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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from dotenv import load_dotenv
from camel.models import ModelFactory
from camel.toolkits import (
ExcelToolkit,
SearchToolkit,
FileWriteToolkit,
CodeExecutionToolkit,
BrowserToolkit,
VideoAnalysisToolkit,
ImageAnalysisToolkit,
)
from camel.types import ModelPlatformType, ModelType
from camel.societies import RolePlaying
from camel.logger import set_log_level
from owl.utils import run_society, DocumentProcessingToolkit
import pathlib
# Set the log level to DEBUG for detailed debugging information
set_log_level(level="DEBUG")
# Get the parent directory of the current file and construct the path to the .env file
base_dir = pathlib.Path(__file__).parent.parent
env_path = base_dir / "owl" / ".env"
load_dotenv(dotenv_path=str(env_path))
def get_user_input(prompt):
# Get user input and strip leading/trailing whitespace
return input(prompt).strip()
def get_construct_params() -> dict[str, any]:
# Welcome message
print("Welcome to owl! Have fun!")
# Select model platform type
model_platforms = ModelPlatformType
print("Please select the model platform type:")
for i, platform in enumerate(model_platforms, 1):
print(f"{i}. {platform}")
model_platform_choice = int(
get_user_input("Please enter the model platform number:")
)
selected_model_platform = list(model_platforms)[model_platform_choice - 1]
print(f"The model platform you selected is: {selected_model_platform}")
# Select model type
models = ModelType
print("Please select the model type:")
for i, model in enumerate(models, 1):
print(f"{i}. {model}")
model_choice = int(get_user_input("Please enter the model number:"))
selected_model = list(models)[model_choice - 1]
print(f"The model you selected is: {selected_model}")
# Select language
languages = ["English", "Chinese"]
print("Please select the language:")
for i, lang in enumerate(languages, 1):
print(f"{i}. {lang}")
language_choice = int(get_user_input("Please enter the language number:"))
selected_language = languages[language_choice - 1]
print(f"The language you selected is: {selected_language}")
# Enter the question
question = get_user_input("Please enter your question:")
print(f"Your question is: {question}")
return {
"language": selected_language,
"model_type": selected_model,
"model_platform": selected_model_platform,
"question": question,
}
def construct_society() -> RolePlaying:
# Get user input parameters
params = get_construct_params()
question = params["question"]
selected_model_type = params["model_type"]
selected_model_platform = params["model_platform"]
selected_language = params["language"]
# Create model instances for different roles
models = {
"user": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"assistant": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"browsing": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"planning": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"video": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"image": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
"document": ModelFactory.create(
model_platform=selected_model_platform,
model_type=selected_model_type,
model_config_dict={"temperature": 0},
),
}
# Configure toolkits
tools = [
*BrowserToolkit(
headless=False,
web_agent_model=models["browsing"],
planning_agent_model=models["planning"],
).get_tools(),
*VideoAnalysisToolkit(model=models["video"]).get_tools(),
*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
*ImageAnalysisToolkit(model=models["image"]).get_tools(),
SearchToolkit().search_duckduckgo,
SearchToolkit().search_google,
SearchToolkit().search_wiki,
SearchToolkit().search_baidu,
SearchToolkit().search_bing,
*ExcelToolkit().get_tools(),
*DocumentProcessingToolkit(model=models["document"]).get_tools(),
*FileWriteToolkit(output_dir="./").get_tools(),
]
# Configure agent roles and parameters
user_agent_kwargs = {"model": models["user"]}
assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
# Configure task parameters
task_kwargs = {
"task_prompt": question,
"with_task_specify": False,
}
# Create and return the society
society = RolePlaying(
**task_kwargs,
user_role_name="user",
user_agent_kwargs=user_agent_kwargs,
assistant_role_name="assistant",
assistant_agent_kwargs=assistant_agent_kwargs,
output_language=selected_language,
)
return society
def main():
# Construct the society
society = construct_society()
# Run the society and get the answer, chat history, and token count
answer, chat_history, token_count = run_society(society)
# Print the answer
print(f"\033[94mAnswer: {answer}\033[0m")
if __name__ == "__main__":
main()
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