import numpy as np
import gradio as gr
import tensorflow as tf #version 2.13.0
import keras #version 
import numpy as np
import cv2
import tensorflow as tf
import h5py
def analyse(img,plant_type):
    import json

    # Load label_disease.json
    with open('data/label_disease.json', 'r') as f:
        label_disease = json.load(f)

    # Load plant_label_disease.json
    with open('data/plant_label_disease.json', 'r') as f:
        plant_label_disease = json.load(f)
    print(label_disease,label_disease["34"])
    HEIGHT = 256
    WIDTH = 256
    modelArchitecturePath ='model/model_architecture.h5'
    modelWeightsPath = 'model/model_weights.h5'
    dnn_model = keras.models.load_model(modelArchitecturePath,compile=False)
    dnn_model.load_weights(modelWeightsPath)
    
    process_img = cv2.resize(img, (HEIGHT, WIDTH),interpolation = cv2.INTER_LINEAR)
    process_img = process_img/(255)
    process_img = np.expand_dims(process_img, axis=0)
    
    
    y_pred = dnn_model.predict(process_img)
    y_pred = y_pred[0]
    print("y pred",y_pred)
    
    id = np.argmax(y_pred)
    p_id = plant_label_disease[plant_type.lower()][0]
    
    for disease in plant_label_disease[plant_type.lower()]:
            if y_pred[disease] > y_pred[p_id]:
                p_id = disease
    
    overall_predicted_id = id
    overall_predicted_name = label_disease[str(overall_predicted_id)]
    overall_predicted_acc = y_pred[overall_predicted_id]
    
    plant_predicted_id =pid
    plant_predicted_name = label_disease[str(plant_predicted_id)]
    plant_predicted_acc = y_pred[plant_predicted_id]
    
    return plant_predicted_id, plant_predicted_name, plant_predicted_acc, overall_predicted_id, overall_predicted_name, overall_predicted_acc

demo = gr.Interface(analyse, 
                    [gr.Image(),gr.Radio(["Apple","Blueberry","Cherry","Corn","Grape","Orange","Peach","Pepper","Potato","Raspberry","Soybean","Squash","Strawberry","Tomato"])],
                    ["number","text","number","number","text","number"],
                   )
demo.launch(share=True,show_error=True)