Spaces:
Build error
Build error
File size: 5,324 Bytes
b18868d 11b96a3 b18868d 6a97837 11b96a3 b18868d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import streamlit as st
from time import sleep
from stqdm import stqdm
import pandas as pd
from transformers import pipeline
import json
def draw_all(
key,
plot=False,
):
st.write(
"""
# NLP Web App
This Natural Language Processing Based Web App can do anything u can imagine with Text. 😱
This App is built using pretrained transformers which are capable of doing wonders with the Textual data.
```python
# Key Features of this App.
1. Advanced Text Summarizer
2. Sentiment Analysis
3. Question Answering
4. Text Completion
```
"""
)
with st.sidebar:
draw_all("sidebar")
#main function that holds all the options
def main():
st.title("NLP IE Web App")
menu = ["--Select--","Summarizer",
"Sentiment Analysis","Question Answering","Text Completion"]
choice = st.sidebar.selectbox("What task would you like to do?", menu)
if choice=="--Select--":
st.write("""
Welcome to the the Web App of Data Dynamos. As an IE student of the Master of Business Analyitics and Big Data you have the opportunity to
do anything with your lectures you like
""")
st.write("""
Never heard of NLP? No way! Natural Language Processing (NLP) is a computational technique
to process human language in all of it's complexity
""")
st.write("""
NLP is an vital discipline in Artificial Intelligence and keeps growing
""")
st.image('banner_image.png')
elif choice=="Summarizer":
st.subheader("Text Summarization")
st.write(" Enter the Text you want to summarize !")
raw_text = st.text_area("Your Text","Enter Your Text Here")
num_words = st.number_input("Enter Number of Words in Summary")
if raw_text!="" and num_words is not None:
num_words = int(num_words)
summarizer = pipeline('summarization')
summary = summarizer(raw_text, min_length=num_words,max_length=50)
s1 = json.dumps(summary[0])
d2 = json.loads(s1)
result_summary = d2['summary_text']
result_summary = '. '.join(list(map(lambda x: x.strip().capitalize(), result_summary.split('.'))))
st.write(f"Here's your Summary : {result_summary}")
elif choice=="Sentiment Analysis":
st.subheader("Sentiment Analysis")
#loading the pipeline
sentiment_analysis = pipeline("sentiment-analysis")
st.write(" Enter the Text below To find out its Sentiment !")
raw_text = st.text_area("Your Text","Enter Text Here")
if raw_text !="Enter Text Here":
result = sentiment_analysis(raw_text)[0]
sentiment = result['label']
for _ in stqdm(range(50), desc="Please wait a bit. The model is fetching the results !!"):
sleep(0.1)
if sentiment =="POSITIVE":
st.write("""# This text has a Positive Sentiment. 🤗""")
elif sentiment =="NEGATIVE":
st.write("""# This text has a Negative Sentiment. 😤""")
elif sentiment =="NEUTRAL":
st.write("""# This text seems Neutral ... 😐""")
elif choice=="Question Answering":
st.subheader("Question Answering")
st.write(" Enter the Context and ask the Question to find out the Answer !")
question_answering = pipeline("question-answering")
context = st.text_area("Context","Enter the Context Here")
#This is the text box for the question
question = st.text_area("Your Question","Enter your Question Here")
if context !="Enter Text Here" and question!="Enter your Question Here":
#we are passing question and the context
result = question_answering(question=question, context=context)
#dump the result in json and load it again
s1 = json.dumps(result)
d2 = json.loads(s1)
generated_text = d2['answer']
#joining and capalizing by dot
generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
st.write(f" Here's your Answer :\n {generated_text}")
elif choice=="Text Completion":
st.subheader("Text Completion")
st.write(" Enter the uncomplete Text to complete it automatically using AI !")
text_generation = pipeline("text-generation")
message = st.text_area("Your Text","Enter the Text to complete")
if message !="Enter the Text to complete":
generator = text_generation(message)
s1 = json.dumps(generator[0])
d2 = json.loads(s1)
generated_text = d2['generated_text']
generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
st.write(f" Here's your Generate Text :\n {generated_text}")
#main function to run
if __name__ == '__main__':
main() |