app.py

import gradio as gr
import numpy as np
import torch
import json
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from typing import Dict, List, Tuple, Any
import logging

# Import your quantum AI agent (assuming it's in quantum_agent.py)
from quantum_agent import QuantumAIAgent, QuantumState, QuantumCircuit

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class QuantumAIInterface:
    """Gradio interface for the Quantum AI Agent."""
    
    def __init__(self):
        self.agent = QuantumAIAgent()
        logger.info("Quantum AI Interface initialized")
    
    def optimize_vqe(self, num_qubits: int, optimization_steps: int) -> Tuple[str, str]:
        """VQE optimization interface."""
        try:
            # Create a random Hamiltonian
            dim = 2**num_qubits
            hamiltonian = np.random.random((dim, dim))
            hamiltonian = hamiltonian + hamiltonian.T  # Make Hermitian
            
            # Initialize random parameters
            initial_params = np.random.random(num_qubits * 2)
            
            # Run optimization
            result = self.agent.optimize_quantum_algorithm("VQE", hamiltonian, initial_params)
            
            # Format results
            result_text = f"""
VQE Optimization Results:
========================
Ground State Energy: {result['ground_state_energy']:.6f}
Optimization Success: {result['optimization_success']}
Number of Iterations: {result['iterations']}
Optimal Parameters: {np.array2string(result['optimal_parameters'], precision=4)}
Circuit Depth: {result['optimal_circuit'].depth}
            """
            
            # Create visualization
            fig = plt.figure(figsize=(10, 6))
            plt.subplot(1, 2, 1)
            plt.plot(result['optimal_parameters'], 'bo-')
            plt.title('Optimal Parameters')
            plt.xlabel('Parameter Index')
            plt.ylabel('Value')
            
            plt.subplot(1, 2, 2)
            plt.bar(range(len(result['optimal_parameters'])), result['optimal_parameters'])
            plt.title('Parameter Distribution')
            plt.xlabel('Parameter Index')
            plt.ylabel('Value')
            
            plt.tight_layout()
            plt.savefig('vqe_results.png', dpi=150, bbox_inches='tight')
            plt.close()
            
            return result_text, 'vqe_results.png'
            
        except Exception as e:
            error_msg = f"Error in VQE optimization: {str(e)}"
            logger.error(error_msg)
            return error_msg, None
    
    def optimize_qaoa(self, num_qubits: int, num_layers: int) -> Tuple[str, str]:
        """QAOA optimization interface."""
        try:
            # Create problem Hamiltonian
            dim = 2**num_qubits
            hamiltonian = np.random.random((dim, dim))
            hamiltonian = hamiltonian + hamiltonian.T
            
            # Initialize QAOA parameters
            initial_params = np.random.random(2 * num_layers)  # beta and gamma
            
            # Run optimization
            result = self.agent.optimize_quantum_algorithm("QAOA", hamiltonian, initial_params)
            
            result_text = f"""
QAOA Optimization Results:
=========================
Optimal Value: {result['optimal_value']:.6f}
Optimization Success: {result['optimization_success']}
Number of Iterations: {result['iterations']}
Optimal Beta: {np.array2string(result['optimal_beta'], precision=4)}
Optimal Gamma: {np.array2string(result['optimal_gamma'], precision=4)}
            """
            
            # Create visualization
            fig = plt.figure(figsize=(12, 5))
            
            plt.subplot(1, 3, 1)
            plt.plot(result['optimal_beta'], 'ro-', label='Beta')
            plt.plot(result['optimal_gamma'], 'bo-', label='Gamma')
            plt.title('QAOA Parameters')
            plt.xlabel('Layer')
            plt.ylabel('Value')
            plt.legend()
            
            plt.subplot(1, 3, 2)
            plt.bar(range(len(result['optimal_beta'])), result['optimal_beta'], alpha=0.7, label='Beta')
            plt.title('Beta Parameters')
            plt.xlabel('Layer')
            plt.ylabel('Value')
            
            plt.subplot(1, 3, 3)
            plt.bar(range(len(result['optimal_gamma'])), result['optimal_gamma'], alpha=0.7, label='Gamma', color='orange')
            plt.title('Gamma Parameters')
            plt.xlabel('Layer')
            plt.ylabel('Value')
            
            plt.tight_layout()
            plt.savefig('qaoa_results.png', dpi=150, bbox_inches='tight')
            plt.close()
            
            return result_text, 'qaoa_results.png'
            
        except Exception as e:
            error_msg = f"Error in QAOA optimization: {str(e)}"
            logger.error(error_msg)
            return error_msg, None
    
    def demonstrate_error_mitigation(self, num_qubits: int, noise_level: float) -> Tuple[str, str]:
        """Error mitigation demonstration."""
        try:
            # Create a quantum state
            dim = 2**num_qubits
            amplitudes = np.random.random(dim) + 1j * np.random.random(dim)
            amplitudes = amplitudes / np.linalg.norm(amplitudes)
            
            quantum_state = QuantumState(
                amplitudes=amplitudes,
                num_qubits=num_qubits,
                fidelity=1.0 - noise_level
            )
            
            # Apply error mitigation
            noise_model = {"noise_factor": noise_level}
            corrected_state = self.agent.mitigate_errors(quantum_state, noise_model)
            
            result_text = f"""
Error Mitigation Results:
========================
Number of Qubits: {num_qubits}
Original Fidelity: {quantum_state.fidelity:.4f}
Corrected Fidelity: {corrected_state.fidelity:.4f}
Fidelity Improvement: {corrected_state.fidelity - quantum_state.fidelity:.4f}
Noise Level: {noise_level:.4f}
            """
            
            # Create visualization
            fig = plt.figure(figsize=(12, 5))
            
            plt.subplot(1, 3, 1)
            plt.bar(['Original', 'Corrected'], [quantum_state.fidelity, corrected_state.fidelity])
            plt.title('Fidelity Comparison')
            plt.ylabel('Fidelity')
            plt.ylim(0, 1)
            
            plt.subplot(1, 3, 2)
            plt.plot(np.abs(quantum_state.amplitudes), 'b-', label='Original', alpha=0.7)
            plt.plot(np.abs(corrected_state.amplitudes), 'r-', label='Corrected', alpha=0.7)
            plt.title('State Amplitudes (Magnitude)')
            plt.xlabel('Basis State')
            plt.ylabel('Amplitude')
            plt.legend()
            
            plt.subplot(1, 3, 3)
            improvement = corrected_state.fidelity - quantum_state.fidelity
            plt.bar(['Fidelity Improvement'], [improvement], color='green' if improvement > 0 else 'red')
            plt.title('Improvement')
            plt.ylabel('Fidelity Change')
            
            plt.tight_layout()
            plt.savefig('error_mitigation_results.png', dpi=150, bbox_inches='tight')
            plt.close()
            
            return result_text, 'error_mitigation_results.png'
            
        except Exception as e:
            error_msg = f"Error in error mitigation: {str(e)}"
            logger.error(error_msg)
            return error_msg, None
    
    def optimize_resources(self, num_circuits: int, max_qubits: int, available_qubits: int) -> Tuple[str, str]:
        """Resource optimization demonstration."""
        try:
            # Generate random circuits
            circuits = []
            for i in range(num_circuits):
                num_qubits = np.random.randint(2, min(max_qubits, available_qubits) + 1)
                depth = np.random.randint(5, 50)
                circuits.append(QuantumCircuit([], np.array([]), num_qubits, depth))
            
            # Optimize resources
            allocation_plan = self.agent.optimize_resources(circuits, available_qubits)
            
            result_text = f"""
Resource Optimization Results:
=============================
Number of Circuits: {num_circuits}
Available Qubits: {available_qubits}
Resource Utilization: {allocation_plan['resource_utilization']:.2%}
Estimated Runtime: {allocation_plan['estimated_runtime']:.2f} time units
Scheduled Circuits: {len(allocation_plan['schedule'])}
            """
            
            if allocation_plan['schedule']:
                result_text += "\nSchedule Details:\n"
                for i, task in enumerate(allocation_plan['schedule'][:5]):  # Show first 5
                    result_text += f"Circuit {task['circuit_id']}: {task['qubits_allocated']} qubits, starts at {task['start_time']:.2f}\n"
            
            # Create visualization
            fig = plt.figure(figsize=(12, 8))
            
            # Resource utilization
            plt.subplot(2, 2, 1)
            plt.pie([allocation_plan['resource_utilization'], 1 - allocation_plan['resource_utilization']], 
                    labels=['Used', 'Available'], autopct='%1.1f%%')
            plt.title('Resource Utilization')
            
            # Circuit requirements
            plt.subplot(2, 2, 2)
            qubit_reqs = [c.num_qubits for c in circuits]
            plt.hist(qubit_reqs, bins=min(10, max_qubits), alpha=0.7)
            plt.title('Circuit Qubit Requirements')
            plt.xlabel('Number of Qubits')
            plt.ylabel('Frequency')
            
            # Circuit depths
            plt.subplot(2, 2, 3)
            depths = [c.depth for c in circuits]
            plt.hist(depths, bins=10, alpha=0.7, color='orange')
            plt.title('Circuit Depths')
            plt.xlabel('Depth')
            plt.ylabel('Frequency')
            
            # Schedule timeline
            plt.subplot(2, 2, 4)
            if allocation_plan['schedule']:
                start_times = [task['start_time'] for task in allocation_plan['schedule']]
                durations = [task['estimated_duration'] for task in allocation_plan['schedule']]
                plt.barh(range(len(start_times)), durations, left=start_times, alpha=0.7)
                plt.title('Schedule Timeline')
                plt.xlabel('Time')
                plt.ylabel('Circuit')
            
            plt.tight_layout()
            plt.savefig('resource_optimization_results.png', dpi=150, bbox_inches='tight')
            plt.close()
            
            return result_text, 'resource_optimization_results.png'
            
        except Exception as e:
            error_msg = f"Error in resource optimization: {str(e)}"
            logger.error(error_msg)
            return error_msg, None
    
    def hybrid_processing_demo(self, data_size: int, quantum_component: str) -> Tuple[str, str]:
        """Hybrid processing demonstration."""
        try:
            # Generate classical data
            classical_data = np.random.random(data_size)
            
            # Run hybrid processing
            result = self.agent.hybrid_processing(classical_data, quantum_component)
            
            result_text = f"""
Hybrid Processing Results:
=========================
Input Data Size: {data_size}
Quantum Component: {quantum_component}
Output Statistics:
  Mean: {result['final_result']['statistics']['mean']:.6f}
  Std: {result['final_result']['statistics']['std']:.6f}
  Min: {result['final_result']['statistics']['min']:.6f}
  Max: {result['final_result']['statistics']['max']:.6f}
Confidence: {result['final_result']['confidence']:.4f}
            """
            
            # Create visualization
            fig = plt.figure(figsize=(15, 5))
            
            plt.subplot(1, 3, 1)
            plt.plot(classical_data, 'b-', alpha=0.7)
            plt.title('Original Classical Data')
            plt.xlabel('Index')
            plt.ylabel('Value')
            
            plt.subplot(1, 3, 2)
            plt.plot(result['preprocessed_data'], 'g-', alpha=0.7)
            plt.title('Preprocessed Data')
            plt.xlabel('Index')
            plt.ylabel('Value')
            
            plt.subplot(1, 3, 3)
            plt.plot(result['quantum_result'].flatten(), 'r-', alpha=0.7)
            plt.title(f'Quantum Result ({quantum_component})')
            plt.xlabel('Index')
            plt.ylabel('Value')
            
            plt.tight_layout()
            plt.savefig('hybrid_processing_results.png', dpi=150, bbox_inches='tight')
            plt.close()
            
            return result_text, 'hybrid_processing_results.png'
            
        except Exception as e:
            error_msg = f"Error in hybrid processing: {str(e)}"
            logger.error(error_msg)
            return error_msg, None

def create_interface():
    """Create the Gradio interface."""
    interface = QuantumAIInterface()
    
    with gr.Blocks(title="Quantum AI Agent", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🚀 Quantum AI Agent
        
        This is an AI agent designed to optimize quantum computing algorithms using classical machine learning techniques.
        
        ## Features:
        - **Algorithm Optimization**: VQE, QAOA, QNN parameter optimization
        - **Error Mitigation**: AI-powered quantum error correction
        - **Resource Management**: Intelligent qubit allocation and scheduling
        - **Hybrid Processing**: Quantum-classical algorithm integration
        """)
        
        with gr.Tabs():
            # VQE Tab
            with gr.TabItem("🔬 VQE Optimization"):
                gr.Markdown("### Variational Quantum Eigensolver")
                with gr.Row():
                    with gr.Column():
                        vqe_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")
                        vqe_steps = gr.Slider(10, 1000, value=100, step=10, label="Optimization Steps")
                        vqe_button = gr.Button("Optimize VQE", variant="primary")
                    
                    with gr.Column():
                        vqe_output = gr.Textbox(label="Results", lines=10)
                        vqe_plot = gr.Image(label="Visualization")
                
                vqe_button.click(
                    interface.optimize_vqe,
                    inputs=[vqe_qubits, vqe_steps],
                    outputs=[vqe_output, vqe_plot]
                )
            
            # QAOA Tab
            with gr.TabItem("🎯 QAOA Optimization"):
                gr.Markdown("### Quantum Approximate Optimization Algorithm")
                with gr.Row():
                    with gr.Column():
                        qaoa_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")
                        qaoa_layers = gr.Slider(1, 5, value=2, step=1, label="Number of Layers")
                        qaoa_button = gr.Button("Optimize QAOA", variant="primary")
                    
                    with gr.Column():
                        qaoa_output = gr.Textbox(label="Results", lines=10)
                        qaoa_plot = gr.Image(label="Visualization")
                
                qaoa_button.click(
                    interface.optimize_qaoa,
                    inputs=[qaoa_qubits, qaoa_layers],
                    outputs=[qaoa_output, qaoa_plot]
                )
            
            # Error Mitigation Tab
            with gr.TabItem("🛡️ Error Mitigation"):
                gr.Markdown("### Quantum Error Correction")
                with gr.Row():
                    with gr.Column():
                        error_qubits = gr.Slider(2, 5, value=3, step=1, label="Number of Qubits")
                        noise_level = gr.Slider(0.0, 0.5, value=0.1, step=0.01, label="Noise Level")
                        error_button = gr.Button("Apply Error Mitigation", variant="primary")
                    
                    with gr.Column():
                        error_output = gr.Textbox(label="Results", lines=10)
                        error_plot = gr.Image(label="Visualization")
                
                error_button.click(
                    interface.demonstrate_error_mitigation,
                    inputs=[error_qubits, noise_level],
                    outputs=[error_output, error_plot]
                )
            
            # Resource Management Tab
            with gr.TabItem("⚡ Resource Management"):
                gr.Markdown("### Quantum Resource Optimization")
                with gr.Row():
                    with gr.Column():
                        num_circuits = gr.Slider(3, 20, value=10, step=1, label="Number of Circuits")
                        max_qubits = gr.Slider(2, 10, value=5, step=1, label="Max Qubits per Circuit")
                        available_qubits = gr.Slider(5, 20, value=10, step=1, label="Available Qubits")
                        resource_button = gr.Button("Optimize Resources", variant="primary")
                    
                    with gr.Column():
                        resource_output = gr.Textbox(label="Results", lines=10)
                        resource_plot = gr.Image(label="Visualization")
                
                resource_button.click(
                    interface.optimize_resources,
                    inputs=[num_circuits, max_qubits, available_qubits],
                    outputs=[resource_output, resource_plot]
                )
            
            # Hybrid Processing Tab
            with gr.TabItem("🔄 Hybrid Processing"):
                gr.Markdown("### Quantum-Classical Hybrid Algorithms")
                with gr.Row():
                    with gr.Column():
                        data_size = gr.Slider(10, 100, value=50, step=5, label="Data Size")
                        quantum_component = gr.Dropdown(
                            ["quantum_kernel", "quantum_feature_map", "quantum_neural_layer"],
                            value="quantum_kernel",
                            label="Quantum Component"
                        )
                        hybrid_button = gr.Button("Run Hybrid Processing", variant="primary")
                    
                    with gr.Column():
                        hybrid_output = gr.Textbox(label="Results", lines=10)
                        hybrid_plot = gr.Image(label="Visualization")
                
                hybrid_button.click(
                    interface.hybrid_processing_demo,
                    inputs=[data_size, quantum_component],
                    outputs=[hybrid_output, hybrid_plot]
                )
        
        gr.Markdown("""
        ---
        ### About
        This Quantum AI Agent demonstrates the integration of classical AI techniques with quantum computing algorithms.
        It showcases optimization strategies for VQE and QAOA, error mitigation using neural networks, 
        intelligent resource management, and hybrid quantum-classical processing.
        
        **Note**: This is a simulation for demonstration purposes. Real quantum hardware integration would require 
        additional components and API connections.
        """)
    
    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )