app.py

import streamlit as st
from ultralytics import YOLO
from streamlit_option_menu import option_menu
import tensorflow as tf
import json
from streamlit_lottie import st_lottie
from PIL import Image,ImageFont,ImageDraw
from tensorflow.keras.models import load_model
import numpy as np
from tensorflow.keras.utils import custom_object_scope
import tensorflow_addons as tfa
from joblib import load
from sklearn.feature_extraction.text import TfidfVectorizer

def create_in():
    return tfa.layers.InstanceNormalization()
    
def img_prep(paths):
    out = []
    for i in paths:
        img = Image.open(i)
        img = img.resize((256,256))
        img = img.convert('RGB')
        out.append(img)
    return out

def img_prep_YOLO(paths,size):
    out = []
    for i in paths:
        img = Image.open(i)
        img = img.resize((size,size))
        img = img.convert('RGB')
        out.append(img)
    return out

def model_out(img,model_path):
    img = ((np.asarray(img))/127.5)-1
    img = np.expand_dims(img,0)
    with custom_object_scope({'InstanceNormalization': create_in}):
        model = load_model(model_path)
    pred = ((model.predict(img)+1)*127.5)/255
    return pred[0]

def yolo_out(model,img):
    model = YOLO(model)
    results = model(img)
    for result in results:
        cls = result.boxes.cls[0]
        cls = arr[int(cls)]
        lbl = result.boxes.conf[0]
        boxes = result.boxes.xyxy[0]
        draw = ImageDraw.Draw(img)
        draw.rectangle([boxes[0], boxes[1], boxes[2], boxes[3]], outline="black", width=5)
        text_position = (boxes[0]+boxes[2])/2, boxes[1]-10
        draw.text(text_position, f'{cls} {lbl}', fill="red", font=font)

    return img
        
font_size = 40
font = ImageFont.truetype("arial.ttf", size=font_size)
arr = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']

def lottiemaker(path):
    with open(path) as f:
        return json.load(f)

def loadimg(path):
    img = Image.open(path)
    img = img.resize((150,150))
    return img


selected = option_menu(
    menu_title=None,
    options = ['About me','Tools and Experience','Projects','Contacts'],
    icons=['house','gear','book','phone'],
    default_index=0,
    orientation="horizontal"
)

if selected == 'About me':
    col1,col2 = st.columns(2)# Display the Lottie animation in the first column
    with col1:
        st_lottie(lottiemaker('Animation - 1704777936737.json'), speed=1, width=400, height=370)
    with col2:
        st.write('# Hi, I am Pallab Ghosh')
    st.header('Let me introduce myself')
    
    st.write('I am Pallab Ghosh from West Bengal, India')
    st.write('I am a Self Taught Machine Learning Engineer and Programmer')
    st.write("My passion include Machine Learning, Programming, Gaming, Football, Badminton, Maths, etc")
    st.write('I am currently in High school')
    st.write('I have been learning Machine learning since 2021 and have Made more than 50 projects in the field of Machine Learning')
    st.write('---')
elif selected == 'Tools and Experience':
    st.header('Tools and Expertise')
    col1,col2 = st.columns(2)
    with col1:
        st.write('Below are the tools I use to train AI and the Machine Learning and Deep Learning models I have expertise in')
    with col2:
        st_lottie(lottiemaker('Tools.json'),speed=1,width=300,height=200)

    st.write('# Programming Languages')
    _,col1,col2,_ = st.columns(4)
    with col1:
        st.button('\n \t Python \t \n')
    with col2:
        st.button('\n \t MySQL \t \n')

    st.write('# Machine Learning Frameworks')
    col1,col2,col3,col4,col5 = st.columns(5)

    with col1:
        st.button('\n \t Tensorflow \t \n')
    with col2:
        st.button('\n \t Keras \t \n')
    with col3:
        st.button('\n \t Scikit-Learn \t \n')
    with col3:
        st.button('\n \t Mediapipe \t \n')
    with col3:
        st.button('\n \t CVzone \t \n')

    st.write('# Machine Learning Algorithim')
    col1,col2 = st.columns(2)
    with col1:
        st.button("\n \t Linear Regression \t \n")
    with col2:
        st.button('\n \t Logistic Regression \t \n')

    col3,col4 = st.columns(2)
    with col3:
        st.button('\n \t Naive Bayes \t \n')
    with col4:
        st.button('\n \t Decision Tree \t \n')

    col1,col2 = st.columns(2)

    with col1:
        st.button('\n \t Random Forest \t \n')
    with col2:
        st.button('\n \t K Nearest Neighbour \t \n')

    st.button('\n \t Support Vector Machine \t \n')

    st.write('# Deep Learning Architectures')
    col1,col2,col3,col4 = st.columns(4)
    with col1:
        st.button('Artificial Neural Network')
    with col2:
        st.button("Convolutional Neural Network")
    with col3:
        st.button("Deep Convolutional Genertive Adversarial Network")
    with col4:
        st.button("Cycle Consistent Generative Adversarial Network") 

    col1,col2,col3,col4 = st.columns(4)
    with col1:
        st.button("Paired Image Translation using Pix2Pix")
    with col2:
        st.button("Object Detection using YOLO")
    with col3:
        st.button("Unet model for semantic segmentation")
    with col4:
        st.button("Segmentation using YOLO")  

    col1,col2,col3,col4 = st.columns(4)
    with col1:
        st.button("\n \t StyleGan \t \n")
    with col2:
        st.button("Facebook Prophet model for timeceries prediction")
    with col3:
        st.button("Bidirectional Encoder Representations from Transformers /n (BERT)")
    with col4:
        st.button("Recurrent Neural Network(RNN) and Long Stort Term Memory(LSTM)")

    col2,col3,col4 = st.columns(3)

    with col2:
        st.button("Hand Landmarks detection")
    with col3:
        st.button("Body Landmarks detection")
    with col4:
        st.button("Face Landmarks detction")

    st.write('# Data Preprocessing and Visualisation')
    col1,col2,col3,col4 = st.columns(4)
    with col1:
        st.button('\n \t Numpy \t \n')
    with col2:
        st.button('\n \t Pandas \t \n')
    with col3:
        st.button('\n \t Pillow \t \n')
    with col4:
        st.button('\n \t Matplotlib \t \n')

    st.write('# Model Deployment')

    _,col1,_ = st.columns(3)

    with col1:
        st.button('\n \t Streamlit \t \n')

    st.write("---")

elif selected == 'Projects':
    st.header('Projects')
    st.write('I have made more than 50 projects in fields including CNN, GAN, Machine Learning, Yolo object detection and segmentation, Pix2pix, Cyclegan and more')
    st.write('Below are some models(one from each category)')
    
    options = {'Cyclegan':1, 'DCGAN':2,'Unet':3,'Machine Learning Text classifier':4,'YOLO object detection':5}
    selected_option = st.selectbox('Select an option', options)

    if selected_option == 'Unet':
        st.header('Description')
        st.write('This is a UNET model that segments the waterbodies in the image')
        st.header('Architecture')
        st.write('The model is of unet architecture to preserve the spatial informations in the images after applying Conv2D')
        
        imgs = img_prep(['unet1.jpg','unet2.jpg'])
        col1,col2 = st.columns(2)
        with col1:
            st.image(imgs[0],use_column_width=False)
        with col2:
            st.image(imgs[1],use_column_width=False)

        img_options = {'img1':1, 'img2':2}
        img_selected_option = st.selectbox('Select an Image', img_options)
        if img_selected_option == 'img1':
            st.image(model_out(imgs[0],'Portfolio Projects/FloodAreaSegmentationUnetPix2Pix.h5'),use_column_width=False)
        if img_selected_option == 'img2':
            st.image(model_out(imgs[1],'Portfolio Projects/FloodAreaSegmentationUnetPix2Pix.h5'),use_column_width=False)

    if selected_option == 'YOLO object detection':
        st.header('Description')
        st.write('This is an YOLO mdoel which can detect different hand sign in an image')
        st.header('Architecture')
        st.write('The model uses YOLOv8 to do the detection')
        
        imgs = img_prep_YOLO(['YOLOB.jpg','YOLOC.jpg'],416)
        col1,col2 = st.columns(2)
        with col1:
            st.image(imgs[0],use_column_width=False)
        with col2:
            st.image(imgs[1],use_column_width=False)

        img_options = {'img1':1, 'img2':2}
        img_selected_option = st.selectbox('Select an Image', img_options)
        if img_selected_option == 'img1':
            st.image(yolo_out('Portfolio Projects/HandSignDetector.pt',imgs[0]),use_column_width=False)
        if img_selected_option == 'img2':
            st.image(yolo_out('Portfolio Projects/HandSignDetector.pt',imgs[1]),use_column_width=False)
            
    elif selected_option == 'Machine Learning Text classifier':
        
        st.header('Spam Detection using Naive Bayes Classifier')
        vectorizer = load('Portfolio Projects/tfidf_vectorizer.joblib')
        user_input = st.text_input("Enter some text:", "")
        if user_input is not None:
            x = vectorizer.transform([user_input])
            model = load('Portfolio Projects/Naive_Bayes_Spam_Detection.joblib')
            pred = model.predict(x)
            if pred[0] == 1:
                st.write('The entered text is a Spam')
            elif pred[0] == 0:
                st.write('The entered text is not a Spam')
            else:
                st.write('Error, Try again')
                
    elif selected_option == 'Cyclegan':
        st.header('Description')
        st.write('This is a CycleGAN model that turns an input image into an Image of a Monet Painting')
        st.header('Architecture')
        st.write('The Generator model is the prebuild tensorflow pix2pix generator model')
        st.write('The Discriminator model is the discriminator model from the same module')
        
        imgs = img_prep(['cyclegan1.jpg','cyclegan2.jpg'])
        col1,col2 = st.columns(2)
        with col1:
            st.image(imgs[0],use_column_width=False)
        with col2:
            st.image(imgs[1],use_column_width=False)

        img_options = {'img1':1, 'img2':2}
        img_selected_option = st.selectbox('Select an Image', img_options)
        if img_selected_option == 'img1':
            st.image(model_out(imgs[0],'Portfolio Projects/photo2monet2.h5'),use_column_width=False)
        if img_selected_option == 'img2':
            st.image(model_out(imgs[1],'Portfolio Projects/photo2monet2.h5'),use_column_width=False)

    elif selected_option == 'DCGAN':

        st.header('Description')
        st.write('This is a DCGAN model that turns a random noise vector into an Image of a dog(that sometimes turns out disformed) using Convolutional2D Transpose layers')
        st.header('Architecture')
        st.write('The Generator model is just a set of Convolutional2D Transpose, BatchNormalization and Leaky relu')
        st.write('The Discriminator model is a very simple model with Convolutional2D, Dropout and Leaky Relu')
        model = tf.keras.models.load_model('Portfolio Projects/doggen3.h5')

# Create a button
        button_clicked = st.button("Generate")

# Check if the button is clicked
        if button_clicked:
    # Generate an image
            seed = tf.random.normal((1, 100))
            pred = model.predict(seed)
            pred = pred * 0.5 + 0.5  # Normalize the pixel values
            pred = np.squeeze(pred)  # Remove singleton dimensions if any
            st.image(pred,use_column_width=True)

    st.write('To see other projects, You can Visit my profile on HuggingFace')
    st.link_button('\n \t \t \t HuggingFace Account \t \t \r \n','https://huggingface.co/Beasto')
elif selected == 'Contacts':
    st.header('Contact me')
    col1,col2,col3,col4 = st.columns(4)
    with col1:
        st.link_button('\n \t \t \t  Instagram \t \t \r \n','https://www.instagram.com/i_suck_at_coding.256/')
    with col2:
        st.link_button('\n \t \t \t Github \t \t \r \n','https://github.com/Beastojenisto')
    with col3:
        st.link_button('\n \t \t \t Twitter/X \t \t \r \n','https://twitter.com/Isuckatcodinboi') 
    with col4:
        st.link_button('\n \t \t \t HuggingFace \t \t \r \n','https://huggingface.co/Beasto')