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README.md

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----
-title: Wine
-emoji: 📚
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 4.1.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import requests
+import hopsworks
+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.pkl")
+print("Model downloaded")
+
+
+def generate_number_image(number, img_size=(100, 100), bg_color='white', text_color='black'):
+    """
+    Generate image of a given number.
+    Function generated by ChatGPT
+    """
+    # Create an image with white background
+    image = Image.new('RGB', img_size, bg_color)
+
+    # Initialize the drawing context
+    draw = ImageDraw.Draw(image)
+
+    # Font settings (Default font in this case)
+    font = ImageFont.load_default()
+
+    # Calculate width and height of the text
+    text_width, text_height = draw.textsize(str(number), font)
+
+    # Calculate X, Y position of the text
+    x = (img_size[0] - text_width) / 2
+    y = (img_size[1] - text_height) / 2
+
+    # Draw the number on the image
+    draw.text((x, y), str(number), fill=text_color, font=font)
+
+    return image
+
+def wine(fixed_acidity, citric_acid, type_white, chlorides, volatile_acidity, density, alcohol):
+    print("Calling function")
+    #     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]],
+    df = pd.DataFrame([[fixed_acidity, citric_acid, type_white, chlorides, volatile_acidity, density, alcohol]],
+                      columns=['fixed_acidity', 'citric_acid', 'type_white', 'chlorides', 'volatile_acidity', 'density', '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)
+    wine_url = generate_number_image(int(res))
+    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 fixed_acidity, citric_acid, type, chlorides, volatile_acidity, density, alcohol"
+                "to predict of which quality the wine is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=7.2, label="fixed acidity"),
+        gr.inputs.Number(default=0.34, label="volatile acidity"),
+        gr.inputs.Number(default=0.32, label="citric acid"),
+        gr.inputs.Textbox(default="red", label="type (red, white)"),
+        gr.inputs.Number(default=10.5, label="alcohol"),
+        gr.inputs.Number(default=0.99, label="density"),
+        gr.inputs.Number(default=0.06, label="chlorides"),
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1