app/services/openai_service.py

# app/services/openai_service.py
from openai import OpenAI
from app.config import settings
from typing import List
import json


class OpenAIService:
    def __init__(self):
        self.client = OpenAI(
            api_key=settings.OPENAI_API_KEY
        )
        self.model = settings.OPENAI_MODEL_CHAT

    async def get_chat_response(self, prompt: str, history: List[dict] = None) -> str:
        """Generate chat response using OpenAI API."""
        messages = []

        if history:
            for msg in history:
                if msg["role"] != "system":
                    messages.append({
                        "role": msg["role"],
                        "content": msg["content"]
                    })

        messages.append({"role": "user", "content": prompt})

        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages
        )
        return response.choices[0].message.content

    async def translate_to_urdu(self, content: str) -> str:
        """Translate English content to Urdu using OpenAI API."""
        messages = [
            {
                "role": "system",
                "content": "You are a professional translator. Translate the following English text to Urdu. Maintain technical terms. Provide only the Urdu translation without any explanation or additional text."
            },
            {
                "role": "user",
                "content": content
            }
        ]

        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages
        )
        return response.choices[0].message.content

    async def personalize_content(
        self,
        content: str,
        software_level: str,
        hardware_level: str,
        learning_goals: str
    ) -> dict:
        """Personalize content based on user's background."""
        system_prompt = f"""You are an expert educational content adapter. Your task is to personalize the following content based on the user's background.

USER PROFILE:
- Software/Programming Level: {software_level}
- Hardware/Electronics Level: {hardware_level}
- Learning Goals: {learning_goals if learning_goals else 'Not specified'}

PERSONALIZATION RULES:

For Software Level:
- beginner: Add detailed explanations, use simpler terminology, break down complex concepts, provide examples
- intermediate: Maintain moderate complexity, brief explanations for advanced concepts only
- advanced: Add technical depth, skip basic explanations, use precise technical terminology

For Hardware Level:
- none: Explain all hardware concepts from scratch, use analogies
- basic: Brief hardware explanations, define technical terms
- experienced: Use technical hardware terminology without explanation

If learning goals are specified, emphasize and connect content to those objectives.

OUTPUT FORMAT:
Respond with a JSON object containing exactly two fields:
1. "personalized_content": The adapted content
2. "adjustments_made": A brief description of what changes were made

Example response format:
{{"personalized_content": "...", "adjustments_made": "..."}}"""

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": content}
        ]

        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages
        )

        result = json.loads(response.choices[0].message.content)
        return result