app.py

import gradio as gr

# This may be in the wrong spot
def generate_outputs(user_prompt):
    report, recommendations, visualization = produce_outputs(combined_data)
    return report, recommendations, visualization

from langchain_openai.agents.agent_types 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 was here but was folded into Gradio interface lines


# 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
message = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"

# Agents Group Chat Finite State Machine
class GroupChatFSM:
    def __init__(self, teams_config):
        """
        Initialize with configurations for teams.
        """
        self.teams = {team_name: self.Team(team_agents) for team_name, team_agents in teams_config.items()}
        self.states = ["waiting", "interacting", "finalizing"]
        self.current_state = "waiting"

    def transition(self, to_state):
        """
        Transition the state of the group chat based on FSM rules.
        """
        if to_state in self.states:
            self.current_state = to_state
        else:
            raise ValueError("Invalid state transition attempted.")

    def broadcast(self, message):
        """
        Broadcast a message to all teams based on the current FSM state.
        """
        if self.current_state == "interacting":
            responses = {team_name: team.broadcast(message) for team_name, team in self.teams.items()}
            return responses
        else:
            return "The group chat is not in an interacting state."

    class Team:
        def __init__(self, agents_config):
            self.agents = [self.Agent(agent_config) for agent_config in agents_config]

        def broadcast(self, message):
            responses = [agent.respond(message) for agent in self.agents]
            return responses

        class Agent:
            def __init__(self, config):
                self.agent_name = config['agent_name']
                self.api_key = config['api_key']
                self.model = config['model']

            def respond(self, message):
                return f"{self.agent_name} responding with {self.model}"

# 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 for testing pyrposes
print("Report:")
print(report)
print("\nRecommendations:")
print(recommendations)
print("\nVisualization:")
print(visualization)  # Currently "None" due to placeholder 'visualization'

# Moved from other gradio chunk near the top
gr.Interface(fn=generate_outputs, inputs=user_prompt, outputs=["text", "text", "image"]).launch()