Upload App.py

App.py

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+# -*- coding: utf-8 -*-
+"""Workshop-Linear kernel.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PlQo6At9kkwbacgmJdJ7FuN93WVLFoEY
+"""
+
+import numpy as np
+
+from sklearn import datasets
+
+from sklearn.model_selection import train_test_split
+
+from sklearn.preprocessing import StandardScaler
+
+from sklearn.svm import SVC
+
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Breast cancer dataset: target = malignant (0) / benign (1)
+
+data = datasets.load_breast_cancer()
+
+X = data.data      # features
+
+y = data.target    # labels
+
+
+
+print("Feature matrix shape:", X.shape)
+
+print("Labels shape:", y.shape)
+
+X_train, X_test, y_train, y_test = train_test_split(
+
+    X, y, test_size=0.3, random_state=42, stratify=y
+
+)
+
+print("Train size:", X_train.shape[0], "Test size:", X_test.shape[0])
+
+scaler = StandardScaler()
+
+X_train_scaled = scaler.fit_transform(X_train)
+
+X_test_scaled = scaler.transform(X_test)
+
+linear_svm = SVC(kernel='linear', C=1.0, random_state=42)
+
+linear_svm.fit(X_train_scaled, y_train)
+
+
+
+y_pred_linear = linear_svm.predict(X_test_scaled)
+
+acc_linear = accuracy_score(y_test, y_pred_linear)
+
+
+
+print("\n=== Linear Kernel SVM (Dot Product) ===")
+
+print("Accuracy:", acc_linear)
+
+print("Confusion matrix:\n", confusion_matrix(y_test, y_pred_linear))
+
+print("Classification report:\n", classification_report(y_test, y_pred_linear))
+
+rbf_svm = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)
+
+rbf_svm.fit(X_train_scaled, y_train)
+
+
+
+y_pred_rbf = rbf_svm.predict(X_test_scaled)
+
+acc_rbf = accuracy_score(y_test, y_pred_rbf)
+
+
+
+print("\n=== RBF Kernel SVM ===")
+
+print("Accuracy:", acc_rbf)
+
+print("Confusion matrix:\n", confusion_matrix(y_test, y_pred_rbf))
+
+print("Classification report:\n", classification_report(y_test, y_pred_rbf))
+
+X_small = X_train_scaled[:5]
+
+
+
+# Dot product kernel matrix: K_ij = x_i · x_j
+
+K_dot = X_small @ X_small.T
+
+
+
+# RBF kernel matrix by hand
+
+def rbf_kernel(X1, X2, gamma):
+
+    # ||x - x'||^2 = (x^2 summed) + (x'^2 summed) - 2 x·x'
+
+    X1_sq = np.sum(X1 ** 2, axis=1).reshape(-1, 1)
+
+    X2_sq = np.sum(X2 ** 2, axis=1).reshape(1, -1)
+
+    sq_dist = X1_sq + X2_sq - 2 * X1 @ X2.T
+
+    return np.exp(-gamma * sq_dist)
+
+
+
+gamma_value = 0.1
+
+K_rbf = rbf_kernel(X_small, X_small, gamma=gamma_value)
+
+
+
+print("\n=== Example Kernel Matrices on 5 Training Samples ===")
+
+print("\nDot-product kernel matrix (linear):\n", K_dot)
+
+print("\nRBF kernel matrix (gamma = 0.1):\n", K_rbf)
+
+import gradio as gr
+
+def predict_cancer(feature1, feature2, feature3, feature4, feature5, feature6, feature7, feature8, feature9, feature10, feature11, feature12, feature13, feature14, feature15, feature16, feature17, feature18, feature19, feature20, feature21, feature22, feature23, feature24, feature25, feature26, feature27, feature28, feature29, feature30):
+    # Collect all features into a numpy array
+    features = np.array([
+        feature1, feature2, feature3, feature4, feature5, feature6, feature7, feature8, feature9, feature10,
+        feature11, feature12, feature13, feature14, feature15, feature16, feature17, feature18, feature19, feature20,
+        feature21, feature22, feature23, feature24, feature25, feature26, feature27, feature28, feature29, feature30
+    ]).reshape(1, -1)
+
+    # Scale the input features using the pre-trained scaler
+    features_scaled = scaler.transform(features)
+
+    # Get predictions from both models
+    prediction_linear = linear_svm.predict(features_scaled)[0]
+    prediction_rbf = rbf_svm.predict(features_scaled)[0]
+
+    # Map predictions to human-readable labels
+    label_map = {0: "Malignant", 1: "Benign"}
+    output_linear = label_map[prediction_linear]
+    output_rbf = label_map[prediction_rbf]
+
+    return output_linear, output_rbf
+
+# Create Gradio input components for 30 features
+inputs = [
+    gr.Number(label=f"Feature {i+1}") for i in range(30)
+]
+
+# Create Gradio output components for both models' predictions
+outputs = [
+    gr.Textbox(label="Linear SVM Prediction"),
+    gr.Textbox(label="RBF SVM Prediction")
+]
+
+# Create and launch the Gradio interface
+iface = gr.Interface(
+    fn=predict_cancer,
+    inputs=inputs,
+    outputs=outputs,
+    title="Breast Cancer Prediction using SVM",
+    description="Enter 30 features to predict if a tumor is Malignant (0) or Benign (1) using Linear and RBF Kernel SVMs."
+)
+
+iface.launch()