import instructor

from pydantic import BaseModel, Field
from typing import List
from graphviz import Digraph

class Node(BaseModel, frozen=True):
    """
    Node representing concept in the subject domain
    """
    id: int
    label: str = Field(..., description = "description of the concept in the subject domain")
    color: str

class Edge(BaseModel, frozen=True):
    """
    Edge representing relationship between concepts in the subject domain
    """
    source: int = Field(..., description = "source representing concept in the subject domain")
    target: int = Field(..., description = "target representing concept in the subject domain")
    label: str = Field(..., description = "description representing relationship between concepts in the subject domain")
    color: str = "black"

class KnowledgeGraph(BaseModel):
    """
    graph representation of concepts in the subject domain
    """
    nodes: List[Node] = Field(..., default_factory=list)
    edges: List[Edge] = Field(..., default_factory=list)

from groq import Groq
import os

# Initialize with API key
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# Enable instructor patches for Groq client
client = instructor.from_groq(client)
llm='llama-3.1-8b-instant' #"llama3.2", #
"""
from openai import OpenAI
client = instructor.from_openai(
    OpenAI(
        base_url="http://localhost:11434/v1",
        api_key="ollama",
    ),
    mode=instructor.Mode.JSON,
)
"""
def generate_graph(q, input) -> KnowledgeGraph:
    return client.chat.completions.create(
    model=llm,
    max_retries=5,
    messages=[
        {
            "role": "user",
            "content": f"Help me understand the following by describing it as a detailed knowledge graph: ### Question: {q} ### Context: {input}",
        }
    ],
    response_model=KnowledgeGraph,
)


class Issue(BaseModel):
    "Break down Issue as sub issues"
    question: str

class IssuePlan(BaseModel):
    "List of Issue"
    issue_graph: List[Issue] = []


def expandIssue(input) -> IssuePlan:
    return client.chat.completions.create(
        model=llm,
        max_retries=10,
        messages=[
            {
                "role": "system",
                "content": "As a Mckinsey Consultant create a framework that relevant to the topic, list all issues.",
            },             {
                "role": "user",
                "content": f"Question: {input}",
            },
        ],
        response_model=IssuePlan,
    )
def graph(query):
    queryx = expandIssue(query)
    ctx = ", ".join([q.question for q in queryx.issue_graph])
    graph = generate_graph(query, ctx)
    return graph.json()