Update app.py

app.py

@@ -1,20 +1,26 @@
 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)}%")
+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))