import streamlit as st import numpy as np from PIL import Image from tensorflow.keras.models import load_model import joblib from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.applications.inception_v3 import preprocess_input from tensorflow.keras.datasets import imdb import cv2 from BackPropogation import BackPropogation from Perceptron import Perceptron from sklearn.linear_model import Perceptron import tensorflow as tf import joblib import pickle from numpy import argmax # 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') # Loading the model using pickle with open('backprop_model.pkl', 'rb') as file: backprop_model = pickle.load(file) with open('imdb_perceptron_model.pkl', 'rb') as file: perceptron_model = pickle.load(file) with open('tokeniser.pkl', 'rb') as file: loaded_tokeniser = pickle.load(file) lstm_model_path='lstm_imdb_model.h5' # Streamlit app st.title("Classification") # Sidebar task = st.sidebar.selectbox("Select Task", ["Tumor Detection ", "Sentiment Classification"]) tokeniser = tf.keras.preprocessing.text.Tokenizer() max_length=10 def predictdnn_spam(text): sequence = loaded_tokeniser.texts_to_sequences([text]) padded_sequence = pad_sequences(sequence, maxlen=10) prediction = dnn_model.predict(padded_sequence)[0][0] if prediction >= 0.5: return "not spam" else: return "spam" def preprocess_imdbtext(text, maxlen=200, num_words=10000): # Tokenizing the text tokenizer = Tokenizer(num_words=num_words) tokenizer.fit_on_texts(text) # Converting text to sequences sequences = tokenizer.texts_to_sequences(text) # Padding sequences to a fixed length padded_sequences = pad_sequences(sequences, maxlen=maxlen) return padded_sequences, tokenizer def predict_sentiment_backprop(text, model): preprocessed_text = preprocess_imdbtext(text, 200) prediction = backprop_model.predict(preprocessed_text) return prediction def preprocess_imdb_lstm(user_input, tokenizer, max_review_length=500): # Tokenize and pad the user input user_input_sequence = tokenizer.texts_to_sequences([user_input]) user_input_padded = pad_sequences(user_input_sequence, maxlen=max_review_length) return user_input_padded def predict_sentiment_lstm(model, user_input, tokenizer): preprocessed_input = preprocess_imdb_lstm(user_input, tokenizer) prediction = model.predict(preprocessed_input) return prediction def predict_sentiment_precep(user_input, num_words=1000, max_len=200): word_index = imdb.get_word_index() input_sequence = [word_index[word] if word in word_index and word_index[word] < num_words else 0 for word in user_input.split()] padded_sequence = pad_sequences([input_sequence], maxlen=max_len) return padded_sequence def preprocess_message_dnn(message, tokeniser, max_length): # Tokenize and pad the input message encoded_message = tokeniser.texts_to_sequences([message]) padded_message = tf.keras.preprocessing.sequence.pad_sequences(encoded_message, maxlen=max_length, padding='post') return padded_message def predict_rnnspam(message, tokeniser, max_length): # Preprocess the message processed_message = preprocess_message_dnn(message, tokeniser, max_length) # Predict spam or ham prediction = rnn_model.predict(processed_message) if prediction >= 0.5: return "Spam" else: return "Ham" # make a prediction for CNN 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, 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 = image_model.predict(preprocessed_image) if prediction > 0.5: st.write("Tumor Detected") else: st.write("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") if model_choice=='DNN': text_input = st.text_area("Enter Text") if st.button("Predict"): if text_input: prediction_result = predictdnn_spam(text_input) st.write(f"The review's class is: {prediction_result}") else: st.write("Enter a movie review") elif model_choice == "RNN": text_input = st.text_area("Enter Text") if text_input: prediction_result = predict_rnnspam(text_input,loaded_tokeniser,max_length=10) if st.button("Predict"): st.write(f"The message is classified as: {prediction_result}") else: st.write("Please enter some text for prediction") elif model_choice == "Perceptron": text_input = st.text_area("Enter Text" ) if st.button('Predict'): processed_input = predict_sentiment_precep(text_input) prediction = perceptron_model.predict(processed_input)[0] sentiment = "Positive" if prediction == 1 else "Negative" st.write(f"Predicted Sentiment: {sentiment}") elif model_choice == "LSTM": lstm_model = tf.keras.models.load_model(lstm_model_path) text_input = st.text_area("Enter text for sentiment analysis:", "") if st.button("Predict"): tokenizer = Tokenizer(num_words=5000) prediction = predict_sentiment_lstm(lstm_model, text_input, tokenizer) if prediction[0][0]<0.5 : result="Negative" st.write(f"The message is classified as: {result}") else: result="Positive" st.write(f"The message is classified as: {result}") elif model_choice == "Backpropagation": text_input = st.text_area("Enter Text" ) if st.button('Predict'): processed_input = predict_sentiment_precep(text_input) prediction = backprop_model.predict(processed_input)[0] sentiment = "Positive" if prediction == 1 else "Negative" st.write(f"Predicted Sentiment: {sentiment}") 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) # Preprocess the image preprocessed_image = preprocess_image(image) if st.button("Predict"): if model_choice == "CNN": make_prediction_cnn(image, image_model)