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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.datasets import imdb | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import pickle | |
| # Load word index for Sentiment Classification | |
| word_to_index = imdb.get_word_index() | |
| # Function to perform sentiment classification | |
| def sentiment_classification(new_review_text, model): | |
| max_review_length = 500 | |
| new_review_tokens = [word_to_index.get(word, 0) for word in new_review_text.split()] | |
| new_review_tokens = pad_sequences([new_review_tokens], maxlen=max_review_length) | |
| prediction = model.predict(new_review_tokens) | |
| if type(prediction) == list: | |
| prediction = prediction[0] | |
| return "Positive" if prediction > 0.5 else "Negative" | |
| # Function to perform tumor detection | |
| def tumor_detection(img, model): | |
| img = Image.open(img) | |
| img = img.resize((128, 128)) | |
| img = np.array(img) | |
| # Apply any necessary preprocessing based on the model's requirements | |
| # For example: img = preprocess_image(img) | |
| input_img = np.expand_dims(img, axis=0) | |
| res = model.predict(input_img) | |
| return "Tumor Detected" if res else "No Tumor" | |
| # Streamlit App | |
| st.title("Deep Prediction Hub") | |
| # Choose between tasks | |
| task = st.radio("Select Task", ("Sentiment Classification", "Tumor Detection")) | |
| if task == "Sentiment Classification": | |
| new_review_text = st.text_area("Enter a New Review:", value="") | |
| if st.button("Submit") and not new_review_text.strip(): | |
| st.warning("Please enter a review.") | |
| if new_review_text.strip(): | |
| st.subheader("Choose Model for Sentiment Classification") | |
| model_option = st.selectbox("Select Model", ("Perceptron", "Backpropagation", "DNN", "RNN", "LSTM")) | |
| # Load models dynamically based on the selected option | |
| if model_option == "Perceptron": | |
| with open('PP.pkl', 'rb') as file: | |
| model = pickle.load(file) | |
| elif model_option == "Backpropagation": | |
| with open('BP.pkl', 'rb') as file: | |
| model = pickle.load(file) | |
| elif model_option == "DNN": | |
| model = load_model('DP.keras') | |
| elif model_option == "RNN": | |
| model = load_model('RN.keras') | |
| elif model_option == "LSTM": | |
| model = load_model('LS.keras') | |
| if st.button("Classify Sentiment"): | |
| result = sentiment_classification(new_review_text, model) | |
| st.subheader("Sentiment Classification Result") | |
| st.write(f"**{result}**") | |
| elif task == "Tumor Detection": | |
| st.subheader("Tumor Detection") | |
| uploaded_file = st.file_uploader("Choose a tumor image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Load the tumor detection model | |
| model = load_model('CN.h5') | |
| st.image(uploaded_file, caption="Uploaded Image.", use_column_width=False, width=200) | |
| st.write("") | |
| if st.button("Detect Tumor"): | |
| result = tumor_detection(uploaded_file, model) | |
| st.subheader("Tumor Detection Result") | |
| st.write(f"**{result}**") | |