initial commit

app.py

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+import streamlit as st
+from streamlit_image_select import image_select
+from PIL import Image
+import pandas as pd
+from transformers import AutoModel
+import torch
+from torchvision.datasets import MNIST
+from torchvision.transforms import ToTensor
+import numpy as np
+st.title("Streamlit GUI")
+
+classifier=AutoModel.from_pretrained("hikmatfarhat/MNIST_Classifier",trust_remote_code=True)
+softmax=torch.nn.Softmax(dim=1)
+np.set_printoptions(precision=3,suppress=True)
+imgs=[]
+
+for i in range(15):
+    imgs.append(Image.open(f"img{i}.png"))
+image=image_select("select a digit",imgs,use_container_width=False)
+image=ToTensor()(image)
+output=classifier(image)
+output=torch.squeeze(softmax(output))
+output=output.detach().numpy()
+## streamlit doesn't show the proper label for the column name when using numpy array
+## so we convert it to pandas dataframe
+df=pd.DataFrame(output,columns=["prob"])
+st.dataframe(df,column_config={"prob":st.column_config.Column(
+    "Probability",required=True)}
+    ,hide_index=False
+    )

requirements.txt

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+torch
+torchvision
+PIL
+streamlit_image_select
+numpy