import numpy as np
import gradio as gr
# teo
from transformers import Dinov2Config, Dinov2Model, Dinov2ForImageClassification, AutoImageProcessor
import torch.nn as nn
import os

# Load PyTorch model
def create_head(num_features , number_classes ,dropout_prob=0.5 ,activation_func =nn.ReLU):
    features_lst = [num_features , num_features//2 , num_features//4]
    layers = []
    for in_f ,out_f in zip(features_lst[:-1] , features_lst[1:]):
        layers.append(nn.Linear(in_f , out_f))
        layers.append(activation_func())
        layers.append(nn.BatchNorm1d(out_f))
        if dropout_prob !=0 : layers.append(nn.Dropout(dropout_prob))
    layers.append(nn.Linear(features_lst[-1] , number_classes))
    return nn.Sequential(*layers)

class NewheadDinov2ForImageClassification(Dinov2ForImageClassification):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.dinov2 = Dinov2Model(config)

        # Classifier head
        self.classifier = create_head(config.hidden_size * 2, config.num_labels)
# IMPORT CLASSIFICATION MODEL
checkpoint_name = "lombardata/dino-base-2023_11_27-with_custom_head"
# import labels
classes_names = ["Acropore_branched", "Acropore_digitised", "Acropore_tabular", "Algae_assembly", 
               "Algae_limestone", "Algae_sodding", "Dead_coral", "Fish", "Human_object",
               "Living_coral", "Millepore", "No_acropore_encrusting", "No_acropore_massive",
              "No_acropore_sub_massive",  "Rock", "Sand",
               "Scrap", "Sea_cucumber", "Syringodium_isoetifolium",
               "Thalassodendron_ciliatum",  "Useless"]

classes_nb = list(np.arange(len(classes_names)))
id2label = {int(classes_nb[i]): classes_names[i] for i in range(len(classes_nb))}
label2id = {v: k for k, v in id2label.items()}

model = NewheadDinov2ForImageClassification.from_pretrained(checkpoint_name)


def predict(path):
    image = path.reshape((224, 224, 3))
    image = tf.keras.utils.img_to_array(image)
    image = np.expand_dims(image, axis=0)
    pred = model.predict(image, verbose=0)
    pred = pred[0]
    confidences = {classes_names[i]: round(float(pred[i]), 2) for i in range(50)}
    return confidences


gr.Interface(
    fn=predict,
    inputs=gr.Image(shape=(224, 224)),
    outputs=gr.Label(num_top_classes=5),
    examples=[
        "Dalbergia oliveri.JPG",
        "Eucalyptus.JPG",
        "Khaya senegalensis.JPG",
        "Syzygium nervosum.JPG",
           ]
).launch()