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)
+