Tumor_Imdb_app / App.py

Update App.py

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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)