOAD_FOLDER = 'static/uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
def preprocess_image(img_path):
img = image.load_img(img_path, target_size=(224, 224)) # Ensure matches model input
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
return img_array
@app.route('/', methods=['GET'])
def index():
return render_template('upload.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return "No file part", 400
file = request.files['file']
if file.filename == '':
return "No selected file", 400
filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
img_path = os.path.join(UPLOAD_FOLDER, filename)
file.save(img_path)
# Preprocess image
processed = preprocess_image(img_path)
# Predict
prediction = model.predict(processed)[0]
prediction /= np.sum(prediction) # normalize
class_index = int(np.argmax(prediction))
confidence = round(float(np.max(prediction)) * 100, 2)
final_class = class_names[class_index]
return render_template(
'result.html',
prediction=final_class,
confidence=confidence,
img_path='/' + img_path
)
if __name__ == '__main__':
import os
# Hugging Face uses 7860 by default.
# This line checks for a PORT variable but falls back to 7860.
port = int(os.environ.get("PORT", 7860))
app.run(host='0.0.0.0', port=port, debug=False)
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