gistfile1.txt

import aiosqlite
import asyncio
from datetime import datetime
from markdown2 import markdown
from kivymd.app import MDApp
from kivymd.uix.screen import MDScreen
from kivymd.uix.button import MDRaisedButton
from kivymd.uix.boxlayout import MDBoxLayout
from kivy.uix.image import AsyncImage
from kivy.clock import Clock
from kivymd.uix.textfield import MDTextField
from kivy.uix.label import Label
import numpy as np
import pennylane as qml
import requests
import json
import httpx
import base64
import logging

DATABASE_NAME = "mental_health.db"
CONFIG_FILE = 'configopenai.json'

with open(CONFIG_FILE, 'r') as f:
    config = json.load(f)

openai_api_key = config['openai_api_key']
stable_url = config['stable_url']
logging.basicConfig(level=logging.INFO)

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

@qml.qnode(dev)
def quantum_circuit(color_code, datetime_factor):
    r, g, b = [int(color_code[i:i+2], 16) for i in (1, 3, 5)]
    r, g, b = r / 255.0, g / 255.0, b / 255.0
    qml.RY(r * np.pi, wires=0)
    qml.RY(g * np.pi, wires=1)
    qml.RY(b * np.pi, wires=2)
    qml.RY(datetime_factor * np.pi, wires=3)
    qml.CNOT(wires=[0, 1])
    qml.CNOT(wires=[1, 2])
    qml.CNOT(wires=[2, 3])
    return qml.state()

def mixed_state_to_color_code(mixed_state):
    mixed_state = np.array(mixed_state)
    probabilities = np.abs(mixed_state)**2
    probabilities /= np.sum(probabilities)
    r_prob = probabilities[:len(probabilities)//3]
    g_prob = probabilities[len(probabilities)//3:2*len(probabilities)//3]
    b_prob = probabilities[2*len(probabilities)//3:]
    r = int(np.sum(r_prob) * 255)
    g = int(np.sum(g_prob) * 255)
    b = int(np.sum(b_prob) * 255)
    return f'#{r:02x}{g:02x}{b:02x}'

async def initialize_database():
    async with aiosqlite.connect(DATABASE_NAME) as db:
        await db.execute('''CREATE TABLE IF NOT EXISTS mood_entries (
                            id INTEGER PRIMARY KEY,
                            mood_text TEXT,
                            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
        await db.commit()

class QuantumImageApp(MDApp):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.theme_cls.theme_style = "Dark"
        self.theme_cls.primary_palette = "BlueGray"
        self.root = MDScreen()
        self.image_display = AsyncImage(source="")
        self.create_gui()

    def create_gui(self):
        self.layout = MDBoxLayout(orientation="vertical", md_bg_color=[0, 0, 0, 1])
        self.text_box = MDTextField(hint_text="Enter your mood", hint_text_color=[1, 1, 1, 1])
        self.journal_text_box = MDTextField(hint_text="Enter journal text", hint_text_color=[1, 1, 1, 1], multiline=True)
        self.checkout_time_picker = MDTextField(hint_text="Enter checkout time (YYYY-MM-DD HH:MM)", hint_text_color=[1, 1, 1, 1])
        run_button = MDRaisedButton(text="Generate Visual", on_press=lambda x: asyncio.run(self.generate_visual(x)), text_color=[1, 1, 1, 1])
        
        self.image_display = AsyncImage(source="", allow_stretch=True, keep_ratio=True)
        self.image_display.size_hint_y = None
        self.image_display.height = 0

        self.layout.add_widget(self.text_box)
        self.layout.add_widget(self.journal_text_box)
        self.layout.add_widget(self.checkout_time_picker)
        self.layout.add_widget(run_button)
        self.layout.add_widget(self.image_display)
        self.root.add_widget(self.layout)

    async def generate_visual(self, instance):
        mood_text = self.text_box.text
        journal_text = self.journal_text_box.text
        checkout_time_str = self.checkout_time_picker.text
        await asyncio.ensure_future(self.process_mood_journal_and_time(mood_text, journal_text, checkout_time_str))

    async def process_mood_journal_and_time(self, mood_text, journal_text, checkout_time_str):
        await self.save_mood_entry(mood_text)
        gpt4_analysis = await self.analyze_journal_and_provide_trading_advice(journal_text, mood_text)
        mood_fluctuation_report = await self.analyze_mood_fluctuations()
        markdown_report = self.generate_markdown_report(gpt4_analysis, mood_fluctuation_report)
        Clock.schedule_once(lambda dt: self.display_markdown_report(markdown_report))
        color_code, datetime_factor = await self.map_sentiment_to_color(mood_text)
        quantum_state = quantum_circuit(color_code, datetime_factor)
        image_path = await self.generate_image_from_quantum_data(quantum_state)
        if image_path:
            self.update_image(image_path)

    async def save_mood_entry(self, mood_text):
        async with aiosqlite.connect(DATABASE_NAME) as db:
            await db.execute('''INSERT INTO mood_entries (mood_text) VALUES (?)''', (mood_text,))
            await db.commit()

    async def analyze_journal_and_provide_trading_advice(self, journal_text, mood_text):
        prompt = (
            f"Consider the following journal entry and the user's mood: '{mood_text}'.\n\n"
            "Journal Entry:\n"
            f"{journal_text}\n\n"
            "Based on this, provide a detailed analysis of the user's emotional state over time. "
            "Include insights into any patterns or changes in sentiment, potential reasons behind these changes, "
            "and suggestions for topics the user might find beneficial to explore in future journal entries. "
            "Additionally, based on the user's emotional patterns and interests, suggest ideas or strategies "
            "that could improve their capability to trade in economic markets, focusing on areas such as risk management, "
            "market analysis, and decision-making under pressure."
        )

        async with httpx.AsyncClient() as client:
            response = await client.post(
                "https://api.openai.com/v1/chat/completions",
                headers={"Authorization": f"Bearer {openai_api_key}"},
                json={
                    "model": "gpt-4",
                    "messages": [{"role": "system", "content": prompt}]
                }
            )
            response.raise_for_status()
            result = response.json()
            return result['choices'][0]['message']['content'].strip()

    def generate_markdown_report(self, gpt4_analysis, mood_fluctuation_report):
        report = f"""
        # Comprehensive Emotional Analysis Report

        ## GPT-4 Sentiment Analysis
        {gpt4_analysis}

        ## Mood Fluctuation Overview
        {mood_fluctuation_report}

        ### Insights and Recommendations
        Based on the analysis above, it is evident that the user's emotional state shows [specific patterns/changes]. 
        It may be beneficial for the user to explore topics such as [suggested topics] in their future journaling 
        to further understand and manage their emotional well-being.

        _This report is generated based on advanced AI analysis of journal entries and mood data._
        """
        return markdown(report)

    def display_markdown_report(self, markdown_report):
        report_label = Label(text=markdown_report, markup=True)
        self.layout.add_widget(report_label)

    async def map_sentiment_to_color(self, sentiment):
        async with httpx.AsyncClient() as client:
            response = await client.post(
                "https://api.openai.com/v1/chat/completions",
                headers={"Authorization": f"Bearer {openai_api_key}"},
                json={
                    "model": "gpt-4",
                    "messages": [
                        {"role": "system", "content": "Suggest a color based on the following sentiment."},
                        {"role": "user", "content": sentiment}
                    ]
                }
            )
            response.raise_for_status()
            result = response.json()
            color_suggestion = result['choices'][0]['message']['content'].strip()
            return color_suggestion if color_suggestion else "#808080", 1

    async def generate_image_from_quantum_data(self, quantum_state):
        color_code = mixed_state_to_color_code(quantum_state)
        prompt = f"Generate an image with predominant color {color_code}"
        url = stable_url
        payload = {
            "prompt": prompt,
            "steps": 121,
            "seed": random.randrange(sys.maxsize),
            "enable_hr": "false",
            "denoising_strength": "0.7",
            "cfg_scale": "7",
            "width": 666,
            "height": 456,
            "restore_faces": "true",
        }
        response = requests.post(url, json=payload)
        response.raise_for_status()
        r = response.json()

        if 'images' in r and r['images']:
            base64_data = r['images'][0]
            image_bytes = base64.b64decode(base64_data)
            image_path = f"output_{random.randint(0, 10000)}.png"
            with open(image_path, "wb") as image_file:
                image_file.write(image_bytes)
            return image_path
        else:
            return None

    def update_image(self, image_path):
        if image_path and os.path.exists(image_path):
            self.image_display.source = image_path
            self.image_display.size_hint_y = 1
            self.image_display.height = 200
        else:
            self.image_display.source = ""
            self.image_display.size_hint_y = None
            self.image_display.height = 0

if __name__ == "__main__":
    asyncio.run(initialize_database())
    QuantumImageApp().run()