app

README.md

@@ -1,8 +1,8 @@
 ---
-title: WinePrediction
-emoji: ⚡
+title: Wine
+emoji: 🚀
 colorFrom: pink
-colorTo: gray
+colorTo: red
 sdk: gradio
 sdk_version: 4.2.0
 app_file: app.py

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+#from gradio.components import inputs
+from PIL import Image
+import requests
+import hopsworks
+import io
+import joblib
+import pandas as pd
+
+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.json")
+print("Model downloaded")
+
+def wine(volatile_acidity,
+           residual_sugar,
+           chlorides,
+           free_sulfur_dioxide,
+           alcohol):
+    print("Calling function")
+    df = pd.DataFrame([[volatile_acidity, residual_sugar, chlorides, free_sulfur_dioxide, alcohol]], 
+                      columns=["volatile_acidity", "residual_sugar", "chlorides", "free_sulfur_dioxide", "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[0])
+    wine_url = "https://github.com/jordicotxet/id2223/blob/63fe7d525afa1cfb626c9fa7513e2cc886e22d41/Wine/wine_dataset/" + str(res[0]) + ".jpg?raw=true"
+    print(wine_url)
+    img = Image.open(requests.get(wine_url, stream=True).raw)  
+    return img
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Quality Predictive Analytics",
+    description="Experiment with few main wine characteristics to predict which quality it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.Slider(minimum=0, maximum=1.5, step=0.01, value=0.2, label="volatile acidity"),
+        gr.Slider(minimum=0, maximum=100, step=0.1, value=5.9, label="residual sugar"),
+        gr.Slider(minimum=0, maximum=0.5, step=0.001, value=0.046, label="chlorides"),
+        gr.Slider(minimum=0, maximum=400, step=1, value=35, label="free_sulfur_dioxide"),
+        gr.Slider(minimum=2, maximum=15, step=0.1, value=10.6, label="alcohol (in %)"),
+        ],
+    examples=[[0.5, 0.8, 0.034, 46, 9.2],[0.42, 4.1, 0.03, 31, 12.8], [0.7, 67.1, 0.219, 275, 10.7]],
+    outputs=gr.Image(type="pil", height=400, width=400))
+
+demo.launch(debug=True)
+ 

requirements.txt

@@ -0,0 +1,9 @@
+#If you have windows, install twofish
+twofish
+hopsworks
+joblib
+scikit-learn==1.1.1
+seaborn
+dataframe-image
+modal
+gradio==3.14