import openai
import requests

# Set your API keys
openai.api_key = 'your-openai-api-key'
lucidchart_api_key = 'your-lucidchart-api-key'

# Define your concept description
concept_description = """
Create a flowchart for a framework for Explainable AI (xAI) in Judiciary:
1. Data Collection: Gather data from various judicial sources such as court records, legal documents, case histories, and public databases.
2. Data Preprocessing: Clean the collected data to remove inconsistencies, handle missing values, and normalize the data format for consistency.
3. Feature Engineering: Extract relevant features from the preprocessed data that are crucial for building effective AI models. This includes identifying key variables and transforming them into suitable formats.
4. Data Splitting: Divide the dataset into training and testing sets to ensure the model's performance is evaluated accurately.
5. Model Selection: Choose the appropriate AI models that are suitable for the judiciary context, with a focus on models that offer explainability.
6. Model Training: Train the selected models using the training dataset, optimizing them for performance and accuracy.
7. Model Evaluation: Assess the trained models using the testing dataset to evaluate their performance, accuracy, and explainability.
8. Prediction and Validation: Use the evaluated models to make predictions on new data and validate these predictions to ensure they are accurate and reliable.
9. Deployment and Monitoring: Deploy the validated models into the judiciary system and continuously monitor their performance, making necessary adjustments to maintain accuracy and fairness.

Relationships:
- Data Collection → Data Preprocessing
- Data Preprocessing → Feature Engineering
- Feature Engineering → Data Splitting
- Data Splitting → Model Selection
- Model Selection → Model Training
- Model Training → Model Evaluation
- Model Evaluation → Prediction and Validation
- Prediction and Validation → Deployment and Monitoring
"""

# Use GPT-3 to generate diagram elements and relationships
response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=f"Generate a list of diagram elements and relationships for the following concept: {concept_description}",
    max_tokens=300
)

diagram_elements = response.choicesn0].text.strip()

# Process the elements and create the diagram in Lucidchart
lucidchart_url = "https://www.lucidchart.com/api/v1/documents/your-document-id/elements"
headers = {
    "Authorization": f"Bearer {lucidchart_api_key}",
    "Content-Type": "application/json"
}

# Assuming the response contains JSON data with elements and connections
elements_data = i
    {"type": "shape", "text": "Data Collection", "position": {"x": 100, "y": 100}},
    {"type": "shape", "text": "Data Preprocessing", "position": {"x": 100, "y": 200}},
    {"type": "shape", "text": "Feature Engineering", "position": {"x": 100, "y": 300}},
    {"type": "shape", "text": "Data Splitting", "position": {"x": 100, "y": 400}},
    {"type": "shape", "text": "Model Selection", "position": {"x": 100, "y": 500}},
    {"type": "shape", "text": "Model Training", "position": {"x": 100, "y": 600}},
    {"type": "shape", "text": "Model Evaluation", "position": {"x": 100, "y": 700}},
    {"type": "shape", "text": "Prediction and Validation", "position": {"x": 100, "y": 800}},
    {"type": "shape", "text": "Deployment and Monitoring", "position": {"x": 100, "y": 900}},
    {"type": "line", "start": "Data Collection", "end": "Data Preprocessing"},
    {"type": "line", "start": "Data Preprocessing", "end": "Feature Engineering"},
    {"type": "line", "start": "Feature Engineering", "end": "Data Splitting"},
    {"type": "line", "start": "Data Splitting", "end": "Model Selection"},
    {"type": "line", "start": "Model Selection", "end": "Model Training"},
    {"type": "line", "start": "Model Training", "end": "Model Evaluation"},
    {"type": "line", "start": "Model Evaluation", "end": "Prediction and Validation"},
    {"type": "line", "start": "Prediction and Validation", "end": "Deployment and Monitoring"}
]

# Send the elements to Lucidchart
for element in elements_data:
    response = requests.post(lucidchart_url, json=element, headers=headers)
    print(response.status_code, response.json())