Update app.py

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
 import numpy as np
 import plotly.graph_objects as go
-import pandas as pd
+import matplotlib.pyplot as plt
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error, accuracy_score
 from sklearn.tree import plot_tree
-import matplotlib.pyplot as plt
 
 # ------------------------------------------------
 # DATA GENERATORS
@@ -25,6 +23,7 @@ def generate_3d_regression(n_points, noise):
 
 
 def generate_classification(n_points, noise):
+    n_points = int(n_points)
     X = np.random.randn(n_points, 2)
     y = (X[:, 0]**2 + X[:, 1] > 0.5).astype(int)
     X += np.random.randn(*X.shape) * noise * 0.1
@@ -32,10 +31,13 @@ def generate_classification(n_points, noise):
 
 
 # ------------------------------------------------
-# TRUE INTERACTIVE 3D USING PLOTLY
+# INTERACTIVE 3D RANDOM FOREST
 # ------------------------------------------------
 
 def interactive_3d(n_points, noise, n_estimators, max_depth):
+    n_estimators = int(n_estimators)
+    max_depth = int(max_depth)
+
     X1, X2, X_flat, Z_flat = generate_3d_regression(n_points, noise)
 
     rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
@@ -50,12 +52,7 @@ def interactive_3d(n_points, noise, n_estimators, max_depth):
 
     fig.update_layout(
         title="Interactive 3D Random Forest Surface",
-        scene=dict(
-            xaxis_title="X1",
-            yaxis_title="X2",
-            zaxis_title="Z",
-            bgcolor="#0b1e3d"
-        ),
+        scene=dict(bgcolor="#0b1e3d"),
         paper_bgcolor="#0b1e3d",
         font=dict(color="white")
     )
@@ -85,6 +82,9 @@ def show_tree(rf_model):
 # ------------------------------------------------
 
 def classification_view(n_points, noise, n_estimators, max_depth):
+    n_estimators = int(n_estimators)
+    max_depth = int(max_depth)
+
     X, y = generate_classification(n_points, noise)
 
     rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
@@ -113,73 +113,18 @@ def classification_view(n_points, noise, n_estimators, max_depth):
 
 
 # ------------------------------------------------
-# LINEAR VS RANDOM FOREST COMPARISON
-# ------------------------------------------------
-
-def compare_models(n_points, noise):
-    x = np.linspace(0, 10, n_points).reshape(-1, 1)
-    y = 2.5 * x.flatten() + 5 + np.random.randn(n_points) * noise
-
-    lr = LinearRegression().fit(x, y)
-    rf = RandomForestRegressor().fit(x, y)
-
-    y_lr = lr.predict(x)
-    y_rf = rf.predict(x)
-
-    fig = go.Figure()
-
-    fig.add_scatter(x=x.flatten(), y=y, mode="markers", name="Data")
-    fig.add_scatter(x=x.flatten(), y=y_lr, mode="lines", name="Linear")
-    fig.add_scatter(x=x.flatten(), y=y_rf, mode="lines", name="Random Forest")
-
-    fig.update_layout(
-        title="Linear vs Random Forest",
-        paper_bgcolor="#0b1e3d",
-        plot_bgcolor="#0b1e3d",
-        font=dict(color="white")
-    )
-
-    return fig
-
-
-# ------------------------------------------------
-# DATASET UPLOAD
-# ------------------------------------------------
-
-def train_uploaded(file, target, n_estimators, max_depth):
-    if file is None:
-        return None, "Upload CSV to train"
-
-    df = pd.read_csv(file.name)
-
-    if target not in df.columns:
-        return None, "Invalid target column"
-
-    X = df.drop(columns=[target])
-    y = df[target]
-
-    rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth)
-    rf.fit(X, y)
-
-    preds = rf.predict(X)
-    mse = mean_squared_error(y, preds)
-
-    return None, f"Uploaded Data MSE: {mse:.4f}"
-
-
-# ------------------------------------------------
-# GRADIO DASHBOARD UI (BLUE THEME)
+# GRADIO UI (AUTO RUN, BLUE THEME)
 # ------------------------------------------------
 
 with gr.Blocks() as demo:
 
-    gr.Markdown("# 🎓 Full Machine Learning Teaching Dashboard")
+    gr.Markdown("# 🌲 Random Forest Teaching Dashboard")
 
-    with gr.Tab("🌐 Interactive 3D"):
-        n_points = gr.Slider(20, 100, 40, label="Points")
+    with gr.Tab("🌐 3D Regression"):
+        n_points = gr.Slider(20, 100, 40, step=1, label="Points")
         noise = gr.Slider(0.0, 3.0, 1.0, label="Noise")
-        n_estimators = gr.Slider(10, 100, 50, label="Trees")
-        max_depth = gr.Slider(2, 15, 8, label="Depth")
+        n_estimators = gr.Slider(10, 100, 50, step=1, label="Trees")
+        max_depth = gr.Slider(2, 15, 8, step=1, label="Depth")
 
         plot3d = gr.Plot()
         mse_text = gr.Markdown()
@@ -190,31 +135,19 @@ with gr.Blocks() as demo:
             tree_fig = show_tree(rf)
             return fig, text, tree_fig
 
-        gr.Button("Run 3D").click(run_3d, [n_points, noise, n_estimators, max_depth], [plot3d, mse_text, tree_plot])
+        for inp in [n_points, noise, n_estimators, max_depth]:
+            inp.change(run_3d, [n_points, noise, n_estimators, max_depth], [plot3d, mse_text, tree_plot])
+
+        demo.load(run_3d, [n_points, noise, n_estimators, max_depth], [plot3d, mse_text, tree_plot])
 
     with gr.Tab("🧩 Classification"):
         cls_plot = gr.Plot()
         cls_text = gr.Markdown()
-        gr.Button("Run Classification").click(
-            classification_view,
-            [n_points, noise, n_estimators, max_depth],
-            [cls_plot, cls_text],
-        )
-
-    with gr.Tab("🧪 Compare Models"):
-        cmp_plot = gr.Plot()
-        gr.Button("Compare").click(compare_models, [n_points, noise], cmp_plot)
-
-    with gr.Tab("📂 Upload Dataset"):
-        file = gr.File(file_types=[".csv"])
-        target = gr.Textbox(label="Target Column")
-        upload_text = gr.Markdown()
-
-        gr.Button("Train Uploaded Data").click(
-            train_uploaded,
-            [file, target, n_estimators, max_depth],
-            [gr.Plot(visible=False), upload_text],
-        )
+
+        for inp in [n_points, noise, n_estimators, max_depth]:
+            inp.change(classification_view, [n_points, noise, n_estimators, max_depth], [cls_plot, cls_text])
+
+        demo.load(classification_view, [n_points, noise, n_estimators, max_depth], [cls_plot, cls_text])
 
 
 demo.launch(theme=gr.themes.Soft(primary_hue="blue"))