app.py

import gradio as gr
import openai
import requests
import json
import os
from duckduckgo_search import ddg
import datetime
from datetime import datetime, date, time, timedelta


def search_duckduckgo(query):

    keywords = query
    results = ddg(keywords, region='wt-wt', safesearch='Off', time='m')
    filtered_results = [{"title": res["title"], "body": res["body"]} for res in results]
    print(filtered_results)
    return filtered_results

def get_search_query(task):
    
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content": f"Given the task: {task}. Generate a concise search query with 1-3 keywords."}
      ]
    )
    search_query = response.choices[0]['message']['content'].strip()
    print("Agent 2: ",search_query)
    return search_query

def summarize_search_result(task, search_result):
    
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content":f"Given the task '{task}' and the search result '{json.dumps(search_result)}', provide a summarized result."}
      ]
    )
    summary = response.choices[0]['message']['content'].strip()
    return summary

def agent_1(objective):
    
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content":f"Given the objective '{objective}', create a list of tasks that are closely related to the objective. If needed add specific key words from objective to the task sentence"}
      ]
    )
    tasks = response.choices[0]['message']['content'].strip().split('\n')
    return tasks

def agent_2(task):
    
    search_query = get_search_query(task)
    print("Agent 2")
    print(search_query)
    search_results = search_duckduckgo(search_query)
    summarized_result = summarize_search_result(task, search_results)
    print(summarized_result)
    return summarized_result

def agent_3(objective, last_result, tasks):
    
    task_list = '\n'.join(tasks)
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content":f"Given the objective '{objective}', the last result '{json.dumps(last_result)}', and the task list:\n{task_list}\n\nRank the tasks based on their relevance to the objective."}
      ]
    )
    modified_tasks = response.choices[0]['message']['content'].strip().split('\n')
    print("Agent 3")
    print(modified_tasks)
    return modified_tasks

def summarize_result(objective, result):
    
    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content":f"Given the objective '{objective}' and the final result '{json.dumps(result)}', provide a summary."}
      ]
    )
    summary = response.choices[0]['message']['content'].strip()
    return summary

def generate_final_answer(objective, all_results):

    response = openai.ChatCompletion.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content":f"Given the objective '{objective}' and the collected results '{json.dumps(all_results)}', provide a final answer addressing the objective."}
      ]
    )
    final_answer = response.choices[0]['message']['content'].strip()
    return final_answer

def main(objective, loop_count):
    tasks = agent_1(objective)
    all_results = []
    completed_tasks = []
    for i in range(loop_count):
        print(i+1)
        if i < len(tasks):
            print('NEXT TASK: ',tasks[i])
            completed_tasks.append(tasks[i])
            result = agent_2(tasks[i])
            all_results.append(result)
            tasks = agent_3(objective, result, tasks)
            print('*********************')
        else:
            break

    final_answer = generate_final_answer(objective, all_results)
    return final_answer,completed_tasks,tasks

def getbabyagianswer(objective,loop_count,openapikey):
    dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p")
    print(objective)
    print(dateforfilesave)
    
    if openapikey=='':
        return ["Please provide OpenAPI Key","Please provide OpenAPI Key","Please provide OpenAPI Key"]
    
    os.environ['OPENAI_API_KEY'] = str(openapikey)
    openai.api_key=str(openapikey)
    
    final_summary,completed_tasts,all_tasks = main(objective, int(loop_count))
    print("Final Summary:", final_summary)
    return final_summary,completed_tasts,all_tasks


with gr.Blocks() as demo:
    gr.Markdown("<h1><center>GPT4 created BabyAGI</center></h1>")
    gr.Markdown(
        """ This is part of a series of experiments using BabyAGI as a "framework" to construct focused use cases (ex: idea generation). In this GPT-4 was prompted to create a BabyAGI with task creation & execution agents but with constraints to give answer with a specified number of loops. Unlike the original BabyAGI concept, this is not open-ended. \n\nNote: This is a series of experiments to understand AI agents and hence do check the quality of output. OpenAI agents (gpt-3.5-turbo) & DuckDuckGo search are used. The analysis takes roughly 120 secs & may not always be consistent. An error occurs when the OpenAI Api key is not provided/ ChatGPT API is overloaded/ ChatGPT is unable to correctly decipher & format the output\n\n"""
        )
    
    with gr.Row() as row:
        with gr.Column():
            textboxtopic = gr.Textbox(placeholder="Enter Objective/ Goal...", lines=1,label='Objective')
        with gr.Column():
            textboxloopcount = gr.Textbox(placeholder="Enter # of loops...", lines=1,label='Loop Count')
        with gr.Column():
            textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
    with gr.Row() as row:
        examples = gr.Examples(examples=['Give me a startup idea in healthcare technology for India','Which is a must see destination in Mysore?','Find me a unique cuisine restaurant in bangalore','Give me a startup idea for AI in music streaming'],
                           inputs=[textboxtopic])
    with gr.Row() as row:
        btn = gr.Button("Unleash AI Agent")
        
    with gr.Row() as row:
        with gr.Column():
            answer1 = gr.Textbox(placeholder="", lines=1,label='Answer')
        with gr.Column():
            fulltasklist1 = gr.Textbox(placeholder="", lines=1,label='Full Task List')
        with gr.Column():
            completedtasklist1 = gr.Textbox(placeholder="", lines=1,label='Completed Tasks')
    
    btn.click(getbabyagianswer, inputs=[textboxtopic,textboxloopcount,textboxopenapi,],outputs=[answer1,fulltasklist1,completedtasklist1])
    
demo.launch(debug=True)