import gradio as gr
from fastai.vision.all import load_learner
from fastai import *
import torch
import os
from PIL import Image
import pandas as pd



model_path  = 'multi_target_resnet34.pkl'
helper_csv  = 'info.csv'
df =  pd.read_csv(helper_csv)

model = load_learner(model_path)

def result(path):    
    pred,_,probability = model.predict(path)
    arr = ['Species','Scientific_Name','Name','Conservation_Status','Color','habitat','Found_In','Diet']
    vals = ['','','','','','','','']
    # pred = ['Acrantophis madagascariensis', 'Carnivore', 'Critically Endangered', 'Forest', 'Gray', 'Madagascar', 'Madagascar ground boa ', 'Snake']
    for x in pred:
        val = df[df['values'] == x]['columns'].values[0]
        ind = arr.index(val)
        vals[ind] = x
      
    return f'{arr[0]}:\t{vals[0]}\n{arr[1]}:\t{vals[1]}\n{arr[2]}:\t{vals[2]}\n{arr[3]}:\t{vals[3]}\n{arr[4]}:\t{vals[4]}\n{arr[5]}:\t{vals[5]}\n{arr[6]}:\t{vals[6]}\n{arr[7]}:\t{vals[7]}\n'

    
path = 'test images/'

image_path = []

for i in os.listdir(path):
  image_path.append(path+i) 


image = gr.inputs.Image(shape =(300,300))
label = gr.outputs.Label()

iface = gr.Interface(fn=result, inputs=image, outputs='text', examples = image_path)
iface.launch(inline = False)