Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import gradio as gr
+from sklearn.neural_network import MLPRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# -------------------------------
+# Step 1: Generate synthetic data
+# -------------------------------
+def forward_kinematics(theta):
+    x = np.cos(theta[0]) + np.cos(theta[1]) + np.cos(theta[2])
+    y = np.sin(theta[0]) + np.sin(theta[1]) + np.sin(theta[2])
+    z = theta[3] + theta[4] + theta[5]
+    return np.array([x, y, z])
+
+# Generate dataset
+def create_dataset(num_samples=5000):
+    X = []
+    y = []
+    for _ in range(num_samples):
+        angles = np.random.uniform(low=-np.pi, high=np.pi, size=6)
+        position = forward_kinematics(angles)
+        X.append(position)
+        y.append(angles)
+    return np.array(X), np.array(y)
+
+# Generate and train
+X, y = create_dataset()
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+model = MLPRegressor(hidden_layer_sizes=(64, 64), max_iter=1000)
+model.fit(X_train, y_train)
+
+# Optional: Evaluate model
+mse = mean_squared_error(y_test, model.predict(X_test))
+print(f"Test MSE: {mse:.4f}")
+
+# -------------------------------
+# Step 2: Use ML to predict angles
+# -------------------------------
+def predict_angles_with_ml(target_position):
+    return model.predict([target_position])[0]
+
+def generate_trajectory(target_position):
+    initial_angles = [0, 0, 0, 0, 0, 0]
+    target_angles = predict_angles_with_ml(target_position)
+    trajectory = np.linspace(initial_angles, target_angles, 100)
+    return trajectory
+
+def plot_trajectory(trajectory):
+    x, y, z = [], [], []
+    for theta in trajectory:
+        pos = forward_kinematics(theta)
+        x.append(pos[0])
+        y.append(pos[1])
+        z.append(pos[2])
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    ax.plot(x, y, z, label="Robot Trajectory")
+    ax.scatter(x[0], y[0], z[0], color='green', label="Start")
+    ax.scatter(x[-1], y[-1], z[-1], color='red', label="Target")
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+    ax.legend()
+    return fig
+
+# -------------------------------
+# Step 3: Gradio Interface
+# -------------------------------
+def gradio_interface(x, y, z):
+    target_position = np.array([x, y, z])
+    trajectory = generate_trajectory(target_position)
+    fig = plot_trajectory(trajectory)
+    return fig
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[gr.Number(label="Target X"), gr.Number(label="Target Y"), gr.Number(label="Target Z")],
+    outputs="plot",
+    title="ML-Based Inverse Kinematics for 6-DOF Robot",
+    description="Neural network predicts joint angles for a given (x, y, z) target using learned inverse kinematics."
+)
+
+iface.launch()