Rough draft first pass

app.py

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+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'