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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)
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