app.py

import numpy as np
import random
import io
import duckdb
import gradio as gr
import math
from datetime import datetime
import PIL
import matplotlib.pyplot as plt
from PIL import Image
import pennylane as qml

# Define a device
dev = qml.device('default.qubit', wires=10)

# Hugging Face and DuckDB function placeholders
from datasets import load_dataset, Dataset

def store_in_hf_dataset(data):
    # Convert data to Hugging Face Dataset format
    dataset = Dataset.from_dict({
        'id': [item[0] for item in data],
        'hamiltonian': [item[2] for item in data],
        'qasm_code': [item[3] for item in data],
        'trotter_code': [item[4] for item in data],
        'num_qubits': [item[5] for item in data],
        'trotter_order': [item[6] for item in data],
        'timestamp': [str(item[7]) for item in data],
    })
    # Push to Hugging Face dataset hub (replace with your dataset path)
    dataset.push_to_hub("your-username/BoltzmannEntropy-QuantumLLMInstruct")

def load_from_hf_dataset():
    # Load from Hugging Face dataset
    dataset = load_dataset("your-username/BoltzmannEntropy-QuantumLLMInstruct")
    return dataset

# Function to buffer the plot and return as PIL image
def buffer_plot_and_get(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    buf.seek(0)
    return PIL.Image.open(buf)

# Store image in bytes for DuckDB
def pil_image_to_bytes(image):
    img_byte_arr = io.BytesIO()
    image.save(img_byte_arr, format='PNG')
    return img_byte_arr.getvalue()

# Function to generate a random Hamiltonian
def generate_random_hamiltonian(num_qubits):
    terms = []
    for _ in range(random.randint(1, 5)):
        coeff = round(random.uniform(-1, 1), 2)
        pauli_ops = [random.choice(['I', 'X', 'Y', 'Z']) for _ in range(num_qubits)]
        term = f"{coeff} * {' '.join(pauli_ops)}"
        terms.append(term)
    return " + ".join(terms)

# Function to convert Hamiltonian to QASM code
def hamiltonian_to_qasm(hamiltonian, num_qubits):
    qasm_code = f"OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[{num_qubits}];\n"
    rotations = {i: 0.0 for i in range(num_qubits)}
    terms = hamiltonian.split(" + ")
    
    for term in terms:
        coeff, paulis = term.split(" * ")
        paulis = paulis.split()
        coeff = float(coeff)

        for i, pauli in enumerate(paulis):
            if pauli == "X":
                qasm_code += f"x q[{i}];\n"
            elif pauli == "Y":
                qasm_code += f"ry(pi/2) q[{i}];\n"
            elif pauli == "Z":
                rotations[i] += coeff

    for i, angle in rotations.items():
        if angle != 0:
            angle_degrees = round(angle * 180 / math.pi, 2)
            qasm_code += f"rz({angle_degrees}) q[{i}];\n"
    
    return qasm_code

# Function for Trotter decomposition
def trotter_decomposition(hamiltonian, order):
    terms = hamiltonian.split(" + ")
    trotter_steps = []
    
    for term in terms:
        coeff, *pauli_ops = term.split(" * ")
        coeff = float(coeff)
        for _ in range(order):
            trotter_steps.append(f"exp({coeff / order}) * ({' * '.join(pauli_ops)})")
        for _ in range(order):
            trotter_steps.append(f"exp({-coeff / order}) * ({' * '.join(pauli_ops)})")
    
    return " + ".join(trotter_steps)

# Store data in DuckDB
def store_in_duckdb(data, db_file='quantum_hamiltonians.duckdb'):
    conn = duckdb.connect(database=db_file)
    conn.execute("""CREATE TABLE IF NOT EXISTS hamiltonians (
        id INTEGER,
        plot BLOB,
        hamiltonian VARCHAR,
        qasm_code VARCHAR,
        trotter_code VARCHAR,
        num_qubits INTEGER,
        trotter_order INTEGER,
        timestamp TIMESTAMP
    )""")
    conn.executemany("""INSERT INTO hamiltonians (id, plot, hamiltonian, qasm_code, trotter_code, num_qubits, trotter_order, timestamp)
                        VALUES (?, ?, ?, ?, ?, ?, ?, ?)""", data)
    conn.close()

# Load results from DuckDB
def load_from_duckdb(db_file='quantum_hamiltonians.duckdb'):
    conn = duckdb.connect(database=db_file)
    df = conn.execute("SELECT * FROM hamiltonians").df()
    conn.close()
    return df

# Function to generate Hamiltonians
def generate_hamiltonians(num_hamiltonians, selected_qubits, selected_order, write_to_hf, write_to_duckdb):
    results_table = []
    timestamp = datetime.now()

    for i in range(num_hamiltonians):
        num_qubits = random.choice(selected_qubits)
        order = selected_order
        hamiltonian = generate_random_hamiltonian(num_qubits)
        qasm_code = hamiltonian_to_qasm(hamiltonian, num_qubits)
        trotter_code = trotter_decomposition(hamiltonian, order)

        # Create a dummy plot (replace with actual plot creation logic)
        fig, ax = plt.subplots()
        ax.plot([0, 1], [0, 1])
        circuit_plot_image = buffer_plot_and_get(fig)
        circuit_plot_bytes = pil_image_to_bytes(circuit_plot_image)

        # Append data to results table
        results_table.append((i + 1, circuit_plot_bytes, hamiltonian, qasm_code, trotter_code, num_qubits, order, timestamp))

    # Write data to Hugging Face dataset if selected
    if write_to_hf:
        store_in_hf_dataset(results_table)
    
    # Write data to DuckDB if selected
    if write_to_duckdb:
        store_in_duckdb(results_table)

# Function to load results from either DuckDB or Hugging Face dataset
def load_results(load_from_hf_checkbox, load_from_duckdb_checkbox):
    if load_from_hf_checkbox:
        return load_from_hf_dataset()  # Load from HF dataset
    if load_from_duckdb_checkbox:
        return load_from_duckdb()  # Load from DuckDB

# Gradio app
with gr.Blocks() as app:
    gr.Markdown("# Quantum Hamiltonian Generator")
    
    with gr.Tab("Generate Hamiltonians"):
        num_hamiltonians = gr.Dropdown(label="Select number of Hamiltonians to generate", choices=[1, 10, 20, 100], value=20)
        qubit_choices = [1, 2, 3, 4, 5, 6]
        selected_qubits = gr.CheckboxGroup(label="Select number of qubits", choices=qubit_choices, value=[1])
        order_choices = [1, 2, 3, 4, 5]
        selected_order = gr.Dropdown(label="Select Trotter order", choices=order_choices, value=1)

        # Radio buttons for selecting either Hugging Face dataset or DuckDB
        write_option = gr.Radio(label="Where do you want to store the data?", 
                                choices=["Write to Hugging Face dataset", "Write to DuckDB"], 
                                value="Write to Hugging Face dataset")

        generate_button = gr.Button("Generate Hamiltonians")
        status = gr.Markdown("Click 'Generate Hamiltonians' to start the process.")

        def update_status(num, qubits, order, write_option):
            if write_option == "Write to Hugging Face dataset":
                # Call function to write to Hugging Face dataset
                generate_hamiltonians(num, qubits, order, write_to_hf=True, write_to_duckdb=False)
            else:
                # Call function to write to DuckDB
                generate_hamiltonians(num, qubits, order, write_to_hf=False, write_to_duckdb=True)
            return "Data stored as per selection."

        generate_button.click(update_status, inputs=[num_hamiltonians, selected_qubits, selected_order, write_option], outputs=status)

    with gr.Tab("View Results"):
        load_option = gr.Radio(label="Where do you want to load the data from?", 
                               choices=["Load from Hugging Face dataset", "Load from DuckDB"], 
                               value="Load from DuckDB")

        load_button = gr.Button("Load Results")
        output_display = gr.HTML()

        def load_results(load_option):
            if load_option == "Load from Hugging Face dataset":
                return load_from_hf_dataset()
            else:
                return load_from_duckdb()

        load_button.click(load_results, inputs=[load_option], outputs=output_display)

app.launch(share=True)