innitial commit

app.py

@@ -0,0 +1,51 @@
+import pandas as pd
+from joblib import dump, load
+import gradio as gr
+
+rfc = load('wine_pred.joblib') 
+
+def predict_quality(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, ph, sulphates, alcohol):
+    df = pd.DataFrame.from_dict(
+        {
+            '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
+        }, orient='index').T
+        
+    return rfc.predict(df)[0]
+
+
+
+iface = gr.Interface(
+    predict_quality,
+    [
+        gr.inputs.Slider(0, 20),
+        gr.inputs.Slider(0, 2),
+        gr.inputs.Slider(0, 1),
+        gr.inputs.Slider(0, 20),
+        gr.inputs.Slider(0, 1),
+        gr.inputs.Slider(0, 100),
+        gr.inputs.Slider(0, 300),
+        gr.inputs.Slider(0, 2),
+        gr.inputs.Slider(0, 5),
+        gr.inputs.Slider(0, 2),
+        gr.inputs.Slider(0, 15)
+
+    ],
+    "label",
+    examples=[
+        [7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
+    ],
+    interpretation="default",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+pandas
+scikit-learn
+joblib

wine_pred.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f9133d0406c5d9f81658681d0d03af8ffbbc0058040baa2b84e25ce683dd5a6d
+size 5147028