Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.neural_network import MLPRegressor
+
+def analyze_data(csv_file):
+    # Read the CSV file
+    df = pd.read_csv(csv_file.name)
+    
+    if len(df.columns) < 3:
+        return "Error: CSV file must have at least 3 columns for analysis."
+    
+    # Use the first three columns for x, y, z
+    x, y, z = df.iloc[:, 0], df.iloc[:, 1], df.iloc[:, 2]
+    
+    # Create the original data plot
+    original_fig = go.Figure(data=[go.Scatter3d(
+        x=x, y=y, z=z, mode='markers',
+        marker=dict(size=5, color=z, colorscale='Viridis', opacity=0.8)
+    )])
+    original_fig.update_layout(title="Original Data: 3,000 points", scene=dict(
+        xaxis_title=df.columns[0], yaxis_title=df.columns[1], zaxis_title=df.columns[2]))
+    
+    # Prepare data for modeling
+    X = df.iloc[:, :2]
+    y = df.iloc[:, 2]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+    
+    # Create a grid for predictions
+    xx, yy = np.meshgrid(np.linspace(X.iloc[:, 0].min(), X.iloc[:, 0].max(), 100),
+                         np.linspace(X.iloc[:, 1].min(), X.iloc[:, 1].max(), 100))
+    grid = np.c_[xx.ravel(), yy.ravel()]
+    grid_scaled = scaler.transform(grid)
+    
+    # Train different models and create plots
+    models = [
+        ("Good model", MLPRegressor(hidden_layer_sizes=(10, 5), max_iter=1000, random_state=42)),
+        ("Underfitting", MLPRegressor(hidden_layer_sizes=(2,), max_iter=10, random_state=42)),
+        ("Overfitting", MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=42)),
+        ("4-layer NN", MLPRegressor(hidden_layer_sizes=(50, 30, 20), max_iter=100000, random_state=42))
+    ]
+    
+    figs = []
+    for name, model in models:
+        model.fit(X_train_scaled, y_train)
+        z_pred = model.predict(grid_scaled).reshape(xx.shape)
+        
+        fig = go.Figure(data=[go.Surface(x=xx, y=yy, z=z_pred, colorscale='Viridis')])
+        fig.update_layout(title=f"{name}", scene=dict(
+            xaxis_title=df.columns[0], yaxis_title=df.columns[1], zaxis_title=df.columns[2]))
+        figs.append(fig)
+    
+    return original_fig, figs[0], figs[1], figs[2], figs[3]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_data,
+    inputs=gr.File(label="Upload CSV file"),
+    outputs=[
+        gr.Plot(label="Original Data"),
+        gr.Plot(label="1. Good model"),
+        gr.Plot(label="2. Underfitting"),
+        gr.Plot(label="3. Overfitting"),
+        gr.Plot(label="4. 4-layer Neural Network")
+    ],
+    title="Advanced 3D Graph Analyzer",
+    description="Upload a CSV file with at least 3 columns to create various 3D graph visualizations and model predictions."
+)
+
+# Launch the app
+iface.launch()