IG Post Maker

import asyncio
import os
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.teams import DiGraphBuilder, GraphFlow
from autogen_agentchat.messages import TextMessage
from autogen_ext.models.ollama import OllamaChatCompletionClient

async def main():
    # Configure the Ollama client with model settings.
    ollama_config = {
        "model": "phi4:latest",  # Default model; change as needed.
        # Uncomment and set these if your setup requires:
        # "host": "http://localhost:11434",
        # "timeout": 120,
    }
    
    client_params = {"model": ollama_config["model"]}
    if "host" in ollama_config and ollama_config["host"]:
        client_params["host"] = ollama_config["host"]
    if "timeout" in ollama_config and ollama_config["timeout"]:
        client_params["timeout"] = ollama_config["timeout"]
    model_client = OllamaChatCompletionClient(**client_params)
    
    # Define agents for each stage of post generation:
    topic_expander_agent = AssistantAgent(
        name="TopicExpanderAgent",
        model_client=model_client,
        system_message="""You are a helpful AI assistant specialized in topic expansion.
Given a general topic, break it down into 3-5 more specific subtopics. Output them as a numbered list."""
    )
    
    planner_agent = AssistantAgent(
        name="PlannerAgent",
        model_client=model_client,
        system_message="""Your role is to plan the Instagram post content.
Define between 3 and 5 slide themes with concise key messages for each."""
    )
    
    slide_generator_agent = AssistantAgent(
        name="SlideGeneratorAgent",
        model_client=model_client,
        system_message="""You will be provided a list of themes.
For each theme, write a single compelling sentence to be used as an Instagram slide caption."""
    )
    
    description_generator_agent = AssistantAgent(
        name="DescriptionGeneratorAgent",
        model_client=model_client,
        system_message="""Given the slide sentences, generate an engaging post description with hashtags and a call-to-action.
Also, re-list the slide content at the end."""
    )
    
    compiler_agent = AssistantAgent(
        name="CompilerAgent",
        model_client=model_client,
        system_message="""Format the complete Instagram post by listing slides under '--- SLIDES ---'
and then the post description along with hashtags under '--- DESCRIPTION ---'."""
    )
    
    user_proxy_agent = UserProxyAgent(name="UserProxyAgent")
    
    # Retrieve main topic from input.
    main_topic = input("Enter the main topic for your Instagram posts: ")
    if not main_topic:
        main_topic = "The future of renewable energy"
        print(f"No topic entered, using default: {main_topic}")
        
    print("\nExpanding topic into subtopics...\n")
    
    # Expand topic and process each subtopic.
    expansion_builder = DiGraphBuilder()
    expansion_builder.add_node(topic_expander_agent)
    expansion_builder.set_entry_point(topic_expander_agent)
    expansion_graph = expansion_builder.build()
    expansion_flow = GraphFlow(
        participants=expansion_builder.get_participants(),
        graph=expansion_graph
    )
    
    expansion_result = await expansion_flow.run(task=main_topic)
    subtopics = []
    if expansion_result and expansion_result.messages:
        for message in reversed(expansion_result.messages):
            if hasattr(message, 'source') and message.source == topic_expander_agent.name and hasattr(message, 'content'):
                content_lines = message.content.strip().split('\n')
                for line in content_lines:
                    if line.strip() and any(line.strip().startswith(str(i) + '.') for i in range(1, 10)):
                        subtopic = line.strip().split('.', 1)[1].strip()
                        subtopics.append(subtopic)
                break
    if not subtopics:
        print("No subtopics could be extracted. Using main topic instead.")
        subtopics = [main_topic]
        
    print(f"\nGenerated {len(subtopics)} subtopic(s):")
    for i, subtopic in enumerate(subtopics, 1):
        print(f"{i}. {subtopic}")
    
    # Process each subtopic using a content flow.
    output_dir = "instagram_posts"
    os.makedirs(output_dir, exist_ok=True)
    
    for subtopic in subtopics:
        print(f"\nGenerating Instagram post for subtopic: {subtopic}...\n")
        content_builder = DiGraphBuilder()
        content_builder.add_node(planner_agent)
        content_builder.add_node(slide_generator_agent)
        content_builder.add_node(description_generator_agent)
        content_builder.add_node(compiler_agent)
        
        # Define agent communication flow.
        content_builder.add_edge(planner_agent, slide_generator_agent)
        content_builder.add_edge(slide_generator_agent, description_generator_agent)
        content_builder.add_edge(description_generator_agent, compiler_agent)
        content_builder.set_entry_point(planner_agent)
        
        content_graph = content_builder.build()
        content_flow = GraphFlow(
            participants=content_builder.get_participants(),
            graph=content_graph
        )
        final_response_task_result = await content_flow.run(task=subtopic)
        
        output_content = None
        if final_response_task_result and final_response_task_result.messages:
            for message in reversed(final_response_task_result.messages):
                if hasattr(message, 'source') and message.source == compiler_agent.name and hasattr(message, 'content'):
                    output_content = str(message.content)
                    break
            if output_content:
                print("\n--- INSTAGRAM POST CONTENT ---")
                print(output_content)
                print("--- END OF POST ---\n")
                
                try:
                    safe_topic_chars = [c for c in subtopic if c.isalnum() or c == ' ']
                    topic_text = "".join(safe_topic_chars).strip()
                    words = topic_text.split()
                    short_topic = " ".join(words[:5])
                    safe_topic = short_topic.replace(' ', '_').lower()
                    filename = f"{output_dir}/{safe_topic}_instagram_post.txt"
                    with open(filename, "w", encoding="utf-8") as f:
                        f.write(output_content)
                    print(f"Output saved to: {filename}")
                except Exception as e:
                    print(f"Error saving output: {e}")
            else:
                print("No valid content from CompilerAgent. Check debugging logs below:")
                for i, msg in enumerate(final_response_task_result.messages):
                    source_name = getattr(msg, 'source', 'N/A')
                    msg_type = type(msg).__name__
                    msg_content = getattr(msg, 'content', '<NO CONTENT>')
                    print(f"Message {i}: Source='{source_name}', Type='{msg_type}', Content='{msg_content}'")
        else:
            print("No response from the graph execution.")
    
    print(f"All Instagram posts have been generated and saved in '{output_dir}'.")
    await model_client.close()

if __name__ == "__main__":
    asyncio.run(main())