import json

import requests
import numpy as np
from indicoio import JSON_HEADERS

def fer(api_root, image):
    """
    Given a grayscale input image of a face, returns a probability distribution over emotional state.
    Input should be in a list of list format, resizing will be attempted internally but for best 
    performance, images should be already sized at 48x48 pixels..

    Example usage:

    .. code-block:: python

       >>> from indicoio import fer
       >>> import numpy as np
       >>> face = np.zeros((48,48)).tolist()
       >>> emotions = fer(face)
       >>> emotions
	   {u'Angry': 0.6340586827229989, u'Sad': 0.1764309536057839, 
	   u'Neutral': 0.05582989039191157, u'Surprise': 0.0072685938275375344, 
	   u'Fear': 0.08523385724298838, u'Happy': 0.04117802220878012}

    :param image: The image to be analyzed.
    :type image: list of lists
    :rtype: Dictionary containing emotion probability pairs
    """
    
    data_dict = json.dumps({"face": image})
    response = requests.post(api_root + "fer", data=data_dict, headers=JSON_HEADERS)
    response_dict = response.json()
    if len(response_dict) < 2:
      raise ValueError(response_dict.values()[0])
    else:
      return response_dict