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| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| from tensorflow.keras.applications.inception_v3 import preprocess_input | |
| import tensorflow as tf | |
| import joblib | |
| # Load saved models | |
| image_model = load_model('tumor_detection_model.h5') | |
| dnn_model = load_model('sms_spam_detection_dnnmodel.h5') | |
| rnn_model = load_model('spam_detection_rnn_model.h5') | |
| perceptron_model = joblib.load('imdb_perceptron_model.pkl') | |
| backprop_model = joblib.load('backprop_model.pkl') | |
| LSTM_model = load_model('imdb_LSTM.h5') | |
| # Streamlit app | |
| st.title("Classification") | |
| # Sidebar | |
| task = st.sidebar.selectbox("Select Task", ["Tumor Detection", "Sentiment Classification"]) | |
| def preprocess_message_dnn(message, tokeniser, max_length): | |
| encoded_message = tokeniser.texts_to_sequences([message]) | |
| padded_message = pad_sequences(encoded_message, maxlen=max_length, padding='post') | |
| return padded_message | |
| def predict_dnnspam(message, tokeniser, max_length): | |
| processed_message = preprocess_message_dnn(message, tokeniser, max_length) | |
| prediction = dnn_model.predict(processed_message) | |
| return "Spam" if prediction >= 0.5 else "Ham" | |
| # Other prediction functions for sentiment analysis can follow a similar pattern | |
| # Function for CNN prediction | |
| def preprocess_image(image): | |
| image = image.resize((299, 299)) | |
| image_array = np.array(image) | |
| preprocessed_image = preprocess_input(image_array) | |
| return preprocessed_image | |
| def make_prediction_cnn(image, model): | |
| img = image.resize((128, 128)) | |
| img_array = np.array(img) | |
| img_array = img_array.reshape((1, img_array.shape[0], img_array.shape[1], img_array.shape[2])) | |
| preprocessed_image = preprocess_input(img_array) | |
| prediction = model.predict(preprocessed_image) | |
| return "Tumor Detected" if prediction > 0.5 else "No Tumor" | |
| if task == "Sentiment Classification": | |
| st.subheader("Choose Model") | |
| model_choice = st.radio("Select Model", ["DNN", "RNN", "Perceptron", "Backpropagation", "LSTM"]) | |
| st.subheader("Text Input") | |
| text_input = st.text_area("Enter Text") | |
| if st.button("Predict"): | |
| if model_choice == "DNN": | |
| # You need to define tokeniser and max_length for DNN model | |
| prediction_result = predict_dnnspam(text_input, tokeniser, max_length) | |
| st.write(f"The message is classified as: {prediction_result}") | |
| # Other model choices should call respective prediction functions similarly | |
| else: | |
| st.subheader("Choose Model") | |
| model_choice = st.radio("Select Model", ["CNN"]) | |
| st.subheader("Image Input") | |
| image_input = st.file_uploader("Choose an image...", type="jpg") | |
| if image_input is not None: | |
| image = Image.open(image_input) | |
| st.image(image, caption="Uploaded Image.", use_column_width=True) | |
| if st.button("Predict"): | |
| if model_choice == "CNN": | |
| prediction_result = make_prediction_cnn(image, image_model) | |
| st.write(prediction_result) | |