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| import pickle | |
| import numpy as np | |
| import streamlit as st | |
| import cv2 | |
| import tensorflow as tf | |
| from tqdm import tqdm | |
| from PIL import Image | |
| import os | |
| from tensorflow.keras.preprocessing import sequence | |
| from tensorflow.keras.saving import load_model | |
| st.title("DL-Classifier") | |
| task = st.selectbox('Select One',("Choose any","Sentiment Classification", 'Tumor Detection')) | |
| #choosing tumor detection | |
| #CNN | |
| if task=="Tumor Detection": | |
| st.subheader("Tumor Detection") | |
| model_path = os.path.join(os.getcwd(), 'cnn_model.h5') | |
| cnn_model = load_model(model_path) | |
| img =st.file_uploader("choose the image",type=('jpg','jpeg','png')) | |
| def cnn_make_prediction(img,cnn_model): | |
| img=Image.open(img) | |
| img=img.resize((128,128)) | |
| img=np.array(img) | |
| input_img = np.expand_dims(img, axis=0) | |
| res = cnn_model.predict(input_img) | |
| if res: | |
| return"Tumor" | |
| else: | |
| return"No Tumor" | |
| #if img != None: | |
| #img_f="D:/SEM 3/DL/DL-ALGORITHMS/CNN/tumor_detection/tumordata/" | |
| #sub_dir=os.listdir(img_f) | |
| # cel_path=os.path.join(sub_dir,img_f)DL-models/app.py | |
| #cel_img=os.listdir(cel_path) | |
| #img_p=cel_img + img.name | |
| #pred=cnn_make_prediction(img_p,cnn_model) | |
| #st.write(pred) | |
| if img is not None: | |
| st.image(img, caption="Uploaded Image.", use_column_width=False, width=200) | |
| st.write("") | |
| if st.button("Detect Tumor"): | |
| result =cnn_make_prediction(img, cnn_model) | |
| st.subheader("Tumor Detection Result") | |
| st.write(f"**{result}**") | |
| #choosing sentiment classification | |
| if task=="Sentiment Classification": | |
| st.subheader("Sentiment Classification") | |
| clss_model= st.radio("Select Classification Model:",("RNN","DNN","Backpropagation",'Perceptron','LSTM')) | |
| select_model=None | |
| if clss_model=="RNN": | |
| model_path = os.path.join(os.getcwd(), 'rnn_model.h5') | |
| rnn_model = load_model(model_path) | |
| with open("rnn_tokeniser.pkl",'rb') as tokeniser_file: | |
| rnn_tokeniser=pickle.load(tokeniser_file) | |
| st.subheader('RNN Spam Classification') | |
| input=st.text_area("Enter your message here:") | |
| def rnn_pred(input): | |
| max_length=10 | |
| encoded_test = rnn_tokeniser.texts_to_sequences(input) | |
| padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=max_length, padding='post') | |
| predict= (rnn_model.predict(padded_test) > 0.5).astype("int32") | |
| if predict: | |
| return "Spam " | |
| else: | |
| return "Not Spam" | |
| if st.button('Check'): | |
| pred=rnn_pred([input]) | |
| st.write(pred) | |
| if clss_model=='Perceptron': | |
| with open("perceptron_model_saved.pkl",'rb') as model_file: | |
| percep_model=pickle.load(model_file) | |
| with open('perceptron_tokeniser_saved.pkl','rb') as model_file: | |
| percep_token=pickle.load(model_file) | |
| st.subheader('Perceptron Spam Classification') | |
| input= st.text_area("Enter your text here") | |
| def percep_pred(input): | |
| encoded_test_p = percep_token.texts_to_sequences([input]) | |
| padded_test_p = tf.keras.preprocessing.sequence.pad_sequences(encoded_test_p, maxlen=10) | |
| predict_p= percep_model.predict(padded_test_p) | |
| if predict_p: | |
| return "Spam" | |
| else: | |
| return "Not Spam" | |
| if st.button("Check"): | |
| pred=percep_pred([input]) | |
| st.write(pred) | |
| if clss_model=="Backpropagation": | |
| with open('bp_model.pkl','rb') as model_file: | |
| bp_model=pickle.load(model_file) | |
| with open('backrpop_tokeniser.pkl','rb') as model_file: | |
| bp_tokeniser=pickle.load(model_file) | |
| st.subheader('Movie Review Classification using Backpropagation') | |
| inp = st.text_area('Enter message') | |
| 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 "Negative" | |
| else: | |
| return "Positive" | |
| if st.button('Check'): | |
| pred = bp_make_predictions([inp], bp_model) | |
| st.write(pred) | |
| if clss_model=="DNN": | |
| model_path = os.path.join(os.getcwd(), 'dnn_model.h5') | |
| dnn_model = load_model(model_path) | |
| with open("dnn_tokeniser.pkl",'rb') as file: | |
| dnn_tokeniser = pickle.load(file) | |
| st.subheader('SMS Spam Classification using DNN') | |
| inp = st.text_area('Enter message') | |
| def dnn_make_predictions(inp, model): | |
| encoded_inp = dnn_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" | |
| if st.button('Check'): | |
| pred = dnn_make_predictions([inp], dnn_model) | |
| st.write(pred) | |
| if clss_model=="LSTM": | |
| model_path = os.path.join(os.getcwd(), 'lstm_model.h5') | |
| lstm_model = load_model(model_path) | |
| with open("lstm_tokeniser.pkl",'rb') as file: | |
| lstm_tokeniser=pickle.load(file) | |
| st.subheader('Movie Review Classification') | |
| inp=st.text_area("Enter your review") | |
| 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 "Negative" | |
| else: | |
| return "Positive" | |
| if st.button('Check'): | |
| pred = lstm_make_predictions([inp], lstm_model) | |
| st.write(pred) | |