import requests
import os
import json
from config import HF_API_TOKEN
from models import get_model_config

def call_hf_model(prompt, task="text_generation"):
    """
    Call Hugging Face Inference API with improved error handling and response parsing.
    """
    if not HF_API_TOKEN:
        return {"error": "API Token missing. Please set HF_API_TOKEN in .env file."}
    
    model_cfg = get_model_config(task)
    model_id = model_cfg["id"]
    api_url = f"https://api-inference.huggingface.co/models/{model_id}"
    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
    
    # Parameters to ensure better text generation
    payload = {
        "inputs": prompt,
        "parameters": {
            "max_new_tokens": 800,
            "temperature": 0.7,
            "top_p": 0.9,
            "return_full_text": False
        }
    }
    
    try:
        response = requests.post(api_url, headers=headers, json=payload, timeout=30)
        response.raise_for_status()
        result = response.json()
        
        # Handle different response formats from HF API
        if isinstance(result, list) and len(result) > 0:
            if "generated_text" in result[0]:
                return result[0]["generated_text"].strip()
            return result[0]
        return result
    except requests.exceptions.HTTPError as e:
        if response.status_code == 503:
            return {"error": "Model is currently loading. Please try again in a few seconds."}
        return {"error": f"HTTP Error: {str(e)}"}
    except Exception as e:
        return {"error": f"Connection Error: {str(e)}"}

def generate_educational_content(prompt_type, **kwargs):
    """
    Helper to generate content based on prompts.json templates.
    """
    try:
        # Get absolute path to data/prompts.json
        base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        prompts_path = os.path.join(base_dir, "data", "prompts.json")
        
        with open(prompts_path, "r", encoding="utf-8") as f:
            prompts = json.load(f)
        
        template = prompts.get(prompt_type, "")
        formatted_prompt = template.format(**kwargs)
        
        return call_hf_model(formatted_prompt)
    except Exception as e:
        return {"error": f"Prompt Error: {str(e)}"}