hotdog

src/streamlit_app.py

@@ -1,40 +1,20 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import pipeline
+from PIL import Image
 
-"""
-# Welcome to Streamlit!
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+st.title("Hot Dog? Or Not?")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+file_name = st.file_uploader("Upload a hot dog candidate image")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+if file_name is not None:
+    col1, col2 = st.columns(2)
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+    image = Image.open(file_name)
+    col1.image(image, use_column_width=True)
+    predictions = pipeline(image)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")