hope.py

import pennylane as qml
import numpy as np
import requests
import random
import sys
import io
import base64
from PIL import Image, ImageTk
import httpx
import asyncio

num_qubits = 6
dev = qml.device('default.qubit', wires=num_qubits)

@qml.qnode(dev)
def quantum_circuit(params):
    for i in range(num_qubits):
        qml.Hadamard(wires=i)
        qml.RY(params[i], wires=i)
    for i in range(num_qubits - 1):
        qml.CNOT(wires=[i, i + 1])
    return qml.state()

def mixed_state_to_color_code(mixed_state):
    norm_probs = mixed_state / np.sum(mixed_state)
    r = int(sum(norm_probs[:2]) * 255)
    g = int(sum(norm_probs[2:4]) * 255)
    b = int(sum(norm_probs[4:]) * 255)
    return f'#{r:02x}{g:02x}{b:02x}'

def generate_image_from_quantum_data(quantum_data):
    color_code = mixed_state_to_color_code(quantum_data)
    prompt = f"Generate an image with predominant color {color_code}"
    url = 'http://127.0.0.1:7860/sdapi/v1/txt2img'
    payload = {
        "prompt": prompt,
        "steps": 121,
        "seed": random.randrange(sys.maxsize),
        "enable_hr": "false",
        "denoising_strength": "0.7",
        "cfg_scale": "7",
        "width": 366,
        "height": 856,
        "restore_faces": "true",
    }
    response = requests.post(url, json=payload)
    if response.status_code == 200:
        r = response.json()
        image_data = r['images'][0]
        image = Image.open(io.BytesIO(base64.b64decode(image_data.split(",",1)[1])))
        return image
    else:
        raise Exception(f"Error generating image: {response.status_code}")

def encode_image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

async def analyze_image_with_gpt4_vision(base64_image):
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "https://api.openai.com/v1/chat/completions",
            headers={"Authorization": "Bearer YOUR_OPENAI_API_KEY"},
            json={
                "model": "gpt-4-vision-preview",
                "messages": [
                    {
                        "role": "user",
                        "content": [
                            {"type": "image", "data": base64_image}
                        ]
                    }
                ]
            }
        )
        return response.json()

async def integrated_quantum_image_analysis(params):
    quantum_state = quantum_circuit(params)
    image = generate_image_from_quantum_data(quantum_state)
    base64_image = encode_image_to_base64(image)
    analysis_result = await analyze_image_with_gpt4_vision(base64_image)
    return analysis_result

params = np.random.random(num_qubits) * np.pi
analysis_result = asyncio.run(integrated_quantum_image_analysis(params))
print(analysis_result)