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| import pandas as pd | |
| import streamlit as st | |
| import numpy as np | |
| import tensorflow as tf | |
| from PIL import Image | |
| import pickle | |
| from numpy import argmax | |
| st.header('Demo') | |
| task = st.selectbox('Select Task', ["Select One",'Sentiment Classification', 'Tumor Detection']) | |
| if task == "Tumor Detection": | |
| def cnn(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" | |
| cnn_model = tf.keras.models.load_model("tumor_detection_model.h5") | |
| uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| st.image(uploaded_file, caption="Uploaded Image", use_column_width=True) | |
| if st.button("Submit"): | |
| result=cnn(uploaded_file, cnn_model) | |
| st.write(result) | |
| elif task == "Sentiment Classification": | |
| types = ["Perceptron","BackPropagation", "RNN","DNN", "LSTM"] | |
| input_text2 = st.radio("Select", types, horizontal=True) | |
| if input_text2 == "Perceptron": | |
| with open("ppn_model.pkl",'rb') as file: | |
| perceptron = pickle.load(file) | |
| with open("ppn_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 = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500) | |
| res = model.predict(padded_inp) | |
| if res: | |
| return "Negative" | |
| else: | |
| return "Positive" | |
| st.subheader('Movie Review Classification using Perceptron') | |
| inp = st.text_area('Enter message') | |
| if st.button('Check'): | |
| pred = ppn_make_predictions([inp], perceptron) | |
| st.write(pred) | |
| if input_text2 == "BackPropagation": | |
| with open("bp_model.pkl",'rb') as file: | |
| backprop = pickle.load(file) | |
| with open("bp_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 = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500) | |
| res = model.predict(padded_inp) | |
| if res: | |
| return "Negative" | |
| else: | |
| return "Positive" | |
| st.subheader('Movie Review Classification using BackPropagation') | |
| inp = st.text_area('Enter message') | |
| if st.button('Check'): | |
| pred = bp_make_predictions([inp], backprop) | |
| st.write(pred) | |
| elif input_text2 == "RNN": | |
| rnn_model = tf.keras.models.load_model("rnn_model.h5") | |
| with open("spam_tokeniser.pkl", 'rb') as model_file: | |
| rnn_tokeniser=pickle.load(model_file) | |
| def rnn_make_predictions(inp, model): | |
| encoded_inp = rnn_tokeniser.texts_to_sequences([inp]) | |
| padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=10, padding='post') | |
| res = (model.predict(padded_inp) > 0.5).astype("int32") | |
| if res: | |
| return "Spam" | |
| else: | |
| return "Ham" | |
| st.subheader('Spam message Classification using RNN') | |
| input = st.text_area("Give message") | |
| if st.button('Check'): | |
| pred = rnn_make_predictions([input], rnn_model) | |
| st.write(pred) | |
| elif input_text2 == "DNN": | |
| dnn_model = tf.keras.models.load_model("dnn_model.h5") | |
| with open("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 = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500) | |
| res = model.predict([inp]) | |
| a=argmax(res) | |
| if a==0: | |
| return "Negative" | |
| else: | |
| return "Positive" | |
| st.subheader('Movie Review Classification using DNN') | |
| inp = st.text_area('Enter message') | |
| if st.button('Check'): | |
| pred = dnn_make_predictions([inp], dnn_model) | |
| st.write(pred) | |
| elif input_text2 == "LSTM": | |
| lstm_model = tf.keras.models.load_model("lstm_model.h5") | |
| with open("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 = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500) | |
| res = (model.predict(inp) > 0.5).astype("int32") | |
| if res: | |
| return "Negative" | |
| else: | |
| return "Positive" | |
| st.subheader('Movie Review Classification using LSTM') | |
| inp = st.text_area('Enter message') | |
| if st.button('Check'): | |
| pred = lstm_make_predictions([inp], lstm_model) | |
| st.write(pred) | |