# Path: analyzer/script_entity.py
# class ScriptEntity(Model):
# 	def __init__(self, character, text, type="speech"):
# 		self.character = character
# 		self.type = type
# 		self.original_text = text
# 
# 		self.text = text.rstrip().replace('\n', ' ') if type == 'speech' else text
# 
# 	def __str__(self):
# 		return "type: {}, character: {}, text: \"{}\"".format(self.type, self.character, self.text)
# 
# 	def __repr__(self):
# 		return self.__str__()
# 
# 	@classmethod
# 	def from_dict(cls, data, from_camel=True):
# 		d = Model.from_dict(data, from_camel)
# 		return cls(d.get("character"), d.get("text"), type=d.get("type"))
# 
# 	@classmethod
# 	def from_dicts(cls, data, from_camel=True):
# 		return [cls.from_dict(d) for d in data]


import re
import urllib.request
from pprint import pprint
from bs4 import BeautifulSoup, Tag, UnicodeDammit
from analyzer.script_entity import ScriptEntity
				script_entity["original_text"] = text

	return script


def convert_to_hierarchy(project, script):
	locations = []
	speeches_in_location = []
	current_location = None
	for i, script_entity in enumerate(script):
		if script_entity["type"] == "location":
			if len(speeches_in_location) > 0:
				locations.append({"location": current_location, "speeches": speeches_in_location})
				speeches_in_location = []

			current_location = script_entity["text"]

		if script_entity["type"] == "speech":
			speeches_in_location.append(script_entity)

		if i == len(script) - 1 and len(speeches_in_location) > 0:
			locations.append({"location": current_location, "speeches": speeches_in_location})

	pprint(locations)


def run(url):
	script_text, encoding = load(url)
	parsed_entities = analyze_content(script_text, encoding)
	script = clean_script(parsed_entities)

# Path: analyzer/script_entity.py
# class ScriptEntity(Model):
# 	def __init__(self, character, text, type="speech"):
# 		self.character = character
# 		self.type = type
# 		self.original_text = text
# 
# 		self.text = text.rstrip().replace('\n', ' ') if type == 'speech' else text
# 
# 	def __str__(self):
# 		return "type: {}, character: {}, text: \"{}\"".format(self.type, self.character, self.text)
# 
# 	def __repr__(self):
# 		return self.__str__()
# 
# 	@classmethod
# 	def from_dict(cls, data, from_camel=True):
# 		d = Model.from_dict(data, from_camel)
# 		return cls(d.get("character"), d.get("text"), type=d.get("type"))
# 
# 	@classmethod
# 	def from_dicts(cls, data, from_camel=True):
# 		return [cls.from_dict(d) for d in data]


# Path: analyzer/script_parser.py
import re
import urllib.request
from pprint import pprint
from bs4 import BeautifulSoup, Tag, UnicodeDammit
from analyzer.script_entity import ScriptEntity

				script_entity["original_text"] = text

	return script


def convert_to_hierarchy(project, script):
	locations = []
	speeches_in_location = []
	current_location = None
	for i, script_entity in enumerate(script):
		if script_entity["type"] == "location":
			if len(speeches_in_location) > 0:
				locations.append({"location": current_location, "speeches": speeches_in_location})
				speeches_in_location = []

			current_location = script_entity["text"]

		if script_entity["type"] == "speech":
			speeches_in_location.append(script_entity)

		if i == len(script) - 1 and len(speeches_in_location) > 0:
			locations.append({"location": current_location, "speeches": speeches_in_location})

	pprint(locations)


def run(url):
	script_text, encoding = load(url)
	parsed_entities = analyze_content(script_text, encoding)
	script = clean_script(parsed_entities)

	entities = [ScriptEntity.from_dict(d) for d in script]