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from flask import Flask, request, jsonify,render_template
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
from flask_cors import CORS
app = Flask(__name__)
CORS(app,origin=['*','http://localhost:3000'],allow_headers=['Content-Type','Authorization','Access-Control-Allow-Credentials','Access-Control-Allow-Origin','Access-Control-Allow-Headers','x-xsrf-token','Access-Control-Allow-Methods','Access-Control-Allow-Headers','Access-Control-Allow-Headers','Access-Control-Allow-Origin','Access-Control-Allow-Methods','Authorization','X-Requested-With','Access-Control-Request-Headers','Access-Control-Request-Method'])
port = 4000
# Load the model
model = load_model("keras_model.h5", compile=False)
# Load the labels
class_names = [line.strip() for line in open("labels.txt", "r").readlines()]
# Confidence threshold for predictions
confidence_threshold = 0.7
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file provided'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No selected file'}), 400
if file:
# Process the image file
image = Image.open(file.stream).convert("RGB")
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
# Convert image to numpy array and normalize
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
# Prepare the image for prediction
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
data[0] = normalized_image_array
# Predict
prediction = model.predict(data)
max_confidence = np.max(prediction)
if max_confidence >= confidence_threshold:
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = float(prediction[0][index])
else:
# Classify as "other" if confidence is below threshold
class_name = "Other"
confidence_score = float(max_confidence)
# Return the result
response = jsonify({'class': class_name, 'confidence_score': confidence_score})
return response
if __name__ == '__main__':
app.run(debug=True, port=port)
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