Create app.p

app.p

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.svm import SVC
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+# --- Mock Morningstar-style data ---
+data = {
+    "5Y_Return": [14.0, 7.5, 13.2, 6.0, 15.0, 8.0, 12.0, 6.5, 10.5, 7.2],
+    "Volatility": [8.0, 6.5, 7.8, 9.0, 7.0, 6.2, 7.1, 8.5, 6.8, 7.9],
+    "Risk_Score": [2, 3, 2, 4, 1, 3, 2, 4, 2, 3],
+    "Rating": ["Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad"]
+}
+df = pd.DataFrame(data)
+df['Label'] = df['Rating'].map({'Good': 1, 'Bad': 0})
+
+X = df[["5Y_Return", "Volatility", "Risk_Score"]]
+y = df["Label"]
+
+# Scaling
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Train SVM
+model = SVC(kernel="linear", probability=True)
+model.fit(X_scaled, y)
+
+# --- Classify and plot function ---
+def classify_and_plot(return_5y, volatility, risk_score):
+    input_data = [[return_5y, volatility, risk_score]]
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)[0]
+    confidence = model.predict_proba(input_scaled)[0][prediction]
+    result = "Good Investment" if prediction == 1 else "Bad Investment"
+
+    # --- Create plot ---
+    fig, ax = plt.subplots(figsize=(6, 5))
+
+    # Plot original data
+    scatter = ax.scatter(X_scaled[:, 0], X_scaled[:, 1], c=y, cmap="bwr", edgecolors="k", s=60)
+    ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1],
+               s=150, facecolors='none', edgecolors='k', linewidths=1.5, label="Support Vectors")
+
+    # Grid for decision boundary
+    xlim = ax.get_xlim()
+    ylim = ax.get_ylim()
+    xx = np.linspace(xlim[0], xlim[1], 30)
+    yy = np.linspace(ylim[0], ylim[1], 30)
+    YY, XX = np.meshgrid(yy, xx)
+    xy = np.vstack([XX.ravel(), YY.ravel()]).T
+    Z = model.decision_function(xy).reshape(XX.shape)
+
+    ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1],
+               alpha=0.7, linestyles=['--', '-', '--'])
+
+    ax.set_title("SVM Decision Boundary")
+    ax.set_xlabel("5Y Return (scaled)")
+    ax.set_ylabel("Volatility (scaled)")
+    ax.legend()
+    ax.grid(True)
+
+    # Save and return the figure
+    plot_path = "/tmp/svm_plot.png"
+    fig.savefig(plot_path)
+    plt.close(fig)
+
+    return f"{result} (Confidence: {confidence:.2f})", plot_path
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 SVM Classifier: Good or Bad Mutual Fund?")
+    with gr.Row():
+        return_input = gr.Number(label="5-Year Return (%)", value=10.0)
+        vol_input = gr.Number(label="Volatility (%)", value=7.0)
+        risk_input = gr.Number(label="Risk Score (1=Low, 5=High)", value=3)
+    classify_btn = gr.Button("Classify and Show Decision Boundary")
+    output_label = gr.Textbox(label="Prediction")
+    output_plot = gr.Image(label="SVM Decision Boundary")
+
+    classify_btn.click(
+        fn=classify_and_plot,
+        inputs=[return_input, vol_input, risk_input],
+        outputs=[output_label, output_plot]
+    )
+
+# Run app
+if __name__ == "__main__":
+    demo.launch()