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from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
import cv2
import easyocr
import numpy as np
import tensorflow as tf
app = Flask(__name__)
CORS(app)
# Load your pre-trained model and set up the test set for phone recognition
test_set = tf.keras.utils.image_dataset_from_directory(
"/app/testing_data",
labels="inferred",
label_mode="categorical",
class_names=None,
color_mode="rgb",
batch_size=32,
image_size=(64, 64),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation="bilinear",
follow_links=False,
crop_to_aspect_ratio=False
)
# Load the CNN model for phone recognition
cnn = tf.keras.models.load_model("/app/trained_model (1).h5")
# Instantiate text reader for OCR
picture_read = easyocr.Reader(["en", "ar"], gpu=False)
def get_class_names():
# Load the class names from the test_set
return test_set.class_names
class_names = get_class_names()
# Route for rendering the index.html template
@app.route("/")
def index():
# Pass the class names to the HTML template
return render_template("index.html", class_names=class_names)
# Route for phone recognition and processing image
@app.route("/process_image", methods=["POST"])
def process_image():
# Get the uploaded file from the request
uploaded_file = request.files["file"]
# Save the file to a temporary location
temp_file_path = "/app/temp_image.png"
uploaded_file.save(temp_file_path)
# Read and preprocess the image for phone model recognition
img = cv2.imread(temp_file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (64, 64))
# Make predictions for phone model
input_arr = np.array([img])
predictions = cnn.predict(input_arr)
# Get the index with the highest probability
result_index = np.argmax(predictions)
# Display the predicted class for phone model
result_phone_model = class_names[result_index]
# Process image using OCR
picture_results = picture_read.readtext(temp_file_path)
results_ocr = []
conf_threshold = 0.2
for y in picture_results:
if y[2] > conf_threshold:
text = y[1]
results_ocr.append({"text": text})
# Return JSON response with the results
return jsonify({"result_phone_model": result_phone_model, "results_ocr": results_ocr})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=8000, debug=True)
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