import pickle
import streamlit as st

model_data = pickle.load(open('gib_model.pki', 'rb'))


import math
import pickle

accepted_chars = 'abcdefghijklmnopqrstuvwxyz '

pos = dict([(char, idx) for idx, char in enumerate(accepted_chars)])


def normalize(line):
    """ Return only the subset of chars from accepted_chars.
    This helps keep the  model relatively small by ignoring punctuation, 
    infrequently symbols, etc. """
    return [c.lower() for c in line if c.lower() in accepted_chars]


def ngram(n, l):
    """ Return all n grams from l after normalizing """
    filtered = normalize(l)
    for start in range(0, len(filtered) - n + 1):
        yield ''.join(filtered[start:start + n])


def get_lines():
    datasets = ['big.txt']
    for filename in datasets:
        with open(filename) as fp:
            for line in fp:
                yield line


def avg_transition_prob(l, log_prob_mat):
    """ Return the average transition prob from l through log_prob_mat. """
    log_prob = 0.0
    transition_ct = 0
    for a, b, c in ngram(3, l):
        log_prob += log_prob_mat[pos[a]][pos[b]][pos[c]]
        transition_ct += 1
    # The exponentiation translates from log probs to probs.
    return math.exp(log_prob / (transition_ct or 1))
    # The exponentiation translates from log probs to probs.
    return math.exp(log_prob / (transition_ct or 1))

while True:
    l = st.text_area('enter a prospection message')
    model_mat = model_data['mat']
    threshold = model_data['thresh']
    st.write(avg_transition_prob(l, model_mat) > threshold)