Python/app.py at master · JohnpFitzgerald/Python · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 28 18:11:06 2020

@author: Jfitz
"""


import numpy as np
import pandas as pd
import preprocessor as p
from tensorflow.keras.models import load_model
import joblib
from pathlib import Path
from PIL import Image
import streamlit as st

#paths
img_path = Path.joinpath(Path.cwd(),'images')
artifacts_path = Path.joinpath(Path.cwd(),'model_artifacts')
datasets_path = Path.joinpath(Path.cwd(),'dataset')

#load images
center = Image.open(Path.joinpath(img_path,'center.jpg'))
federer_image = Image.open(Path.joinpath(img_path,'federer.jpg'))
nadal =Image.open(Path.joinpath(img_path,'Nadal.jpg'))

#load artefacts
model = load_model(Path.joinpath(artifacts_path,'model-v1.h5'))
tokenizer_t = joblib.load(Path.joinpath(artifacts_path,'tokenizer_t.pkl'))
vocab = joblib.load(Path.joinpath(artifacts_path,'vocab.pkl'))

df2 = pd.read_csv(Path.joinpath(datasets_path,'response.csv'))

def get_pred(model,encoded_input):
    pred = np.argmax(model.predict(encoded_input))
    return pred

def bot_precausion(df_input,pred):
    words = df_input.questions[0].split()
    if len([w for w in words if w in vocab])==0 :
        pred = 1
    return pred

def get_response(df2,pred):
    upper_bound = df2.groupby('labels').get_group(pred).shape[0]
    r = np.random.randint(0,upper_bound)
    responses = list(df2.groupby('labels').get_group(pred).response)
    return responses[r]

def bot_response(response,):
    return response


def botResponse(user_input,is_startup=True):
    df_input = user_input

    df_input = p.remove_stop_words_for_input(p.tokenizer,df_input,'questions')
    encoded_input = p.encode_input_text(tokenizer_t,df_input,'questions')

    pred = get_pred(model,encoded_input)
    pred = bot_precausion(df_input,pred)

    response = get_response(df2,pred)
    response = bot_response(response)

    if is_startup:
        response = "Hi, I'm happy to have you here \nI have a lot to discuss about tennis"
        is_startup = False
        return response

    else:
        return  response

def get_text():
    input_text = st.text_input("You: ","type here")
    df_input = pd.DataFrame([input_text],columns=['questions'])
    return df_input


st.sidebar.title("BoTennis")
st.title("""
BoTennis
BoTennis is an NLP conversational chatbot trainned on tennis corpus using  CNN achitecture
""")

st.image(center,width=700)
st.sidebar.image(federer_image)
st.sidebar.image(nadal,width=350)

user_input = get_text()
if str(user_input['questions']) =="type here":
    response = botResponse(user_input)
else:
    response = botResponse(user_input,is_startup=False)

st.text_area("Bot:", value=response, height=200, max_chars=None, key=None)