| import streamlit as st |
| import pickle |
| import tensorflow as tf |
| from tensorflow.keras.preprocessing import sequence |
| from tensorflow.keras.saving import load_model |
| import numpy as np |
| import cv2 |
| from PIL import Image |
|
|
|
|
| st.title('Classifier System') |
| task = st.selectbox('Select Task', ['Choose one','Sentiment Classification', 'Tumor Detection']) |
|
|
| if task=='Tumor Detection': |
| st.subheader('Tumor Detection with CNN') |
| |
| |
| |
| cnn_model = load_model(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\CNN\cnn_model1.h5") |
|
|
| img = st.file_uploader('Upload image', type=['jpeg', 'jpg', 'png']) |
|
|
| def cnn_make_prediction(img,model): |
| img=Image.open(img) |
| img=img.resize((128,128)) |
| img=np.array(img) |
| input_img = np.expand_dims(img, axis=0) |
| res = model.predict(input_img) |
| if res: |
| return "Tumor Detected" |
| else: |
| return "No Tumor Detected" |
| |
| |
| if img is not None: |
| st.image(img, caption = "Image preview") |
| if st.button('Submit'): |
| pred = cnn_make_prediction(img, cnn_model) |
| st.write(pred) |
|
|
|
|
| if task=='Sentiment Classification': |
| arcs = ['Perceptron', 'Backpropagation', 'DNN', 'RNN', 'LSTM'] |
| arc = st.radio('Pick one:', arcs, horizontal=True) |
|
|
| if arc == arcs[0]: |
| |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\Perceptron\pnn_model.pkl",'rb') as file: |
| perceptron = pickle.load(file) |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\Perceptron\pnn_tokeniser.pkl",'rb') as file: |
| ppn_tokeniser = pickle.load(file) |
|
|
| def ppn_make_predictions(inp, model): |
| encoded_inp = ppn_tokeniser.texts_to_sequences([inp]) |
| padded_inp = sequence.pad_sequences(encoded_inp, maxlen=500) |
| res = model.predict(padded_inp) |
| if res: |
| return "Not spam" |
| else: |
| return "Spam" |
| |
| st.subheader('SMS spam Classification using Perceptron') |
| inp = st.text_area('Enter message') |
| if st.button('Check'): |
| pred = ppn_make_predictions([inp], perceptron) |
| st.write(pred) |
|
|
| elif arc == arcs[1]: |
| |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\BackPropagation\bpn_model.pkl",'rb') as file: |
| backprop = pickle.load(file) |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\BackPropagation\bpn_tokeniser.pkl",'rb') as file: |
| bp_tokeniser = pickle.load(file) |
|
|
| def bp_make_predictions(inp, model): |
| encoded_inp = bp_tokeniser.texts_to_sequences([inp]) |
| padded_inp = sequence.pad_sequences(encoded_inp, maxlen=500) |
| res = model.predict(padded_inp) |
| if res: |
| return "Not spam" |
| else: |
| return "Spam" |
| |
| st.subheader('SMS spam Classification using Backpropagation') |
| inp = st.text_area('Enter message') |
| if st.button('Check'): |
| pred = bp_make_predictions([inp], backprop) |
| st.write(pred) |
|
|
|
|
| elif arc == arcs[2]: |
| |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\DNN\dnn_model.pkl",'rb') as file: |
| dnn_model = pickle.load(file) |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\DNN\dnn_tokeniser.pkl",'rb') as file: |
| dnn_tokeniser = pickle.load(file) |
|
|
| def dnn_make_predictions(inp, model): |
| inp = dnn_tokeniser.texts_to_sequences(inp) |
| inp = sequence.pad_sequences(inp, maxlen=500) |
| res = (model.predict(inp) > 0.5).astype("int32") |
| if res: |
| return "Not spam" |
| else: |
| return "Spam" |
| |
| st.subheader('SMS spam Classification using DNN') |
| inp = st.text_area('Enter message') |
| if st.button('Check'): |
| pred = dnn_make_predictions([inp], dnn_model) |
| st.write(pred) |
|
|
|
|
| elif arc == arcs[3]: |
| |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\RNN\rnn_model.pkl",'rb') as file: |
| rnn_model = pickle.load(file) |
|
|
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\RNN\rnn_tokeniser.pkl",'rb') as file: |
| rnn_tokeniser = pickle.load(file) |
|
|
| def rnn_make_predictions(inp, model): |
| encoded_inp = rnn_tokeniser.texts_to_sequences(inp) |
| padded_inp = sequence.pad_sequences(encoded_inp, maxlen=10, padding='post') |
| res = (model.predict(padded_inp) > 0.5).astype("int32") |
| if res: |
| return "Spam" |
| else: |
| return "Not spam" |
|
|
| st.subheader('SMS Spam Classification using RNN') |
| inp = st.text_area('Enter message') |
| if st.button('Check'): |
| pred = rnn_make_predictions([inp], rnn_model) |
| st.write(pred) |
| |
|
|
| elif arc == arcs[4]: |
| |
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\LSTM\lstm_model.pkl",'rb') as file: |
| lstm_model = pickle.load(file) |
|
|
| with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\LSTM\lstm_tokeniser.pkl",'rb') as file: |
| lstm_tokeniser = pickle.load(file) |
|
|
| def lstm_make_predictions(inp, model): |
| inp = lstm_tokeniser.texts_to_sequences(inp) |
| inp = sequence.pad_sequences(inp, maxlen=500) |
| res = (model.predict(inp) > 0.5).astype("int32") |
| if res: |
| return "Not spam" |
| else: |
| return "Spam" |
|
|
| st.subheader('SMS spam Classification using LSTM') |
| inp = st.text_area('Enter message') |
| if st.button('Check'): |
| pred = lstm_make_predictions([inp], lstm_model) |
| st.write(pred) |
