import gradio as gr
from fastai.vision.all import *
from fastcore.all import *
import skimage

import io
import pandas as pd
df = pd.read_csv('citytempdata.csv')

def ContfromPath(input_Path):
    a = parent_label(input_Path)
    #print(a)
    b = (df[df['Capital']==a]['Continent'])
    return b

learn = load_learner('P2_model_v1.pkl')

labels = learn.dls.vocab

def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}
    
title = "City to Continent Finder"
description = "A fastai model trained on aerial views of cities in order to estimate which continent they are located in."
examples = ['Brisbane_aerial_view.jpg','NewYork_aerial_view.jpg','Yakutsk_aerial_view.jpg','LasVegas_aerial_view.jpg','Darwin_aerial_view.jpg']

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512,512)),outputs=gr.outputs.Textbox(),title=title,description=description,examples=examples).launch()