from langchain.llms import OpenAI
from langchain.agents import TextProcessingAgent
from dspy.agents import Agent  # Base class for custom agent
from dspy.utils import spawn_processes  # Distributed computing utility

# API key **ADD A KEY OR LOCAL LLM PATHWAY**
openai = OpenAI(api_key="KEY")


# User prompt intake
user_prompt = "What are the potential strategies to increase my online sales?"


# Synthetic data generation (using Langchain's Text-Davinci-003 model for illustration)
def generate_synthetic_data(prompt):
    response = openai.complete(prompt=prompt, engine="text-davinci-003", max_tokens=100)
    return response.choices[0].text


# Custom data processing agent (inheriting from DSPy's Agent class)  [TONIC PLEASE HELP LOL]
class DataProcessingAgent(Agent):
    def __init__(self):
        super().__init__()

    def process(self, data):
        # Implement our custom data processing logic here (e.g., feature engineering)
        processed_data = data.lower().strip()
        return processed_data


# Dynamic team composition (replace with logic for dynamic team creation)
team = [
    OpenAI(api_key="YOUR_OPENAI_API_KEY", engine="text-davinci-003"),  # LLM agent
    DataProcessingAgent(),  # Custom data processing agent
]


# Prompt and data flow refinement
combined_data = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"

#  [TONIC PLEASE HELP LOL]
for agent in team:
    combined_data = agent.process(combined_data)


# Multimedia output production (using Langchain's Text-Davinci-003 as default) because I don't know how to implement DSPy properly yet  [TONIC PLEASE HELP LOL]
def produce_outputs(processed_data):
    # Use Langchain for LLM-based analysis, recommendations, etc. Should this be updated to DSPy too? again:[TONIC PLEASE HELP LOL]
    analysis = openai.complete(prompt=f"Analyze {processed_data}", engine="text-davinci-003", max_tokens=200)
    recommendations = openai.complete(prompt=f"Recommend strategies based on {processed_data}", engine="text-davinci-003", max_tokens=100)
    # Replace with your visualization logic
    visualization = None
    return analysis.choices[0].text, recommendations.choices[0].text, visualization


# Synth data generation using DSPy's distributed computing capabilities (taken partially from DSPY documentation)
def generate_synthetic_data_distributed(prompt, num_nodes=3):
    # Spawn synthetic data generation processes across multiple nodes
    processes = [spawn_processes(generate_synthetic_data, [f"Simulate scenarios for {prompt}"]) for _ in range(num_nodes)]

    # Collect the results from each node
    synthetic_data_list = []
    for process in processes:
        synthetic_data_list.extend(process.get())

    # Combine the results and return the synthetic data
    return "\n".join(synthetic_data_list)


# Generate synthetic data using DSPy's distributed computing capabilities. Again:[TONIC PLEASE HELP LOL]
synthetic_data = generate_synthetic_data_distributed(user_prompt)


# Generate outputs
report, recommendations, visualization = produce_outputs(combined_data)


# Print the results
print("Report:")
print(report)
print("\nRecommendations:")
print(recommendations)
print("\nVisualization:")
print(visualization)  # Currently "None" due to placeholder 'visualization'