import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
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.utils import ops as utils_op
import gradio as gr
import os
import cv2

def greet(name):
    return "Hello " + name + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()








PATH_TO_LABELS = 'data/label_map.pbtxt'   
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

def pil_image_as_numpy_array(pilimg):
    img_array = tf.keras.utils.img_to_array(pilimg)
    return img_array

def load_model():
    model_dir = 'saved_model'    
    detection_model = tf.saved_model.load(str(model_dir))
    return detection_model

def predict(image_np):
    image_np = pil_image_as_numpy_array(image_np)
    image_np = np.expand_dims(image_np, axis=0)
    results = detection_model(image_np)
    result = {key: value.numpy() for key, value in results.items()}
    label_id_offset = 0
    image_np_with_detections = image_np.copy()
    viz_utils.visualize_boxes_and_labels_on_image_array(
        image_np_with_detections[0],
        result['detection_boxes'][0],
        (result['detection_classes'][0] + label_id_offset).astype(int),
        result['detection_scores'][0],
        category_index,
        use_normalized_coordinates=True,
        max_boxes_to_draw=200,
        min_score_thresh=.6,
        agnostic_mode=False,
        line_thickness=2
    )
    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
    return result_pil_img



detection_model = load_model()

# Specify paths to example images
sample_images = [["br_61.jpg"], ["br_61.jpg"],
                ]

tab1 = gr.Interface(
    fn=predict,
    inputs=gr.Image(label='Upload an expressway image', type="pil"),
    outputs=gr.Image(type="pil"),
    title='Image Processing',
    examples=sample_images
)


# Create a Multi Interface with Tabs
iface = gr.TabbedInterface([tab1], title='Cauliflower and Beetroot Detection via ssd_resnet50_v1_fpn_640x640_coco17_tpu-8')

# Launch the interface
iface.launch(share=True)