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