app.py

import numpy as np
import pandas as pd
import streamlit as st
import json
import torch
from streamlit_option_menu import option_menu
import time
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from streamlit_lottie import st_lottie
from streamlit_lottie import st_lottie_spinner
import speech_recognition as s 




with st.sidebar:
 selected = option_menu(
 menu_title="Main Menu",
 options=["Home","Information"]
 )

 ################# Animation of the title
 
st.markdown(
 """
<style>
.sidebar{
 background-image: linear-gradient(#2e7bcf,#2e7bcf);
 color: white;
}
</style>
""",
 unsafe_allow_html=True,
)



st.markdown(
 f"""
 <style>
 .stApp {{
 background-image: url("https://img.freepik.com/free-vector/seamless-gold-rhombus-grid-pattern-black-background_53876-97589.jpg?w=1060&t=st=1671168593~exp=1671169193~hmac=34116cdbd09587f0c6d4c289e5b48129239d316204877b7f5191f0c6e50d715d");
 background-attachment: fixed;
 background-size: cover
 }}
 </style>
 """,
 unsafe_allow_html=True
 )


if selected == "Home":

 col1,col2,col3,col4,col5,col6,col7,col8 = st.columns(8)




 def load_lottiefile(filepath: str):
 with open(filepath, "r") as f:
 return json.load(f)


 Tbr = load_lottiefile("images/t.json")
 with col1:
 st_lottie(Tbr,
 loop=True,
 speed=0.28,
 key="hello")


 Obr = load_lottiefile("images/o.json")
 with col2:
 st_lottie(Obr,
 speed=0.28,
 key="O")


 Xbr = load_lottiefile("images/x.json")
 with col3:
 st_lottie(Xbr,
 speed=0.28,
 key="X")


 Ibr = load_lottiefile("images/i.json")
 with col4:
 st_lottie(Ibr,
 speed=0.28,
 key="I")



 Cbr = load_lottiefile("images/c.json")
 with col5:
 st_lottie(Cbr,
 speed=0.28,
 key="C") 


 with col6:
 st_lottie(Ibr,
 speed=0.28,
 key="II")


 with col7:
 st_lottie(Tbr,
 speed=0.28,
 key="TT")


 Ybr = load_lottiefile("images/y.json")
 with col8:
 st_lottie(Ybr,
 speed=0.28,
 key="Y")





 ########################END OF ANIMATIONS



 st.markdown(f'<h1 style="color:#00ddb3;font-size:40px;border:2px solid white;margin-bottom:10px; text-align:center">{"Analysis of Text/Speech"}</h1>', unsafe_allow_html=True)

 tokenizer = AutoTokenizer.from_pretrained('unitary/toxic-bert')
 model = AutoModelForSequenceClassification.from_pretrained('unitary/toxic-bert')

 labels=('Toxic','Severe Toxic','Obscene/Sexual','Threat/Violent','Insulting','Identity Hate')

 st.markdown(f'<h1 style="color:#febf00;font-size:30px; text-shadow: 2px 2px 4px #ff0000; text-transform: uppercase;line-height: 1;">{"👉 Input Method"}</h1>', unsafe_allow_html=True)

 t1= st.radio("",
 ('Text','Speech'))

 st.markdown(
 """
 <style>
 .stProgress > div > div > div > div {
 background-image: linear-gradient(to right, #5fde , #00cc42);
 }
 </style>""",
 unsafe_allow_html=True,
 )

 bar = st.progress(0)
 
 if t1 == "Text":

 st.markdown(f'<h1 style="color:#febf00; text-shadow: 2px 2px 2px #ff0000;font-size:25px;">{"Enter Text : "}</h1>', unsafe_allow_html=True)

 text = st.text_area("👇")
 if st.button("Done"):
 with st.spinner("Analysing Toxicity of the Text"):
 time.sleep(1.5)
 tokens = tokenizer.encode(text, return_tensors='pt', max_length=512, truncation=True) 
 
 result = model(tokens) 
 
 toxic_array=[]
 
 for i in range(6):
 toxic_array.append((labels[i],round(float(result.logits[0][i]),3)))
 bar.progress(50)
 bar.progress(100) 
 
 max_= max(toxic_array[1:5], key= lambda tup:tup[1])
 st.success("Result", icon="✅")
 
 if ((toxic_array[0][1]<=0) and (max_[1]<0)):
 st.markdown(f'<h1 style="color:#00e500;text-shadow:1px 1px 1px orange;font-size:25px;">{"Not Toxic"}</h1>', unsafe_allow_html=True)

 else:
 st.markdown(f'<h1 style="color:#EC2001;text-shadow:1px 1px #36024c;font-size:30px;">⭕{max_[0]}</h1>', unsafe_allow_html=True)


 count=0
 for i in range(1,6):
 if toxic_array[i][1]>0:
 count+=1
 if count>1:
 st.markdown(f'<h1 style="color:#febf00;font-size:28px;">{"Other Toxicity:"}</h1>', unsafe_allow_html=True)

 for i in range(1,6):
 if (toxic_array[i][1]>0) and (toxic_array[i]!=max_):
 st.markdown(f'<h1 style="color:#EC2001;text-shadow:1px 1px #36024c;font-size:25px;">⭕{toxic_array[i][0]}</h1>', unsafe_allow_html=True)




 else:
 st.markdown(f'<h1 style="color:#febf00;text-shadow: 2px 2px 3px #ff0000;font-size:23px;">{"🤏 Click on the Mic to Record"}</h1>', unsafe_allow_html=True)


 if st.button("🎙️"):

 sr=s.Recognizer()
 with s.Microphone() as m:
 st.markdown(f'<h1 style="color:white; margin-bottom:5px;font-size:25px;">{"Speak Now..."}</h1>', unsafe_allow_html=True)

 
 audio=sr.listen(m)
 query=sr.recognize_google(audio,language='eng-in')
 #st.image(query)
 st.markdown(f'<h1 style="color:black;margin-bottom:7px;border-radius:10px;background-color:orange;padding:10px;border:2px solid white;font-size:20px;">{query}</h1>', unsafe_allow_html=True)

 tokens = tokenizer.encode(query, return_tensors='pt', max_length=512, truncation=True) 
 result = model(tokens) 
 toxic_array=[]
 with st.spinner("Analysing Toxicity of the Text"):
 time.sleep(1.5) 
 for i in range(6):
 toxic_array.append((labels[i],round(float(result.logits[0][i]),3)))
 bar.progress(50)
 bar.progress(100) 
 max_= max(toxic_array[1:5], key= lambda tup:tup[1])
 st.success('Result', icon="✅")

 if ((toxic_array[0][1]<=0) and (max_[1]<0)):
 st.markdown(f'<h1 style="color:#00e500;font-size:25px;">{"Not Toxic"}</h1>', unsafe_allow_html=True)
 
 else:
 st.markdown(f'<h1 style="color:#ffff;font-size:30px;">⭕{max_[0]}</h1>', unsafe_allow_html=True)
 count=0
 for i in range(1,6):
 if toxic_array[i][1]>0:
 count+=1
 if count>1:
 st.markdown(f'<h1 style="color:#EC2001;font-size:28px;">{"Other Toxicity:"}</h1>', unsafe_allow_html=True)

 for i in range(1,6):
 if (toxic_array[i][1]>0) and (toxic_array[i]!=max_):
 st.markdown(f'<h1 style="color:#ffff;font-size:25px;">⭕{toxic_array[i][0]}</h1>', unsafe_allow_html=True)




if selected == "Information":
 st.markdown(f'<h1 style="color:#ffff;text-shadow:4px 4px 4px black; text-align:center;font-size:32px;">{"This Application uses BERT Model for Analysing Toxicity of Text/Speech"}</h1>', unsafe_allow_html=True)
 
 st.markdown(f'<h1 style="color:#febf00;margin-top:5rem;text-shadow:4px 4px 4px black; text-align:center;font-size:27px;">{"Made by :"}</h1>', unsafe_allow_html=True)
 
 st.markdown(f'<h1 style="color:#ffff;margin-top:0.3rem;text-shadow:4px 4px 4px black;text-align:center;font-size:25px;">{"🪶Aman Kaintura"}</h1>', unsafe_allow_html=True)
 st.markdown(f'<h1 style="color:#ffff;margin-top:1rem;text-shadow:4px 4px 4px black; text-align:center;font-size:25px;">{"🪶Yuvraj Chakravarty"}</h1>', unsafe_allow_html=True)
 st.markdown(f'<h1 style="color:#ffff;margin-top:1rem;text-shadow:4px 4px 4px black; text-align:center;font-size:25px;">{"🪶Bharat Kumar"}</h1>', unsafe_allow_html=True)

 


st.markdown("""
<style>
.css-1lsmgbg.egzxvld0
{
visibility: hidden;
}
</style>
""",unsafe_allow_html=True)