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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}**")
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