Update app.py

app.py

@@ -3,10 +3,9 @@ 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 ---
+# --- Sample data mimicking Morningstar style ---
 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],
@@ -14,54 +13,62 @@ data = {
     "Rating": ["Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad"]
 }
 df = pd.DataFrame(data)
-df['Label'] = df['Rating'].map({'Good': 1, 'Bad': 0})
+df["Label"] = df["Rating"].map({"Good": 1, "Bad": 0})
 
+# --- Train the 3-feature SVM model for prediction ---
 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 ---
+# --- Function to predict and plot ---
 def classify_and_plot(return_5y, volatility, risk_score):
+    # Prediction
     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))
+    # --- Use only 2 features for plotting ---
+    X_2d = df[["5Y_Return", "Volatility"]].values
+    y_2d = df["Label"].values
+
+    scaler_2d = StandardScaler()
+    X_2d_scaled = scaler_2d.fit_transform(X_2d)
 
-    # 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],
+    model_2d = SVC(kernel="linear")
+    model_2d.fit(X_2d_scaled, y_2d)
+
+    # Plot decision boundary
+    fig, ax = plt.subplots(figsize=(6, 5))
+    ax.scatter(X_2d_scaled[:, 0], X_2d_scaled[:, 1], c=y_2d, cmap="bwr", edgecolors="k", s=60)
+    ax.scatter(model_2d.support_vectors_[:, 0], model_2d.support_vectors_[:, 1],
                s=150, facecolors='none', edgecolors='k', linewidths=1.5, label="Support Vectors")
 
-    # Grid for decision boundary
+    # Grid
     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)
+    Z = model_2d.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_title("SVM Decision Boundary (2 Features)")
     ax.set_xlabel("5Y Return (scaled)")
     ax.set_ylabel("Volatility (scaled)")
     ax.legend()
     ax.grid(True)
 
-    # Save and return the figure
+    # Save and return plot
     plot_path = "/tmp/svm_plot.png"
     fig.savefig(plot_path)
     plt.close(fig)
@@ -70,7 +77,7 @@ def classify_and_plot(return_5y, volatility, risk_score):
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 SVM Classifier: Good or Bad Mutual Fund?")
+    gr.Markdown("## 🧠 SVM Classifier: Mutual Fund Recommendation")
     with gr.Row():
         return_input = gr.Number(label="5-Year Return (%)", value=10.0)
         vol_input = gr.Number(label="Volatility (%)", value=7.0)
@@ -85,6 +92,6 @@ with gr.Blocks() as demo:
         outputs=[output_label, output_plot]
     )
 
-# Run app
+# Launch app
 if __name__ == "__main__":
     demo.launch()