Updated app.py

app.py

@@ -0,0 +1,27 @@
+import torch
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+import streamlit as st
+from PIL import Image
+
+model_id = f'amanneo/vit-base-patch16-224-finetuned-flower'
+labels = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
+
+def classify_image(image):
+  model = AutoModelForImageClassification.from_pretrained(model_id)
+  feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+  inp = feature_extractor(image, return_tensors='pt')
+  outp = model(**inp)
+  pred = torch.nn.functional.softmax(outp.logits, dim=-1)
+  preds = pred[0].cpu().detach().numpy()
+  confidence = {label: float(preds[i]) for i, label in enumerate(labels)}
+  return confidence
+
+file_name = st.file_uploader("Upload flower image")
+if file_name is not None:
+    col1,col2 = st.columns(2)
+    image = Image.open(file_name)
+    col1.image(image,use_column_width=True)
+    predictions = classify_image(image)
+    col2.header("Probabilities")
+    for l,p in predictions.items():
+      col2.subheader("{} : {}".format(l,p))