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import pandas as pd
import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import pickle
from numpy import argmax
st.header('Demo')
task = st.selectbox('Select Task', ["Select One",'Sentiment Classification', 'Tumor Detection'])
if task == "Tumor Detection":
def cnn(img, model):
img = Image.open(img)
img = img.resize((128, 128))
img = np.array(img)
input_img = np.expand_dims(img, axis=0)
res = model.predict(input_img)
if res:
return "Tumor Detected"
else:
return "No Tumor"
cnn_model = tf.keras.models.load_model("tumor_detection_model.h5")
uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
if st.button("Submit"):
result=cnn(uploaded_file, cnn_model)
st.write(result)
elif task == "Sentiment Classification":
types = ["Perceptron","BackPropagation", "RNN","DNN", "LSTM"]
input_text2 = st.radio("Select", types, horizontal=True)
if input_text2 == "Perceptron":
with open("ppn_model.pkl",'rb') as file:
perceptron = pickle.load(file)
with open("ppn_tokeniser.pkl",'rb') as file:
ppn_tokeniser = pickle.load(file)
def ppn_make_predictions(inp, model):
encoded_inp = ppn_tokeniser.texts_to_sequences([inp])
padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500)
res = model.predict(padded_inp)
if res:
return "Negative"
else:
return "Positive"
st.subheader('Movie Review Classification using Perceptron')
inp = st.text_area('Enter message')
if st.button('Check'):
pred = ppn_make_predictions([inp], perceptron)
st.write(pred)
if input_text2 == "BackPropagation":
with open("bp_model.pkl",'rb') as file:
backprop = pickle.load(file)
with open("bp_tokeniser.pkl",'rb') as file:
bp_tokeniser = pickle.load(file)
def bp_make_predictions(inp, model):
encoded_inp = bp_tokeniser.texts_to_sequences([inp])
padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500)
res = model.predict(padded_inp)
if res:
return "Negative"
else:
return "Positive"
st.subheader('Movie Review Classification using BackPropagation')
inp = st.text_area('Enter message')
if st.button('Check'):
pred = bp_make_predictions([inp], backprop)
st.write(pred)
elif input_text2 == "RNN":
rnn_model = tf.keras.models.load_model("rnn_model.h5")
with open("spam_tokeniser.pkl", 'rb') as model_file:
rnn_tokeniser=pickle.load(model_file)
def rnn_make_predictions(inp, model):
encoded_inp = rnn_tokeniser.texts_to_sequences([inp])
padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=10, padding='post')
res = (model.predict(padded_inp) > 0.5).astype("int32")
if res:
return "Spam"
else:
return "Ham"
st.subheader('Spam message Classification using RNN')
input = st.text_area("Give message")
if st.button('Check'):
pred = rnn_make_predictions([input], rnn_model)
st.write(pred)
elif input_text2 == "DNN":
dnn_model = tf.keras.models.load_model("dnn_model.h5")
with open("dnn_tokeniser.pkl",'rb') as file:
dnn_tokeniser = pickle.load(file)
def dnn_make_predictions(inp, model):
inp = dnn_tokeniser.texts_to_sequences([inp])
inp = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500)
res = model.predict([inp])
a=argmax(res)
if a==0:
return "Negative"
else:
return "Positive"
st.subheader('Movie Review Classification using DNN')
inp = st.text_area('Enter message')
if st.button('Check'):
pred = dnn_make_predictions([inp], dnn_model)
st.write(pred)
elif input_text2 == "LSTM":
lstm_model = tf.keras.models.load_model("lstm_model.h5")
with open("lstm_tokeniser.pkl",'rb') as file:
lstm_tokeniser = pickle.load(file)
def lstm_make_predictions(inp, model):
inp = lstm_tokeniser.texts_to_sequences([inp])
inp = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500)
res = (model.predict(inp) > 0.5).astype("int32")
if res:
return "Negative"
else:
return "Positive"
st.subheader('Movie Review Classification using LSTM')
inp = st.text_area('Enter message')
if st.button('Check'):
pred = lstm_make_predictions([inp], lstm_model)
st.write(pred)
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