Update app.py

app.py

@@ -1,8 +1,14 @@
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
+from transformers import (
+    AutoImageProcessor,
+    AutoModelForImageClassification
+)
+
+pre_process = AutoImageProcessor.from_pretrained('javierrf91/Streamlite_ViT_2000')
+
 
-pipeline = pipeline(task="image-classification", model="javierrf91/Streamlite_ViT_2000")
 st.title("What is it?")
 
 file_name = st.file_uploader("Upload a hot dog candidate image")
@@ -11,9 +17,8 @@ if file_name is not None:
     col1, col2 = st.columns(2)
 
     image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
-
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    inputs = pre_process(images=image, return_tensors="pt")
+    input_pixels = inputs.pixel_values
+    model = AutoModelForImageClassification.from_pretrained('G:\Mi unidad\output')
+    outputs = model(input_pixels)
+    print("Predicted class:", model.config.id2label[outputs.logits.argmax(-1)  .item()])