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generate_prompt.py
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generate_prompt.py
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import json
import math
import os
import pprint
import random
import re
import textwrap
import google.generativeai as palm
import numpy as np
from json_files.master_task import master_tasks
from json_files.task_users import task_user_1, task_user_2
from keyconfig import gemini as palm_api
from utils import (prompt_claude, prompt_gemini, prompt_gpt,
remove_parentheses, replace_options)
random.seed(9511)
models = [
m for m in palm.list_models() if "generateText" in m.supported_generation_methods
]
model = models[0].name
f = open("./json_files/object_2.json", "r")
objects = json.load(f)
f.close()
f = open("./json_files/receptacle.json", "r")
receptacles = json.load(f)
f.close()
f = open("./json_files/task.json", "r")
task_sample_space = json.load(f)
f.close()
f = open("./json_files/sequence_1.json", "r")
sequences = json.load(f)
f.close()
f = open("./json_files/food.json", "r")
food = json.load(f)
f.close()
task_sample_space = replace_options(task_sample_space, food)
f = open("./json_files/task_resource.json", "r")
resource_mapping = json.load(f)
f.close()
f = open("./json_files/task_phrase.json", "r")
phrase_mapping = json.load(f)
f.close()
f = open("./json_files/task_resource.json", "r")
resource_mapping = json.load(f)
f.close()
with open("data/h1_corrected_fabri_1.json") as f:
household_responses = json.load(f)
# for i in range(10):
# # random_key = random.choice(list(task_sample_space.keys()))
# # task = task_sample_space[random_key]
# task = random.choice(task_user_2)
# # if "fire" in task:
# # continue
# # if "clothes" in task or "room" in task:
# # pass
# # else:
# # task = remove_parentheses(task)
# print(
# "You see the user perform the task: ",
# task,
# "\n What do you anticipate to be the next 4 tasks?",
# )
# task = "It is morning time, the user has prepared his breakfast \n You see the user perform the task: \n *serve the food (boiled eggs) (location=office table)* \n What do you anticipate to be the next 4 tasks? \n Requirement: The kitchen is very dirty\n"
# op_dict = prompt_gemini(task, user=1)
# import pdb
# resource_found = False
# for task in op_dict["tasks"]:
# if task in resource_mapping.keys():
# print(f"Resource {resource_mapping[task]} is not available")
# resource_found = True
# if not resource_found:
# print("No resource found")
# task_phrase = random.choice(list(phrase_mapping.keys()))
# print("Requirement: ", phrase_mapping[task_phrase])
# print("------------------------------------")
# pdb.set_trace()
def run_llm_expts(
llm="gemini",
):
common_ratio = list()
llm_resource_used = list()
user_resource_used = list()
for exp in range(7, 10):
scene_counter = 0
for scenes in list(household_responses.keys()):
scene_details = household_responses[scenes]["details"]
# required_task = household_responses[scenes]["required_task"]
# print("scene details: ", scene_details)
if "not_required_task" in household_responses[scenes].keys():
not_required_task = household_responses[scenes]["not_required_task"]
else:
not_required_task = None
# required_task = remove_parentheses(required_task)
if not os.path.exists(f"./llm_cache/scene_{scene_counter}"):
os.makedirs(f"./llm_cache/scene_{scene_counter}")
op_dict, convo = prompt_claude(
scene_details,
f"scene_{scene_counter}/{exp}",
icl=False,
cot=False,
user=1
)
scene_counter += 1
op_tasks = op_dict["tasks"]
op_tasks = [
(
"prepare food"
if "breakfast" in task.lower()
or "lunch" in task.lower()
or "dinner" in task.lower()
else task
)
for task in op_tasks
]
op_tasks = [
remove_parentheses(task) if "food" in task or "drink" in task else task
for task in op_tasks
]
if len(op_tasks) > 4:
op_tasks = op_tasks[:4]
# if required_task in op_tasks:
# llm_requirement_satisfied.append(1)
# else:
# llm_requirement_satisfied.append(0)
# breakpoint()
if not_required_task and not_required_task in op_tasks:
llm_resource_used.append(1)
else:
llm_resource_used.append(0)
response_users = [
key
for key in household_responses[scenes].keys()
if key.startswith("user")
]
for user in response_users:
user_tasks = household_responses[scenes][user]
user_tasks = [
(
remove_parentheses(task)
if "food" in task or "drink" in task
else task
)
for task in user_tasks
]
user_tasks = [
(
"prepare food"
if task
in ["prepare breakfast", "prepare lunch", "prepare dinner"]
else task
)
for task in user_tasks
]
if len(user_tasks) > 4:
user_tasks = user_tasks[:4]
# if required_task in user_tasks:
# user_requirement_satisfied.append(1)
# else:
# user_requirement_satisfied.append(0)
if not_required_task and not_required_task in user_tasks:
user_resource_used.append(1)
else:
user_resource_used.append(0)
print(f"User {user} tasks: ", user_tasks)
print(f"Predicted tasks: ", op_tasks)
print("Overlap: ", set(user_tasks).intersection(op_tasks))
print("-------------------------------------------------")
common_ratio.append(len(set(user_tasks).intersection(op_tasks)) / 4)
if len(set(user_tasks).intersection(op_tasks)) <= 1:
breakpoint()
# if len(set(user_tasks).intersection(op_tasks)) == 0:
print("Common tasks: ", sum(common_ratio) / len(common_ratio))
# print(
# "LLM requirement satisfied: ",
# sum(llm_requirement_satisfied) / len(llm_requirement_satisfied),
# )
# print(
# "User requirement satisfied: ",
# sum(user_requirement_satisfied) / len(user_requirement_satisfied),
# )
# print("LLM resource used: ", sum(llm_resource_used) / len(llm_resource_used))
# print("User resource used: ", sum(user_resource_used) / len(user_resource_used))
breakpoint()
def get_user_overlap():
alpha = np.zeros([11, 11, 5])
try:
for scn_id, scenes in enumerate(household_responses.keys()):
print(f"Scene {scn_id}")
if scn_id == 5:
break
# scene_details = household_responses[scenes]["details"]
response_users = list(household_responses[scenes].keys())[1:]
for user1_id, user in enumerate(response_users):
user_tasks = household_responses[scenes][user]
user_tasks = [
(
remove_parentheses(task)
if "food" in task or "drink" in task
else task
)
for task in user_tasks
]
for user2_id, user2 in enumerate(response_users):
user2_tasks = household_responses[scenes][user2]
user2_tasks = [
(
remove_parentheses(task)
if "food" in task or "drink" in task
else task
)
for task in user2_tasks
]
overlap = set(user_tasks).intersection(user2_tasks)
print(f"{user} and {user2} overlap: ", len(overlap))
alpha[user1_id][user2_id][scn_id] = len(overlap)
print("-------------------------------------------------")
except Exception as exc:
breakpoint()
print(exc)
print(np.mean(alpha, axis=-1))
print(np.mean(alpha))
breakpoint()
if __name__ == "__main__":
run_llm_expts()
# breakpoint()