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app.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+
+model = joblib.load('model.joblib')
+
+def main():
+    st.title("Wine Quality Analysis")
+
+    with st.form("questionaire"):
+
+        fixed_acidity = st.slider("Fixed Acidity", min_value=0.0, max_value=20.0, step=0.01)
+        volatile_acidity = st.slider("Volatile Acidity", min_value=0.0, max_value=20.0, step=0.01)
+        citric_acid = st.slider("Citric Acid", min_value=0.0, max_value=1.0, step=0.01)
+        residual_sugar = st.slider("Residual Sugar", min_value=0.0, max_value=20.0, step=0.01)
+        chlorides = st.slider("Chlorides", min_value=0.0, max_value=1.0, step=0.01)
+        free_sulfur_dioxide = st.slider("Free Sulfur Dioxide", min_value=0.0, max_value=100.0, step=0.01)
+        total_sulfur_dioxide = st.slider("Total Sulfur Dioxide", min_value=0.0, max_value=500.0, step=0.01)
+        density = st.slider("Density", min_value=0.0, max_value=10.0, step=0.01)
+        ph = st.slider("pH", min_value=1.0, max_value=14.0, step=0.01)
+        sulphates = st.slider("Sulphates", min_value=0.0, max_value=20.0, step=0.01)
+        alcohol = st.slider("Alcohol", min_value=0.0, max_value=25.0, step=0.01)
+
+        clicked = st.form_submit_button("Predict quality")
+        
+        if clicked:
+            result=model.predict(pd.DataFrame({"fixed_acidity": [fixed_acidity],
+                                               "volatile_acidity": [volatile_acidity],
+                                               "citric_acid": [citric_acid],
+                                               "residual_sugar": [residual_sugar],
+                                               "chlorides": [chlorides],
+                                               "free_sulfur_dioxide": [free_sulfur_dioxide],
+                                               "total_sulfur_dioxide": [total_sulfur_dioxide],
+                                               "density": [density],
+                                               "ph": [ph],
+                                               "sulphates": [sulphates],
+                                               "alcohol": [alcohol]}))
+            predicted_quality_rank = result[0]
+            st.success('The predicted wine quality ranking is {}'.format(predicted_quality_rank))
+
+if __name__=='__main__':
+    main()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9fe062d0f6779fb8654b1914c4ff51702e89253ed6bb9a16f21e69b9af1692d
+size 971676

requirements.txt

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+joblib
+pandas
+scikit-learn==1.2.2
+xgboost==1.7.6
+altair<5