from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel
from typing import Dict
from groq import Groq

# ─── CONFIG ─────────────────────────────────────────────
GROQ_API_KEY = "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"
MODEL         = "llama-3.3-70b-versatile"
# ────────────────────────────────────────────────────────

app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

class BudgetInput(BaseModel):
    salary: float
    risk_tolerance: str
    expenses: Dict[str, Dict[str, float]]

@app.get("/", response_class=HTMLResponse)
async def get_chat(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/analyze")
async def analyze(data: BudgetInput):
    try:
        total = sum(
            amt
            for category in data.expenses.values()
            for amt in category.values()
        )
        remaining = data.salary - total

        # build messages
        system_msg = {
            "role": "system",
            "content": (
                "You are a helpful financial advisor assistant "
                "Based on a user's monthly salary, categorized expenses, and risk tolerance, and don't gave them desclaimer about anything "
                "provide secure, actionable investment suggestions in a friendly tone , make sure give them concise on point and stats of it , list of where can you invest, is there any way ."
            )
        }
        lines = []
        for cat, vals in data.expenses.items():
            for name, amt in vals.items():
                lines.append(f"{name}: ₹{amt:.2f}")
        user_msg = {
            "role": "user",
            "content": (
                f"I have a monthly salary of ₹{data.salary:.2f}.\n"
                f"My expenses:\n" + "\n".join(lines) + "\n"
                f"Remaining balance: ₹{remaining:.2f}\n"
                f"My risk tolerance is '{data.risk_tolerance}'.\n"
                "Please suggest how I should invest this amount securely."
            )
        }

        client = Groq(api_key=GROQ_API_KEY)
        resp = client.chat.completions.create(
            model=MODEL,
            messages=[system_msg, user_msg],
            temperature=0.5,
            max_tokens=1024,
            top_p=1.0,
        )
        advice_md = resp.choices[0].message.content

        return JSONResponse({
            "summary": {
                "salary": data.salary,
                "total_expenses": total,
                "remaining_balance": remaining,
                "expense_breakdown": data.expenses,
            },
            "groq_advice_markdown": advice_md
        })
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))