Create app.py

app.py

@@ -0,0 +1,63 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+df=pd.read_csv("/content/WineQT.csv")
+
+x=df.drop(["Id","quality"],axis=1)
+y = df["quality"]
+
+df["quality"].unique()
+
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+
+model=RandomForestClassifier()
+
+model.fit(x_test,y_test)
+
+model.fit(x_train,y_train)
+
+x_predict=model.predict(x_train)
+x_accuracy=accuracy_score(x_predict,y_train)
+
+y_predict=model.predict(x_test)
+y_accuracy=accuracy_score(y_predict,y_test)
+
+import gradio as gr
+
+# Assuming you've already trained the RandomForest model
+model = RandomForestClassifier()
+
+# Fit the model with your training data (re-run if needed)
+model.fit(x_train, y_train)
+
+# Function to make predictions
+def predict_wine_quality(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol):
+    input_data = np.array([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]])
+    prediction = model.predict(input_data)
+
+    return f"Predicted Wine Quality: {prediction[0]}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_wine_quality,  # Function that makes predictions
+    inputs=[
+        gr.Slider(minimum=0.0, maximum=15.0, step=0.1, label="Fixed Acidity"),
+        gr.Slider(minimum=0.0, maximum=2.0, step=0.1, label="Volatile Acidity"),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Citric Acid"),
+        gr.Slider(minimum=0.0, maximum=20.0, step=0.1, label="Residual Sugar"),
+        gr.Slider(minimum=0.0, maximum=0.2, step=0.01, label="Chlorides"),
+        gr.Slider(minimum=0.0, maximum=100.0, step=1, label="Free Sulfur Dioxide"),
+        gr.Slider(minimum=0.0, maximum=300.0, step=1, label="Total Sulfur Dioxide"),
+        gr.Slider(minimum=0.990, maximum=1.100, step=0.001, label="Density"),
+        gr.Slider(minimum=2.5, maximum=4.0, step=0.1, label="pH"),
+        gr.Slider(minimum=0.3, maximum=2.0, step=0.1, label="Sulphates"),
+        gr.Slider(minimum=8.0, maximum=15.0, step=0.1, label="Alcohol")
+    ],  # Input fields for the features of the wine
+    outputs="text"  # Output the predicted wine quality as text
+)
+
+# Launch the Gradio UI
+iface.launch()