from langchain.schema.output_parser import StrOutputParser
from chains.utils import getToolPromptTemplate
from model import llm4
from langchain.tools import tool

step2ToolName = "Problem Deconstruction"
step2ToolContext = """\
For addressing scalable problems, utilizing the [Problem Deconstruction Canvas] can help in delving into various \
aspects of the issue such as the superficial problem, underlying causes, affected groups, and the resulting impact. \
Here are some tips:
1. Distinguishing Superficial Problems from Underlying Causes:
   - Help participants differentiate between the superficial phenomena and the underlying causes of the problem. \
Superficial problems are observable direct manifestations leading to some impact, for example, pedestrian pathways being occupied by vehicles, \
higher resume rejection rates for disabled individuals during job applications, \
or visually impaired users finding it difficult to use a particular brand's app for online orders.
   - Underlying causes focus on the root causes behind superficial problems. \
Identifying these often requires proposing hypotheses and logical multi-level deduction and research. \
For instance, the occupation of pedestrian pathways might be linked to lack of public education on traffic regulations, \
insufficient regulatory enforcement, or poor road design in specific areas.
2. Refining Broad Problems:
   - If the problem posed is relatively broad, such as when its solution spans multiple industries or fields \
and the team lacks the necessary expertise and resources to address it, consider further segmenting the problem. \
For example, the issue of gender discrimination could be narrowed down to a specific domain like the workplace, \
transforming it into "gender discrimination in the workplace."
3. Monitoring Changes:
   - Pay attention to changes over time in the underlying causes. Have they evolved? Will they continue to change in the future?

These guiding suggestions aim to facilitate a more structured and insightful exploration of the problem, \
enabling teams to delve deeper into understanding the issues at hand and how they might be addressed in a scalable and impactful manner.\
"""
step2ToolSuggestion = """\
1. This step unfolds around the [Problem Deconstruction Canvas], with each participant selecting a problem from the first step to refine and structure.
2. It's suggested to spend 5 minutes before starting to clarify with everyone the definition of each dimension on the canvas.
3. Each participant should choose a specific color of sticky note, and use the four \
dimensions divided on the canvas to provide a structured description of the selected problem within 10 minutes.\
"""
prompt = getToolPromptTemplate(step2ToolName, step2ToolContext, step2ToolSuggestion)

step2Chain = prompt | llm4 | StrOutputParser()


@tool("Problem Deconstruction")
def step2Tool(context: str) -> str:
    """Useful for find some detail advise or examples when you process on "Problem Deconstruction" step. Need to input current problem context about Problem Deconstruction."""

    return step2Chain.invoke({"current_situation": context})