from pathlib import Path

from langchain.llms import Replicate
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.utilities import SQLDatabase

# make sure to set REPLICATE_API_TOKEN in your environment
# use llama-2-13b model in replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d"  # noqa: E501
llm = Replicate(
    model=replicate_id,
    model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
)

db_path = Path(__file__).parent / "nba_roster.db"
rel = db_path.relative_to(Path.cwd())
db_string = f"sqlite:///{rel}"
db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0)


def get_schema(_):
    return db.get_table_info()


def run_query(query):
    return db.run(query)


template_query = """Based on the table schema below, write a SQL query that would answer the user's question:
{schema}

Question: {question}
SQL Query:"""  # noqa: E501
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "Given an input question, convert it to a SQL query. No pre-amble."),
        ("human", template_query),
    ]
)

sql_response = (
    RunnablePassthrough.assign(schema=get_schema)
    | prompt
    | llm.bind(stop=["\nSQLResult:"])
    | StrOutputParser()
)

template_response = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
{schema}

Question: {question}
SQL Query: {query}
SQL Response: {response}"""  # noqa: E501

prompt_response = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "Given an input question and SQL response, convert it to a natural "
            "language answer. No pre-amble.",
        ),
        ("human", template_response),
    ]
)


# Supply the input types to the prompt
class InputType(BaseModel):
    question: str


chain = (
    RunnablePassthrough.assign(query=sql_response).with_types(input_type=InputType)
    | RunnablePassthrough.assign(
        schema=get_schema,
        response=lambda x: db.run(x["query"]),
    )
    | prompt_response
    | llm
)
