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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
# Load the tokenizer using pickle
with open(r'tokeniser.pkl', 'rb') as handle:
loaded_tokenizer = pickle.load(handle)
# Load saved models
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')
# Streamlit app
st.title("Classification")
# Sidebar
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)) # Adjust the shape according to your model's input shape
prediction = dnn_model.predict(preprocessed_text)
st.write("DNN Prediction:", prediction)
def predict_rnn(input_text):
# Process input text similarly to training data
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) # Using the custom Perceptron
prediction = perceptron.predict(preprocessed_text)
st.write("Custom Perceptron Prediction:", prediction)
def predict_sklearn_perceptron(preprocessed_text):
perceptron = SklearnPerceptron() # Using the sklearn Perceptron
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)
# 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"])
st.subheader("Text Input")
text_input = st.text_area("Enter Text")
if st.button("Predict"):
# Preprocess the text
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)
# Preprocess the image
preprocessed_image = preprocess_image(image)
if st.button("Predict"):
if model_choice == "CNN":
make_prediction_cnn(image, image_model)
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