v3

README.md

@@ -8,9 +8,10 @@ sdk_version: 1.28.2
 app_file: app.py
 pinned: false
 license: apache-2.0
----
-
 sdk: streamlit
 sdk_version: 1.25.0 # The latest supported version
+---
+
+
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,20 +1,21 @@
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
-
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-st.title("Hot Dog? Or Not?")
-
-file_name = st.file_uploader("Upload a hot dog candidate 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 = pipeline(image)
-
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+print("Hello")
+# import streamlit as st
+# from transformers import pipeline
+# from PIL import Image
+#
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+#
+# st.title("Hot Dog? Or Not?")
+#
+# file_name = st.file_uploader("Upload a hot dog candidate 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 = pipeline(image)
+#
+#     col2.header("Probabilities")
+#     for p in predictions:
+#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")