Create Your Own Planner with GPT4o Mini Locally

 This video is a step-by-step easy tutorial to create a generic planner with API calls and Gradio interface by using GPT4o Mini.

Code:

#pip install openai gradio

#export OPENAI_API_KEY=""

import openai

import os

import gradio as gr

client = openai.OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))

def generate_plans(user_query, n=5):

    response = client.chat.completions.create(

        model="gpt-4o-mini",

        messages=[

            {"role": "system", "content": "Plan and respond to the user query."},

            {"role": "user", "content": user_query}

        ],

        n=n,

        temperature=0.7,

        max_tokens=500,

        stop=['']

    )

    plans = [choice.message.content for choice in response.choices if choice.message.content.strip() != '']

    if not plans:

        plans = ["Plan A", "Plan B", "Plan C"]  

    return plans

def compare_plans(plan1, plan2):

    response = client.chat.completions.create(

        model="gpt-4o-mini",

        messages=[

            {"role": "system", "content": "Choose the better plan."},

            {"role": "user", "content": f"Plan 1: {plan1}\n\nPlan 2: {plan2}\n\nWhich plan is better? Respond with either '1' or '2'."}

        ],

        temperature=0.2,

        max_tokens=10

    )

    return response.choices[0].message.content.strip() if response.choices[0].message.content.strip() != '' else '1'

def evaluate_plans(plans, user_query):

    winners = plans

    while len(winners) > 1:

        next_round = []

        for i in range(0, len(winners), 2):

            if i+1 < len(winners):

                winner = winners[i] if compare_plans(winners[i], winners[i+1]) == '1' else winners[i+1]

            else:

                winner = winners[i]

            next_round.append(winner)

        winners = next_round

    return winners[0] if winners else 'No best plan found'

def generate_response(best_plan, user_query):

    response = client.chat.completions.create(

        model="gpt-4o-mini",

        messages=[

            {"role": "system", "content": "Respond to the user query based on the plan."},

            {"role": "user", "content": f"User Query: {user_query}\n\nPlan: {best_plan}\n\nGenerate a detailed response."}

        ],

        temperature=0.5,

        max_tokens=2000

    )

    return response.choices[0].message.content

def improved_ai_output(user_query, num_plans=20):

    plans = generate_plans(user_query, n=num_plans)

    best_plan = evaluate_plans(plans, user_query)

    final_response = generate_response(best_plan, user_query)

    return {

        "user_query": user_query,

        "best_plan": best_plan,

        "final_response": final_response

    }

def chat(query):

    result = improved_ai_output(query)

    return result['final_response']

interface = gr.Interface(

    fn=chat,

    inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."),

    outputs=gr.Textbox(),

    title="My Planner",

    description="Get a personalized plan as per your requirement!"

)

if __name__ == "__main__":

    interface.launch()