app.py

#import streamlit as st
from io import StringIO
from PIL import Image
import pandas as pd
import numpy as np
import glob
import os

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
#import torchvision.models as models
#from torchinfo import summary

#from sklearn.model_selection import StratifiedKFold
#from sklearn.metrics import accuracy_score

from tqdm import tqdm
import cv2
#import albumentations as A # Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. (https://albumentations.ai/)
import matplotlib.pyplot as plt
import seaborn as sns
#from albumentations.pytorch.transforms import ToTensorV2

model = models.resnet50(pretrained=False)
model.fc = nn.Linear(2048, num_classes)
model.load_state_dict(torch.load('resnet_best.pth'), strict=True)

st.title("some big ML function")

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

if uploaded_file is not None:
  if ".jpg" in uploaded_file.name or ".png" in uploaded_file.name:
    image = Image.open(uploaded_file)
    st.image(image)  #show it
    
  elif ".csv" in uploaded_file.name:
    dataframe = pd.read_csv(uploaded_file)
    st.write(dataframe)