import streamlit as st
from fastai.vision.all import *
import matplotlib.pyplot as plt
import pandas as pd

def is_cat(x) : return x[0].isupper()

learn = load_learner('model.pkl')
categories = ('Dog', 'Cat')

def classify_image(img):
    pred,idx,probs = learn.predict(img)
    return dict(zip(categories,map(float,probs)))

def save_uploaded_file(uploadedfile):
  temp_folder = "tmp"
  if not os.path.exists(temp_folder):
     os.makedirs(temp_folder)
  path_name = os.path.join(temp_folder,uploadedfile.name)
  with open(path_name,"wb") as f:
    f.write(uploadedfile.getbuffer())
  return path_name

st.write("""
# Dog vs Cat Classifier
This is a application that classifies images of dogs vs cats
""")

upload_image = st.file_uploader("Choose a file")

if upload_image is not None:
    image = PILImage.create(upload_image)
    image.thumbnail((192,192))
    st.image(image)
    path_name = save_uploaded_file(upload_image)
    st.write("Prediction Propabilities:")
    st.write(classify_image(path_name))
    preds = pd.DataFrame(classify_image(path_name), index=[0])
    st.bar_chart(preds)