import os, sys, requests

src_dir = os.path.join(
    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
sys.path.append(src_dir)

from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH
from configs.server_config import SANDBOX_SERVER

from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
from coagent.connector.phase import BasePhase
from coagent.connector.schema import Message
import base64, openai

from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
from coagent.connector.utils import parse_section
import importlib


# update new agent configs
auto_feedback_from_code_execution_PROMPT = """#### Agent Profile

You are a helpful AI assistant. Solve tasks using your coding and language skills.
In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.
    1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
    2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
Reply "stopped" in the end when everything is done.

ATTENTION: The Action Status field ensures that the tools or code mentioned in the Action can be parsed smoothly. Please make sure not to omit the Action Status field when replying.

#### Response Output Format

**Thoughts:** Based on the question and observations above, provide the plan for executing this step.

**Action Status:** Set to 'stopped' or 'code_executing'. If it's 'stopped', the action is to provide the final answer to the original question. If it's 'code_executing', the action is to write the code.

**Action:** 
```python
# Write your code here
import os
...
```

**Observation:** Check the results and effects of the executed code.

... (Repeat this Thoughts/Action/Observation cycle as needed)

**Thoughts:** I now know the final answer

**Action Status:** stopped

**Action:** The final answer to the original input question


"""


# Design your personal PROMPT INPPUT FORMAT 
AUTO_FEEDBACK_FROM_CODE_EXECUTION_PROMPT_CONFIGS = [
    {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
    {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
    {"field_name": 'session_records', "function_name": 'handle_session_records'},
    {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
    {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
]


# set a 
AGETN_CONFIGS.update({
    "auto_feedback_from_code_execution": {
        "role": {
            "role_prompt": auto_feedback_from_code_execution_PROMPT,
            "role_type": "assistant",
            "role_name": "auto_feedback_from_code_execution",
            "role_desc": "",
            "agent_type": "ReactAgent"
        },
        "prompt_config": AUTO_FEEDBACK_FROM_CODE_EXECUTION_PROMPT_CONFIGS,
        "chat_turn": 5,
        "stop": "\n**Observation:**",
        "focus_agents": [],
        "focus_message_keys": [],
    },
})

# update new chain configs
CHAIN_CONFIGS.update({
    "auto_feedback_from_code_executionChain": {
        "chain_name": "auto_feedback_from_code_executionChain",
        "chain_type": "BaseChain",
        "agents": ["auto_feedback_from_code_execution"],
        "chat_turn": 1,
        "do_checker": False,
        "chain_prompt": ""
    }
})

# update phase configs
PHASE_CONFIGS.update({
    "auto_feedback_from_code_executionPhase": {
        "phase_name": "auto_feedback_from_code_executionPhase",
        "phase_type": "BasePhase",
        "chains": ["auto_feedback_from_code_executionChain"],
        "do_summary": False,
        "do_search": False,
        "do_doc_retrieval": False,
        "do_code_retrieval": False,
        "do_tool_retrieval": False,
        "do_using_tool": False
    },
})


llm_config = LLMConfig(
    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
    api_base_url=os.environ["API_BASE_URL"], temperature=0.3
    )

embed_config = EmbedConfig(
    embed_engine="model", embed_model="text2vec-base-chinese", 
    embed_model_path="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
    )

# 
os.environ["log_verbose"] = "1"

phase_name = "auto_feedback_from_code_executionPhase"
phase = BasePhase(
    phase_name,
    sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
    kb_root_path=KB_ROOT_PATH,
    embed_config=embed_config, llm_config=llm_config, 
    base_phase_config = PHASE_CONFIGS,
    base_chain_config = CHAIN_CONFIGS,
    base_role_config = AGETN_CONFIGS,
)


# # round-1
# query_content = "What date is today? Compare the year-to-date gain for META and TESLA."
# query = Message(
#     role_name="human", role_type="user", 
#     role_content=query_content, input_query=query_content, origin_query=query_content,
#     code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
#     )

# output_message1, _ = phase.step(query)



# round-2
query_content = """Plot a chart of META and TESLA's stock prices for the past year and save it as stock_price_ytd.png."""

query = Message(
    role_name="human", role_type="user", 
    role_content=query_content, input_query=query_content, origin_query=query_content,
    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
    )

output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))