import gradio as gr
import numpy as np
import torch
import sympy as sp
from transformers import pipeline
import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from typing import Dict, List, Tuple

class PhysicsSolver:
    def __init__(self):
        self.nlp = pipeline("text-classification", model="bert-base-uncased")
        
    def solve_mechanics_problem(self, problem_text: str, problem_type: str) -> str:
        """Solve various physics problems using symbolic mathematics"""
        try:
            # Common symbols
            t, v, a, s, m, F, E, P, W, k, x = sp.symbols('t v a s m F E P W k x')
            g = 9.81

            solutions = {
                'kinematics': self._solve_kinematics,
                'forces': self._solve_forces,
                'energy': self._solve_energy,
                'harmonic': self._solve_harmonic_motion
            }
            
            if problem_type in solutions:
                return solutions[problem_type](problem_text)
            else:
                return "Unsupported problem type"
            
        except Exception as e:
            return f"Error solving problem: {str(e)}"
    
    def _solve_kinematics(self, problem_text: str) -> str:
        """Handle kinematics problems"""
        t, v, a, s = sp.symbols('t v a s')
        solution = "Kinematics Analysis:\n\n"
        solution += "Using equations:\n"
        solution += "1. v = u + at\n"
        solution += "2. s = ut + ½at²\n"
        solution += "3. v² = u² + 2as\n\n"
        return solution
    
    def _solve_forces(self, problem_text: str) -> str:
        """Handle force and Newton's laws problems"""
        m, a, F = sp.symbols('m a F')
        solution = "Force Analysis:\n\n"
        solution += "Using Newton's Laws:\n"
        solution += "1. F = ma\n"
        solution += "2. Action = -Reaction\n"
        return solution
    
    def _solve_energy(self, problem_text: str) -> str:
        """Handle energy conservation problems"""
        m, v, h, k, E = sp.symbols('m v h k E')
        solution = "Energy Analysis:\n\n"
        solution += "Using energy conservation:\n"
        solution += "1. KE = ½mv²\n"
        solution += "2. PE = mgh\n"
        solution += "3. Total E = KE + PE\n"
        return solution
    
    def _solve_harmonic_motion(self, problem_text: str) -> str:
        """Handle simple harmonic motion problems"""
        m, k, x, t = sp.symbols('m k x t')
        solution = "Harmonic Motion Analysis:\n\n"
        solution += "Using SHM equations:\n"
        solution += "1. F = -kx\n"
        solution += "2. ω = √(k/m)\n"
        solution += "3. x = A cos(ωt)\n"
        return solution

class ExperimentSimulator:
    def __init__(self):
        self.supported_experiments = ['pendulum', 'projectile', 'spring', 'wave']
        
    def simulate_pendulum(self, length: float, theta0: float, time_span: float) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Simulate simple pendulum motion"""
        def pendulum_eq(state, t, L):
            theta, omega = state
            dydt = [omega, -(9.81/L) * np.sin(theta)]
            return dydt
        
        t = np.linspace(0, time_span, 1000)
        state0 = [theta0, 0]
        solution = odeint(pendulum_eq, state0, t, args=(length,))
        
        x = length * np.sin(solution[:, 0])
        y = -length * np.cos(solution[:, 0])
        
        return x, y, t
    
    def simulate_projectile(self, v0: float, angle: float, height: float) -> Tuple[np.ndarray, np.ndarray]:
        """Simulate projectile motion"""
        g = 9.81
        theta = np.radians(angle)
        
        # Calculate time of flight
        t_flight = (v0 * np.sin(theta) + np.sqrt((v0 * np.sin(theta))**2 + 2*g*height)) / g
        t = np.linspace(0, t_flight, 1000)
        
        # Calculate position
        x = v0 * np.cos(theta) * t
        y = height + v0 * np.sin(theta) * t - 0.5 * g * t**2
        
        return x, y
    
    def simulate_spring(self, mass: float, k: float, x0: float, time_span: float) -> Tuple[np.ndarray, np.ndarray]:
        """Simulate spring motion"""
        omega = np.sqrt(k/mass)
        t = np.linspace(0, time_span, 1000)
        x = x0 * np.cos(omega * t)
        v = -x0 * omega * np.sin(omega * t)
        
        return x, v, t
    
    def simulate_wave(self, amplitude: float, frequency: float, wavelength: float, time_span: float) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """Simulate wave propagation"""
        x = np.linspace(0, 5*wavelength, 1000)
        t = np.linspace(0, time_span, 100)
        k = 2 * np.pi / wavelength
        omega = 2 * np.pi * frequency
        
        X, T = np.meshgrid(x, t)
        Y = amplitude * np.sin(k*X - omega*T)
        
        return X, T, Y

class VisualizationTools:
    @staticmethod
    def plot_pendulum(x: np.ndarray, y: np.ndarray, t: np.ndarray) -> go.Figure:
        """Create an interactive pendulum visualization"""
        fig = go.Figure()
        
        # Add pendulum path
        fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='Pendulum Path'))
        
        # Add animation frames
        frames = [go.Frame(data=[go.Scatter(
            x=[0, x[i]],
            y=[0, y[i]],
            mode='lines+markers',
            line=dict(color='red', width=2),
            marker=dict(size=[10, 20])
        )]) for i in range(0, len(t), 10)]
        
        fig.frames = frames
        
        # Add animation controls
        fig.update_layout(
            updatemenus=[dict(
                type='buttons',
                showactive=False,
                buttons=[dict(label='Play',
                            method='animate',
                            args=[None, dict(frame=dict(duration=50, redraw=True),
                                          fromcurrent=True,
                                          mode='immediate')])])])
        
        fig.update_layout(
            title='Simple Pendulum Motion',
            xaxis_title='X Position (m)',
            yaxis_title='Y Position (m)',
            yaxis=dict(scaleanchor="x", scaleratio=1),
        )
        
        return fig
    
    @staticmethod
    def plot_projectile(x: np.ndarray, y: np.ndarray) -> go.Figure:
        """Create projectile motion visualization"""
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(x=x, y=y, mode='lines',
                               name='Projectile Path'))
        
        fig.update_layout(
            title='Projectile Motion',
            xaxis_title='Distance (m)',
            yaxis_title='Height (m)',
            yaxis=dict(scaleanchor="x", scaleratio=1),
        )
        
        return fig
    
    @staticmethod
    def plot_spring(x: np.ndarray, v: np.ndarray, t: np.ndarray) -> go.Figure:
        """Create spring motion visualization"""
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(x=t, y=x, name='Position'))
        fig.add_trace(go.Scatter(x=t, y=v, name='Velocity'))
        
        fig.update_layout(
            title='Spring Motion',
            xaxis_title='Time (s)',
            yaxis_title='Position/Velocity',
        )
        
        return fig
    
    @staticmethod
    def plot_wave(X: np.ndarray, T: np.ndarray, Y: np.ndarray) -> go.Figure:
        """Create wave propagation visualization"""
        fig = go.Figure()
        
        # Create animation frames
        frames = [go.Frame(
            data=[go.Scatter(
                x=X[i],
                y=Y[i],
                mode='lines',
                line=dict(width=2)
            )],
            name=f'frame{i}'
        ) for i in range(len(T))]
        
        fig.frames = frames
        
        # Add initial data
        fig.add_trace(go.Scatter(
            x=X[0],
            y=Y[0],
            mode='lines',
            line=dict(width=2)
        ))
        
        # Add animation controls
        fig.update_layout(
            updatemenus=[dict(
                type='buttons',
                showactive=False,
                buttons=[dict(label='Play',
                            method='animate',
                            args=[None, dict(frame=dict(duration=50, redraw=True),
                                          fromcurrent=True,
                                          mode='immediate')])])])
        
        fig.update_layout(
            title='Wave Propagation',
            xaxis_title='Position (m)',
            yaxis_title='Amplitude',
        )
        
        return fig

def create_interface():
    # Initialize components
    solver = PhysicsSolver()
    simulator = ExperimentSimulator()
    viz_tools = VisualizationTools()
    
    # Define interface functions
    def solve_physics_problem(problem_text: str, problem_type: str) -> str:
        return solver.solve_mechanics_problem(problem_text, problem_type)
    
    def run_pendulum_simulation(length: float, initial_angle: float, duration: float) -> go.Figure:
        x, y, t = simulator.simulate_pendulum(length, np.radians(initial_angle), duration)
        return viz_tools.plot_pendulum(x, y, t)
    
    def run_projectile_simulation(velocity: float, angle: float, height: float) -> go.Figure:
        x, y = simulator.simulate_projectile(velocity, angle, height)
        return viz_tools.plot_projectile(x, y)
    
    def run_spring_simulation(mass: float, k: float, x0: float, duration: float) -> go.Figure:
        x, v, t = simulator.simulate_spring(mass, k, x0, duration)
        return viz_tools.plot_spring(x, v, t)
    
    def run_wave_simulation(amplitude: float, frequency: float, wavelength: float, duration: float) -> go.Figure:
        X, T, Y = simulator.simulate_wave(amplitude, frequency, wavelength, duration)
        return viz_tools.plot_wave(X, T, Y)
    
    # Create the interface
    with gr.Blocks() as interface:
        gr.Markdown("# Enhanced AI Physics Platform")
        
        with gr.Tab("Problem Solver"):
            problem_input = gr.Textbox(
                label="Enter your physics problem",
                placeholder="Describe your physics problem here..."
            )
            problem_type = gr.Dropdown(
                choices=['kinematics', 'forces', 'energy', 'harmonic'],
                label="Problem Type"
            )
            solve_button = gr.Button("Solve Problem")
            solution_output = gr.Textbox(label="Solution")
            
            solve_button.click(
                fn=solve_physics_problem,
                inputs=[problem_input, problem_type],
                outputs=solution_output
            )
        
        with gr.Tab("Pendulum Simulation"):
            with gr.Row():
                length_input = gr.Slider(1, 10, value=2, label="Pendulum Length (m)")
                angle_input = gr.Slider(0, 90, value=45, label="Initial Angle (degrees)")
                duration_input = gr.Slider(1, 20, value=10, label="Simulation Duration (s)")
            
            pendulum_button = gr.Button("Run Pendulum Simulation")
            pendulum_plot = gr.Plot(label="Pendulum Motion")
            
            pendulum_button.click(
                fn=run_pendulum_simulation,
                inputs=[length_input, angle_input, duration_input],
                outputs=pendulum_plot
            )
        
        with gr.Tab("Projectile Motion"):
            with gr.Row():
                velocity_input = gr.Slider(0, 50, value=20, label="Initial Velocity (m/s)")
                proj_angle_input = gr.Slider(0, 90, value=45, label="Launch Angle (degrees)")
                height_input = gr.Slider(0, 100, value=0, label="Initial Height (m)")
            
            projectile_button = gr.Button("Run Projectile Simulation")
            projectile_plot = gr.Plot(label="Projectile Motion")
            
            projectile_button.click(
                fn=run_projectile_simulation,
                inputs=[velocity_input, proj_angle_input, height_input],
                outputs=projectile_plot
            )
        
        with gr.Tab("Spring Motion"):
            with gr.Row():
                mass_input = gr.Slider(0.1, 10, value=1, label="Mass (kg)")
                k_input = gr.Slider(1, 100, value=10, label="Spring Constant (N/m)")
                x0_input = gr.Slider(0.1, 2, value=0.5, label="Initial Displacement (m)")
                spring_duration = gr.Slider(1, 20, value=10, label="Simulation Duration (s)")
            
            spring_button = gr.Button("Run Spring Simulation")
            spring_plot = gr.Plot(label="Spring Motion")
            
            spring_button.click(
                fn=run_spring_simulation,
                inputs=[mass_input, k_input, x0_input, spring_duration],
                outputs=spring_plot
            )
        
        with gr.Tab("Wave Propagation"):
            with gr.Row():
                amp_input = gr.Slider(0.1, 2, value=1, label="Amplitude (m)")
                freq_input = gr.Slider(0.1, 5, value=1, label="Frequency (Hz)")
                wavelength_input = gr.Slider(0.1, 10, value=2, label="Wavelength (m)")
                wave_duration = gr.Slider(1, 20, value=10, label="Simulation Duration (s)")
            
            # Continuing from the previous code...
            wave_button = gr.Button("Run Wave Simulation")
            wave_plot = gr.Plot(label="Wave Propagation")
            
            wave_button.click(
                fn=run_wave_simulation,
                inputs=[amp_input, freq_input, wavelength_input, wave_duration],
                outputs=wave_plot
            )

        with gr.Tab("Advanced Visualizations"):
            with gr.Row():
                visualization_type = gr.Dropdown(
                    choices=[
                        'Phase Space Plot',
                        'Energy Distribution',
                        'Vector Field',
                        '3D Motion'
                    ],
                    label="Visualization Type"
                )
            
            def create_advanced_visualization(viz_type):
                if viz_type == 'Phase Space Plot':
                    return create_phase_space_plot()
                elif viz_type == 'Energy Distribution':
                    return create_energy_distribution()
                elif viz_type == 'Vector Field':
                    return create_vector_field()
                elif viz_type == '3D Motion':
                    return create_3d_motion()
            
            advanced_viz_button = gr.Button("Generate Visualization")
            advanced_plot = gr.Plot(label="Advanced Visualization")
            
            advanced_viz_button.click(
                fn=create_advanced_visualization,
                inputs=visualization_type,
                outputs=advanced_plot
            )

        with gr.Tab("Data Analysis"):
            data_input = gr.File(label="Upload Experimental Data (CSV)")
            analysis_type = gr.Dropdown(
                choices=[
                    'Statistical Analysis',
                    'Curve Fitting',
                    'Error Analysis',
                    'Fourier Transform'
                ],
                label="Analysis Type"
            )
            
            def analyze_data(file, analysis_type):
                # Add data analysis functionality here
                return f"Analysis results for {analysis_type}"
            
            analyze_button = gr.Button("Analyze Data")
            analysis_output = gr.Textbox(label="Analysis Results")
            analysis_plot = gr.Plot(label="Analysis Visualization")
            
            analyze_button.click(
                fn=analyze_data,
                inputs=[data_input, analysis_type],
                outputs=[analysis_output, analysis_plot]
            )
    
    return interface

# Additional visualization functions
def create_phase_space_plot():
    """Create a phase space plot for a dynamical system"""
    fig = go.Figure()
    
    # Generate sample phase space data
    t = np.linspace(0, 20, 1000)
    x = np.sin(t)
    v = np.cos(t)
    
    fig.add_trace(go.Scatter(x=x, y=v, mode='lines',
                           name='Phase Space Trajectory'))
    
    fig.update_layout(
        title='Phase Space Plot',
        xaxis_title='Position',
        yaxis_title='Velocity'
    )
    
    return fig

def create_energy_distribution():
    """Create an energy distribution visualization"""
    fig = go.Figure()
    
    # Generate sample energy data
    E = np.linspace(0, 10, 100)
    P = np.exp(-E) * np.sqrt(E)  # Maxwell-Boltzmann-like distribution
    
    fig.add_trace(go.Scatter(x=E, y=P, mode='lines',
                           name='Energy Distribution'))
    
    fig.update_layout(
        title='Energy Distribution',
        xaxis_title='Energy (J)',
        yaxis_title='Probability Density'
    )
    
    return fig

def create_vector_field():
    """Create a vector field visualization"""
    fig = go.Figure()
    
    # Generate vector field data
    x = np.linspace(-5, 5, 20)
    y = np.linspace(-5, 5, 20)
    X, Y = np.meshgrid(x, y)
    
    U = -Y  # x-component of vector field
    V = X   # y-component of vector field
    
    fig.add_trace(go.Cone(
        x=X.flatten(),
        y=Y.flatten(),
        u=U.flatten(),
        v=V.flatten(),
        name='Vector Field'
    ))
    
    fig.update_layout(
        title='Vector Field Visualization',
        scene=dict(
            xaxis_title='X',
            yaxis_title='Y'
        )
    )
    
    return fig

def create_3d_motion():
    """Create a 3D motion visualization"""
    fig = go.Figure()
    
    # Generate 3D motion data (e.g., spiral motion)
    t = np.linspace(0, 10*np.pi, 1000)
    x = np.cos(t)
    y = np.sin(t)
    z = t/10
    
    fig.add_trace(go.Scatter3d(
        x=x, y=y, z=z,
        mode='lines',
        name='3D Motion Path'
    ))
    
    fig.update_layout(
        title='3D Motion Visualization',
        scene=dict(
            xaxis_title='X',
            yaxis_title='Y',
            zaxis_title='Z'
        )
    )
    
    return fig

class DataAnalyzer:
    @staticmethod
    def statistical_analysis(data):
        """Perform statistical analysis on experimental data"""
        stats = {
            'mean': np.mean(data),
            'std': np.std(data),
            'median': np.median(data),
            'min': np.min(data),
            'max': np.max(data)
        }
        return stats
    
    @staticmethod
    def curve_fit(x, y):
        """Perform curve fitting on experimental data"""
        from scipy.optimize import curve_fit
        
        def func(x, a, b, c):
            return a * np.exp(-b * x) + c
        
        popt, pcov = curve_fit(func, x, y)
        return popt, pcov
    
    @staticmethod
    def error_analysis(data, uncertainties):
        """Perform error propagation analysis"""
        # Add error analysis calculations here
        pass
    
    @staticmethod
    def fourier_transform(data):
        """Perform Fourier transform analysis"""
        from scipy.fft import fft, fftfreq
        
        n = len(data)
        freq = fftfreq(n)
        freq_spectrum = fft(data)
        return freq, freq_spectrum

# Launch the interface
if __name__ == "__main__":
    interface = create_interface()
    interface.launch(share=True)