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| import streamlit as st | |
| from keras.models import load_model | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| from tensorflow.keras.datasets import imdb | |
| from tensorflow.keras.preprocessing import sequence | |
| import joblib | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| st.title("DL_PREDICTION") | |
| # Dropdown for selecting the task | |
| task = st.selectbox("Select Task", ["Sentiment Analysis", "Tumor Classification"]) | |
| if task == "Sentiment Analysis": | |
| selected_algorithm = st.selectbox("Select Algorithm", ["Perceptron", "Backpropagation", "DNN", "RNN", "LSTM"]) | |
| if selected_algorithm=='LSTM': | |
| imdb_lstm=load_model('saved_model/imdb_lstm.h5') | |
| def lstm_predict_imdb(review, model, max_review_length): | |
| top_words=5000 | |
| # Convert the review to the IMDB dataset format | |
| review_sequence = imdb.get_word_index() | |
| review = [review_sequence[word] if word in review_sequence and review_sequence[word] < top_words else 0 for word in review.split()] | |
| review = sequence.pad_sequences([review], maxlen=max_review_length) | |
| prediction = model.predict(review) | |
| if prediction>0.5: | |
| st.write("Positive Sentiment") | |
| else: | |
| st.write("Negative Sentiment") | |
| review=st.text_input("Enter your review here.") | |
| if st.button("*Make Prediction*"): | |
| lstm_predict_imdb(review,imdb_lstm,500) | |
| elif selected_algorithm=="Perceptron": | |
| imdb_perceptron = joblib.load(r"saved_model/imdb_percepton.joblib") | |
| def perceptron_predict_imdb(review, vectorizer, model): | |
| review_bow = vectorizer.transform([review]) | |
| prediction = imdb_perceptron.predict(review_bow) | |
| if prediction > 0.5 : | |
| st.success("Positive sentiment") | |
| else: | |
| st.success("Negative sentiment") | |
| review=st.text_input("Give your review here.") | |
| vectorizer = joblib.load(r"saved_model/vectariser_imdb.joblib") | |
| if st.button("*Make Prediction*"): | |
| perceptron_predict_imdb(review,vectorizer,imdb_perceptron) | |
| elif selected_algorithm=='DNN': | |
| imdb_dnn = load_model(r'saved_model/imdb_dnn.h5') | |
| word_to_index = imdb.get_word_index() | |
| def dnn_predict_sentiment(review, model): | |
| prediction = imdb_dnn.predict(review) | |
| if prediction > 0.5 : | |
| st.success("Positive sentiment") | |
| else: | |
| st.success("Negative sentiment") | |
| review=st.text_input("Enter your review here.") | |
| if st.button("Make Prediction"): | |
| max_review_length=500 | |
| new_review_tokens=[word_to_index.get(word, 0) for word in review.split()] | |
| new_review_tokens = pad_sequences([new_review_tokens], maxlen=max_review_length) | |
| dnn_predict_sentiment(new_review_tokens,imdb_dnn) | |
| elif selected_algorithm=='RNN': | |
| imdb_rnn=load_model("saved_model/rnn_imdb.h5") | |
| def rnn_predict_imdb(review, model, max_review_length): | |
| top_words=5000 | |
| # Convert the review to the IMDB dataset format | |
| review_sequence = imdb.get_word_index() | |
| review = [review_sequence[word] if word in review_sequence and review_sequence[word] < top_words else 0 for word in review.split()] | |
| review = sequence.pad_sequences([review], maxlen=max_review_length) | |
| prediction = model.predict(review) | |
| if prediction>0.5: | |
| st.write("Positive Sentiment") | |
| else: | |
| st.write("Negative Sentiment") | |
| review=st.text_input("Enter your review here.") | |
| if st.button("*Make Prediction*"): | |
| rnn_predict_imdb(review,imdb_rnn,500) | |
| elif task == "Tumor Classification": | |
| selected_algorithm = st.selectbox("Select Algorithm", ["CNN"]) | |
| uploaded_file = st.file_uploader("Upload Tumor Image", type=["jpg", "jpeg", "png"]) | |
| tumor_cnn = load_model('saved_model/cnn_tumor.h5') | |
| def make_tumor_prediction(img,model): | |
| if img is None: | |
| st.warning("Please upload an image.") | |
| return | |
| if img is not None: | |
| img_array = np.array(Image.open(img)) | |
| img_array = cv2.resize(img_array, (128, 128)) | |
| input_img = np.expand_dims(img_array, axis=0) | |
| prediction = model.predict(input_img) | |
| if prediction > 0.5: | |
| st.success("Tumor Detected") | |
| else: | |
| st.success("No Tumor") | |
| if st.button('Make prediction'): | |
| make_tumor_prediction(uploaded_file, tumor_cnn) | |