from flask import Flask, render_template, request, jsonify
import os
import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder
from object_detection.utils import config_util

app = Flask(__name__)

# Constants
CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
CHECKPOINT_PATH = os.path.join('Tensorflow', 'workspace', 'models', CUSTOM_MODEL_NAME)
LABELMAP_PATH = os.path.join('Tensorflow', 'workspace', 'annotations', 'label_map.pbtxt')
MIN_SCORE_THRESH = 0.4
MAX_BOXES_TO_DRAW = 10

# Load model and label map
def load_model():
    configs = config_util.get_configs_from_pipeline_file(os.path.join(CHECKPOINT_PATH, 'pipeline.config'))
    detection_model = model_builder.build(model_config=configs['model'], is_training=False)
    ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
    ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-7')).expect_partial()
    category_index = label_map_util.create_category_index_from_labelmap(LABELMAP_PATH)
    return detection_model, category_index

# Define detection function
@tf.function
def detect_fn(image: tf.Tensor) -> tf.Tensor:
    image, shapes = detection_model.preprocess(image)
    prediction_dict = detection_model.predict(image, shapes)
    detections = detection_model.postprocess(prediction_dict, shapes)
    return detections

# Define route for object detection
@app.route('/detect', methods=['POST'])
def detect():
    try:
        file = request.files['image']
        img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
        image_np = np.array(img)
        input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
        detections = detect_fn(input_tensor)
        #...
        return img_str
    except Exception as e:
        return jsonify({'error': str(e)}), 500

# Define index route
@app.route('/', methods=['GET'])
def index():
    return render_template('index.html')

if __name__ == "__main__":
    detection_model, category_index = load_model()
    app.run(debug=True)