Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import  accuracy_score
+
+# Load the breast cancer dataset
+data = load_breast_cancer()
+X = pd.DataFrame(data.data, columns=data.feature_names)
+y = pd.Series(data.target)
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Create and train a logistic regression model
+model = LogisticRegression()
+model.fit(X_train, y_train)
+
+# Define a function for prediction
+def predict_cancer(*input_data):
+    # Preprocess the input data
+    input_data = [float(val) for val in input_data]
+    input_data = scaler.transform([input_data])  # Standardize input data
+    prediction = model.predict(input_data)
+    if prediction[0] == 0:
+        result = "Malignant"
+    else:
+        result = "Benign"
+    
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+    #return (result, accuracy_score)
+    return f"Prediction: {result}, Accuracy: {accuracy:.2f}"
+
+# Create a Gradio interface
+input_component = gr.Interface(
+    fn=predict_cancer,
+    inputs=[gr.inputs.Number(label=feature) for feature in data.feature_names],
+   outputs="text",
+    examples = [
+    [13.21, 25.25, 84.1, 537.9, 0.08791, 0.05205, 0.02772, 0.02068, 0.1619, 0.05584, 0.2084, 1.35, 1.314, 17.58, 0.003047, 0.00781, 0.00511, 0.003749, 0.006471, 0.001095, 14.87, 38.97, 99.59, 698.8, 0.1162, 0.1711, 0.2282, 0.1282, 0.2871, 0.06917],
+    [9.029, 17.33, 58.79, 250.5, 0.1066, 0.1413, 0.313, 0.04375, 0.2111, 0.08046, 0.3274, 1.194, 2.157, 20.96, 0.01284, 0.03088, 0.05025, 0.01055, 0.02461, 0.004711, 10.31, 22.65, 65.5, 324.7, 0.1482, 0.4365, 1.252, 0.175, 0.4228, 0.1175],
+    [20.29, 14.34, 135.1, 1297, 0.1003, 0.1328, 0.198, 0.1043, 0.1809, 0.05883, 0.7572, 0.7813, 5.438, 94.44, 0.01149, 0.02461, 0.05688, 0.01885, 0.01756, 0.005115, 22.65, 18.1, 146.7, 1600, 0.1412, 0.2772, 0.8216, 0.1571, 0.3108, 0.1259],
+    [12.34, 12.27, 78.94, 468.5, 0.09003, 0.06307, 0.02958, 0.02647, 0.1689, 0.05808, 0.1166, 0.4957, 1.344, 8.966, 0.006808, 0.0187, 0.0198, 0.01185, 0.02037, 0.0027, 13.61, 19.27, 87.22, 564.9, 0.1292, 0.2074, 0.1791, 0.107, 0.311, 0.07592]
+   ],
+    description = " Press flag if any erroneous output comes ",
+     theme=gr.themes.Soft(),
+     title = "Breast Cancer Predictor Model",
+     live = True
+     
+)
+
+
+# Launch the Gradio interface
+input_component.launch(share=True, inline = False, debug=True)