from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List
from groq import Groq
import pandas as pd
import os
from dotenv import load_dotenv
load_dotenv()

# Initialize FastAPI app
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",
        "http://127.0.0.1:3000"
    ],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
    # ... other parameters
)

# Load Groq API key
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
    raise EnvironmentError("GROQ_API_KEY environment variable not set.")

client = Groq(api_key=api_key)

# Load Quran data
try:
    data = pd.read_csv('quran_arabic.csv', encoding='utf-8')
    new_data = data[['verse_key', 'text_uthmani']]
    verse_dict = dict(zip(new_data['verse_key'], new_data['text_uthmani']))
except FileNotFoundError:
    raise FileNotFoundError("CSV file 'quran_arabic.csv' not found. Please check the path.")

# Request schema
class TafsirRequest(BaseModel):
    verse_key: str  # e.g., "1:2"
    verse_text: str  # e.g., "ٱلْحَمْدُ لِلَّهِ رَبِّ ٱلْعَـٰلَمِينَ"

# Build LLM prompt
def build_arabic_prompt(reference: str, text: str) -> str:
    return (
        "أنت عالم متخصص في تفسير القرآن الكريم.\n"
        "يرجى تقديم تفسير شامل ومبسط للآية التالية، مع الأخذ بعين الاعتبار رقم السورة ورقم الآية:\n\n"
        f"{reference}\t{text}\n\n"
        "اكتب التفسير باللغة العربية الفصحى وبأسلوب واضح وميسر للقارئ العام."
    )

# Call Groq LLM
def query_llm_arabic(prompt: str) -> str:
    try:
        response = client.chat.completions.create(
            model="llama3-70b-8192",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.3
        )
        return response.choices[0].message.content  
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"LLM Error: {str(e)}")

# Full tafsir workflow
def get_tafsir_from_input(verse_key: str, verse_text: str) -> dict:
    prompt = build_arabic_prompt(verse_key, verse_text)
    tafsir = query_llm_arabic(prompt)
    return {"reference": verse_key, "text": verse_text, "tafsir": tafsir}

# Route: Tafsir
@app.post("/tafsir")
async def get_tafsir(request: TafsirRequest):
    return get_tafsir_from_input(request.verse_key, request.verse_text)

# Health check
@app.get("/health")
def health_check():
    return {"status": "ok", "quran_verses_loaded": len(verse_dict)}