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main.py
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# from tensorflow.keras.models import load_model
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import streamlit as st
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import random
# tf.keras.utils.disable_interactive_logging()
st.set_page_config(layout="wide")
# You can disable all debugging logs using os.environ :
# st.header('TopicPredictorAI')
target = ['BACKGROUND','CONCLUSIONS', 'METHODS', 'OBJECTIVE','RESULTS']
def preprocess(text):
df = pd.DataFrame(text,columns=['text'])
df['line_number'] = df.index
df['total_lines'] = len(text)
return df
def split_chars(text):
return " ".join(list(text))
def convartTotensor(df):
label = np.array(random.choices(target,k=len(df)))
line_number_one_hot = tf.one_hot(df['line_number'].to_numpy(),depth=15)
total_lines_one_hot = tf.one_hot(df['total_lines'].to_numpy(),depth=20)
test_sentence = df['text'].tolist()
test_sentence = [i.strip() for i in test_sentence]
test_char =[split_chars(sentence) for sentence in test_sentence]
one_hot_encoder = OneHotEncoder(sparse=False)
label_one_hot = one_hot_encoder.fit_transform(label.reshape(-1, 1))
test_dataset = tf.data.Dataset.from_tensor_slices((line_number_one_hot,
total_lines_one_hot,
test_sentence,
test_char))
random_labels = tf.data.Dataset.from_tensor_slices(label_one_hot)
test_dataset = tf.data.Dataset.zip((test_dataset,random_labels))
test_dataset=test_dataset.batch(32).prefetch(tf.data.AUTOTUNE)
return test_dataset
def prediction(test_dataset):
model = tf.keras.models.load_model("model")
prediction = model.predict(test_dataset)
predict_label = tf.argmax(prediction,axis=1)
pred_label = predict_label.numpy().tolist()
return pred_label
def printText(pred_label,test_sentence):
BACKGROUND = " "
CONCLUSIONS = " "
METHODS = " "
OBJECTIVE = " "
RESULTS = " "
l = len(test_sentence)
for i in range(l):
if pred_label[i] == 0:
BACKGROUND +="\n"+ test_sentence[i]
elif pred_label[i] == 1:
CONCLUSIONS +="\n"+ test_sentence[i]
elif pred_label[i] == 2:
METHODS +="\n"+ test_sentence[i]
elif pred_label[i] == 3:
OBJECTIVE +="\n"+ test_sentence[i]
elif pred_label[i] == 4:
RESULTS +="\n"+ test_sentence[i]
if BACKGROUND != " ":
m = f'<i><p style="font-family:Courier; color:#fc6b03; font-size: 20px;">Background</p></i><br>{BACKGROUND}'
st.markdown(m,unsafe_allow_html=True)
# st.write(f"**Background** :\n {BACKGROUND}\n")
if OBJECTIVE !=" ":
# st.write(f"**OBJECTIVE** :\n {OBJECTIVE}\n")
m = f'<i><p style="font-family:Courier; color:#fc6b03; font-size: 20px;">OBJECTIVE</p></i><br>{OBJECTIVE}'
st.markdown(m,unsafe_allow_html=True)
if METHODS !=" ":
# st.write(f"**METHODS** :\n {METHODS}\n")
m = f'<i><p style="font-family:Courier; color:#fc6b03; font-size: 20px;">METHODS</p></i><br>{METHODS}'
st.markdown(m,unsafe_allow_html=True)
if RESULTS !=" ":
# st.write(f"**RESULTS** :\n {RESULTS}\n")
m = f'<i><p style="font-family:Courier; color:#fc6b03; font-size: 20px;">RESULTS</p></i><br>{RESULTS}'
st.markdown(m,unsafe_allow_html=True)
if CONCLUSIONS != " ":
# st.write(f"**CONCLUSIONS** :\n {CONCLUSIONS}\n")
m = f'<i><p style="font-family:Courier; color:#fc6b03; font-size: 20px;">CONCLUSIONS</p></i><br>{CONCLUSIONS}'
st.markdown(m,unsafe_allow_html=True)
def model(raw_text):
text = raw_text.split(". ")
data = preprocess(text)
st.success('Text is preprocesed.', icon="✅")
test_dataset = convartTotensor(data)
st.success('Dataset is created.', icon="✅")
# st.write(test_dataset)
st.success('Model is working the data', icon="⏳")
predict = prediction(test_dataset)
printText(predict,text)
def aboutMe():
st.subheader("About me")
me = "### <b>Hi I am Biltu Dey. Currently I am a student. I am doing this project for educational Purpose.</b>"
st.markdown(me,unsafe_allow_html=True)
follow = """
If you liked the project you can follow me on social media.
[](https://www.linkedin.com/in/BiltuDey/)
[](https://twitter.com/mr_biltu)
### check [](https://github.com/biltudey) account for outher project.
## Support
Any kind of help or facing any problem feel free to contact me.
For support, email biltudey222@gmail.com.
"""
st.markdown(follow,unsafe_allow_html=True)
def main():
"""
This is main Function
"""
st.title("TopicPredictorAI")
# try:
font_css = """
<style>
button[data-baseweb="tab"] > div[data-testid="stMarkdownContainer"] > p {
font-size: 24px;
}
</style>
"""
st.write(font_css, unsafe_allow_html=True)
button_style = """
<style>
.stButton > button {
color: white;
background: black;
width: 100px;
height: 50px;
font: 50px;
}
</style>
"""
st.markdown(button_style, unsafe_allow_html=True)
tab1, tab2, tab3 = st.tabs(["AI","About Me" ,"How to use"])
with tab1:
st.subheader("Enter The Text")
raw_text = st.text_area(" ",value='', height=None, max_chars=None, key=None)
if st.button("Skim"):
if raw_text.strip() == "":
st.error("Please enter something")
else:
model(raw_text)
with tab2:
aboutMe()
with tab3:
text = """
### **Enter your abstract text data.**
#### Something like this.
"""
st.markdown(text,unsafe_allow_html=True)
# st.image('./screenshot\Screenshot.png')
text = """
### **Then click the skim button.**
#### Wait while text is processed by the model.
#### After few second your data will look like this.
"""
st.markdown(text,unsafe_allow_html=True)
# st.image('./screenshot\Screenshot1.png')
# except:
# st.error("There is an error. Please check your text data.")
# st.write(predict)
if __name__ == '__main__':
main()