|
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 |
|
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 |
|
|
|
|
|
|
|
with open(r'tokeniser.pkl', 'rb') as handle: |
|
loaded_tokenizer = pickle.load(handle) |
|
|
|
|
|
image_model = load_model('tumor_detection_model.h5') |
|
dnn_model = load_model('imdb_model.h5') |
|
loaded_model = tf.keras.models.load_model('sms_spam_detection_dnnmodel.h5') |
|
perceptron_model = joblib.load('perceptron_model.joblib') |
|
backprop_model = joblib.load('backprop_model.pkl') |
|
|
|
|
|
|
|
st.title("Classification") |
|
|
|
|
|
task = st.sidebar.selectbox("Select Task", ["Tumor Detection", "Sentiment Classification"]) |
|
|
|
def preprocess_text(text): |
|
tokenizer = Tokenizer() |
|
tokenizer.fit_on_texts([text]) |
|
sequences = tokenizer.texts_to_sequences([text]) |
|
preprocessed_text = pad_sequences(sequences, maxlen=4) |
|
|
|
return preprocessed_text |
|
|
|
|
|
|
|
def predict_dnn(preprocessed_text): |
|
preprocessed_text = preprocessed_text.reshape((1, 4)) |
|
|
|
prediction = dnn_model.predict(preprocessed_text) |
|
st.write("DNN Prediction:", prediction) |
|
|
|
|
|
|
|
def predict_rnn(input_text): |
|
|
|
encoded_input = loaded_tokenizer.texts_to_sequences([input_text]) |
|
padded_input = tf.keras.preprocessing.sequence.pad_sequences(encoded_input, maxlen=10, padding='post') |
|
prediction = loaded_model.predict(padded_input) |
|
if prediction > 0.5: |
|
return "spam" |
|
else: |
|
return "ham" |
|
|
|
|
|
def predict_custom_perceptron(preprocessed_text): |
|
perceptron = CustomPerceptron(epochs=10) |
|
prediction = perceptron.predict(preprocessed_text) |
|
st.write("Custom Perceptron Prediction:", prediction) |
|
|
|
def predict_sklearn_perceptron(preprocessed_text): |
|
perceptron = SklearnPerceptron() |
|
prediction = perceptron.predict(preprocessed_text) |
|
st.write("Sklearn Perceptron Prediction:", prediction) |
|
|
|
def predict_backpropagation(preprocessed_text): |
|
prediction = backprop_model.predict(preprocessed_text) |
|
st.write("Backpropagation Prediction:", 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, 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"]) |
|
|
|
st.subheader("Text Input") |
|
text_input = st.text_area("Enter Text") |
|
|
|
if st.button("Predict"): |
|
|
|
preprocessed_text = preprocess_text(text_input) |
|
if model_choice == "DNN": |
|
predict_dnn(preprocessed_text) |
|
elif model_choice == "RNN": |
|
if text_input: |
|
prediction_result = predict_rnn(text_input) |
|
st.write(f"The message is classified as: {prediction_result}") |
|
else: |
|
st.write("Please enter some text for prediction") |
|
elif model_choice == "Custom Perceptron": |
|
predict_custom_perceptron(preprocessed_text) |
|
elif model_choice == "Sklearn Perceptron": |
|
predict_sklearn_perceptron(preprocessed_text) |
|
elif model_choice == "Backpropagation": |
|
predict_backpropagation(preprocessed_text) |
|
|
|
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) |
|
|
|
|
|
preprocessed_image = preprocess_image(image) |
|
|
|
if st.button("Predict"): |
|
if model_choice == "CNN": |
|
make_prediction_cnn(image, image_model) |
|
|