from pybrain.tools.shortcuts import buildNetwork

import transcript

import scdb

import simplejson

import scores

import re

from os import listdir

from os.path import isfile, join

mypath = "transcripts"

files = ["transcripts/" + f for f in listdir(mypath) if isfile(join(mypath, f))]

net = buildNetwork(775, 9, 1)

for file in files:

print "Now processing " + file

slash = file.find('/')

end_docket = file.find('_')

if end_docket == -1:

end_docket = file.find(".pdf")

questionnumber = re.search(r"q\d", file)

if questionnumber:

end_docket = questionnumber.start()

docket_number = file[slash+1:end_docket]

print "Checking database for docket #%s" % docket_number

info = scdb.get_case_info(docket_number)

if not info:

print "Couldn't find case in SCDB. Will skip."

continue

print "Now processing transcript for ", info["caseName"]

winner = scdb.get_winning_party(docket_number)

if winner == "unclear":

print "The winner of this case is unclear. Will skip."

continue

print "Winner identified: %s" % winner

print "Decision direction: %s" % info["decisionDirection"]

the_transcript = transcript.get_transcript_from_PDF(file)

petitioners, respondents = transcript.get_petitioners_and_respondents(the_transcript)

argument = transcript.get_argument(the_transcript)

arguments_by_advocate = transcript.get_arguments_by_advocate(petitioners, respondents, argument)

inputs = []

for petitioner, argument in arguments_by_advocate["petitioner"].iteritems():

statements = transcript.get_statements_in_argument(argument, petitioner)

#scores.get_statistics_from_statements(statements)

number_of_words_per_speaker = scores.get_number_of_words_per_speaker(statements)

features = scores.get_features_from_statements(statements)

flat_features = scores.flatten_features(features)

#import matplotlib.pyplot as plt

#from mpltools import style

#style.use('ggplot')

#plt.ion()

#D = flat_features

#plt.barh(range(20), D.values()[:20], align='center')

#plt.yticks(range(20), D.keys()[:20])

#plt.draw()

#plt.clf()

normalized = scores.normalize_feature_list(flat_features)

myscores = scores.get_feature_vector(normalized)

inputs.extend(myscores)

for respondent, argument in arguments_by_advocate["respondent"].iteritems():

statements = transcript.get_statements_in_argument(argument, respondent)

#scores.get_statistics_from_statements(statements)

number_of_words_per_speaker = scores.get_number_of_words_per_speaker(statements)

features = scores.get_features_from_statements(statements)

flat_features = scores.flatten_features(features)

#import matplotlib.pyplot as plt

#from mpltools import style

#style.use('ggplot')

#plt.ion()

#D = flat_features

#plt.barh(range(20), D.values()[:20], align='center')

#plt.yticks(range(20), D.keys()[:20])

#plt.draw()

#plt.clf()

#normalized = scores.normalize_feature_list(flat_features)

myscores = scores.get_feature_vector(normalized)

inputs.extend(myscores)

f = open("scores","a")

f.write(simplejson.dumps(myscores)+"\n")

f.close()

if len(inputs) < 774:

# We were unable to match all arguments

# Not gonna help us, continue

print "Parsing error? Did not create enough features."

continue

if len(inputs) > 774:

# More than one argument per advocate

# just get the first two

inputs = inputs[:774]

print "Parsing error? Had to truncate features."

# Add final "decisionDirection" score

inputs.append(int(info["decisionDirection"]))

print "Final input vector length: %d" % len(inputs)

line_to_write = {}

line_to_write["inputs"] = inputs

line_to_write["output"] = winner

f = open("dataset", "a")

f.write(simplejson.dumps(line_to_write)+"\n")

f.close()

print inputs

print net.activate(inputs)