Update app.py

app.py

@@ -2,9 +2,14 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 
+from datasets import load_dataset
+dataset = load_dataset("rwcuffney/pick_a_card_test")
 
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+#tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+#tokenized_data = tokenizer(dataset["sentence"], return_tensors="np", padding=True)
 
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+'''
 extractor = AutoFeatureExtractor.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099221")
 model = AutoModelForImageClassification.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099221")
 st.write(model.__class__.__name__)
@@ -22,23 +27,27 @@ model = AutoModelForImageClassification.from_pretrained("rwcuffney/autotrain-pic
 st.write(model.__class__.__name__)
 st.code(type(model))
 
-
+'''
 
 extractor = AutoFeatureExtractor.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099224")
 model = AutoModelForImageClassification.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099224")
 st.write(model.__class__.__name__)
 st.code(type(model))
 
+from transformers import AutoImageProcessor
+import torch
 
+image_processor = AutoImageProcessor.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099224"))
+inputs = image_processor(image, return_tensors="pt")
+
+'''
 extractor = AutoFeatureExtractor.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099225")
 model = AutoModelForImageClassification.from_pretrained("rwcuffney/autotrain-pick_a_card-3726099225")
 st.write(model.__class__.__name__)
 st.code(type(model))
+'''
 
 
-from datasets import load_dataset
-dataset = load_dataset("rwcuffney/pick_a_card_test")
-
 '''
 x = st.slider('Select a value')
 st.write(x, 'squared is', x * x)