Update app.py

app.py

@@ -1,26 +1,20 @@
 import streamlit as st
-import pandas as pd
-import pickle
-# Load Model
-model = pickle.load(open('logreg_model.pkl', 'rb'))
-st.title('Iris Variety Prediction')
-# Form
-with st.form(key='form_parameters'):
-   sepal_length = st.slider('Sepal Length', 4.0, 8.0, 4.0)
-   sepal_width = st.slider('Sepal Width', 2.0, 4.5, 2.0)
-   petal_length = st.slider('Petal Length', 1.0, 7.0, 1.0)
-   petal_width = st.slider('Petal Width', 0.1, 2.5, 0.1)
-   st.markdown('---')
-   submitted = st.form_submit_button('Predict')
-# Data Inference
-data_inf = {
-   'sepal.length': sepal_length,
-   'sepal.width': sepal_width,
-   'petal.length': petal_length,
-   'petal.width': petal_width
-}
-data_inf = pd.DataFrame([data_inf])
-if submitted:
-   # Predict using Logistic Regression
-   y_pred_inf = model.predict(data_inf)
-   st.write('## Iris Variety = '+ str(y_pred_inf))
+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)}%")