Create app.py

app.py

@@ -0,0 +1,261 @@
+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)
+