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| 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) | |
| final_summary,completed_tasts,all_tasks = main(objective, 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[visitors](https://visitor-badge.glitch.me/badge?page_id=hra/GPT4-makes-BabyAGI)""" | |
| ) | |
| 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) |