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README.md

@@ -1,10 +1,10 @@
 ---
-title: WINE
-emoji: 📊
-colorFrom: green
-colorTo: pink
+title: Wine
+emoji: 🐠
+colorFrom: yellow
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.5.0
+sdk_version: 4.4.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 19 17:34:34 2023
+
+@author: Antares
+"""
+
+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+import numpy as np
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+mr = project.get_model_registry()
+model = mr.get_model("wine_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/wine_model.pkl")
+print("Model downloaded")
+
+def wine(ttype,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,ph,sulphates,alcohol):
+    print("Calling function")
+    if(ttype=="White/0"):
+        ttype = int(0)
+    else:
+        ttype = int(1)
+    #df = [ttype],[fixed_acidity],[volatile_acidity],[citric_acid],[residual_sugar],[chlorides],[free_sulfur_dioxide],[total_sulfur_dioxide],[density],[ph],[sulphates],[alcohol]])
+    df = pd.DataFrame([[ttype,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,ph,sulphates,alcohol]], 
+                      columns=["type","fixed_acidity","volatile_acidity","citric_acid","residual_sugar","chlorides","free_sulfur_dioxide","total_sulfur_dioxide","density","ph","sulphates","alcohol"])
+    print("Predicting")
+    print(df)
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(df) 
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
+    # the first element.
+#     print("Res: {0}").format(res)
+    print('res:',res)
+    #flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
+    #img = Image.open(requests.get(flower_url, stream=True).raw)            
+    return res
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Quality Predictive Analytics",
+    description="Experiment with 12 wine attributes to predict what quality it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Radio(["White/0", "Red/1"], label="type"),
+        gr.inputs.Number(default=1.0, label="fixed_acidity"),
+        gr.inputs.Number(default=1.0, label="volatile_acidity"),
+        gr.inputs.Number(default=1.0, label="citric_acid"),
+        gr.inputs.Number(default=1.0, label="residual_sugar"),
+        gr.inputs.Number(default=1.0, label="chlorides"),
+        gr.inputs.Number(default=1.0, label="free_sulfur_dioxide"),
+        gr.inputs.Number(default=1.0, label="total_sulfur_dioxide"),
+        gr.inputs.Number(default=1.0, label="density"),
+        gr.inputs.Number(default=1.0, label="ph"),
+        gr.inputs.Number(default=1.0, label="sulphates"),
+        gr.inputs.Number(default=1.0, label="alcohol"),
+        ],
+    
+    outputs=gr.Number(label="quality"))
+
+demo.launch(debug=True,share = True)
+

requirements.txt

@@ -0,0 +1,3 @@
+hopsworks
+joblib
+scikit-learn==1.1.1