Spaces:
Sleeping
Sleeping
File size: 5,935 Bytes
7967a16 f256088 7967a16 c72100d 7967a16 c72100d a9512f0 d421498 7967a16 d421498 7967a16 d421498 7967a16 c72100d 7967a16 c72100d 7967a16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
import streamlit as st
import pickle
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
import pandas as pd
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.text import Tokenizer
from BackPropogation import BackPropogation
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import save_model,load_model
def cnn_tumor(img):
img=Image.open(img)
img=np.array(img)
img=cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
img_array = cv2.medianBlur(img, 5)
img = Image.fromarray(cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB))
img=img.resize((128,128))
input_img = np.expand_dims(img, axis=0)
st.image(input_img, caption='Processed Image', use_column_width=True)
loaded_model = load_model('cnn_model.h5')
predictions = loaded_model.predict(input_img)
if predictions:
st.write("Tumor Detected")
else:
st.write("No Tumor")
def perceptron():
with open('imdb_perceptron.pkl', 'rb') as file:
loaded_model = pickle.load(file)
top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
word_index = imdb.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
user_input = st.text_input("Enter your movie review:")
if st.button("Predict"):
if user_input:
st.write("Review:", user_input)
user_input_sequence = [word_index.get(word, 0) for word in user_input.split()]
processed_input = tf.keras.preprocessing.sequence.pad_sequences([user_input_sequence], maxlen=500, padding='post', truncating='post')
prediction = loaded_model.predict(processed_input)
sentiment = 'Positive' if prediction[0] > 0.5 else 'Negative'
st.write("Predicted Sentiment:", sentiment)
else:
st.warning("Please enter a movie review.")
def backprop():
with open('BackP.pkl', 'rb') as file:
loaded_model = pickle.load(file)
top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
word_index = imdb.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
user_input = st.text_input("Enter your movie review:")
if st.button("Predict"):
if user_input:
st.write("Review:", user_input)
user_input_sequence = [word_index.get(word, 0) for word in user_input.split()]
processed_input = tf.keras.preprocessing.sequence.pad_sequences([user_input_sequence], maxlen=500, padding='post', truncating='post')
prediction = loaded_model.predict(processed_input)
sentiment = 'Positive' if prediction[0] > 0.5 else 'Negative'
st.write("Predicted Sentiment:", sentiment)
else:
st.warning("Please enter a movie review.")
def rnn_model():
loaded_model = load_model('rnn_model.h5')
user_input_sequence = st.text_area("Enter your text message:")
if st.button('Predict'):
if user_input_sequence:
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts([user_input_sequence])
sequences = tokenizer.texts_to_sequences([user_input_sequence])
processed_input = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=10, padding='post', truncating='post')
prediction = loaded_model.predict(np.array(processed_input))
is_spam = 'Spam' if prediction[0] > 0.5 else 'Not Spam'
st.write("Predicted Label:", is_spam)
else:
st.warning("Please enter a text message.")
def lstm_model():
user_input = st.text_input('Enter a sentence:', 'I love this movie!')
tokenizer = Tokenizer(num_words=10000, oov_token='<OOV>')
tokenizer.fit_on_texts([user_input])
sequences = tokenizer.texts_to_sequences([user_input])
padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=500, padding='post', truncating='post')
loaded_model = load_model('lstm_model.h5')
if st.button('Predict Sentiment'):
prediction = loaded_model.predict(np.array(padded_sequence))
sentiment = 'Positive' if prediction > 0.5 else 'Negative'
st.success(f'Sentiment: {sentiment}, Confidence: {prediction[0][0]:.4f}')
def dnn_model():
user_input = st.text_input('Enter a sentence:', 'I love this movie!')
tokenizer = Tokenizer(num_words=10000, oov_token='<OOV>')
tokenizer.fit_on_texts([user_input])
sequences = tokenizer.texts_to_sequences([user_input])
padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=500, padding='post', truncating='post')
loaded_model = load_model('dnn_model.h5')
if st.button('Predict Sentiment'):
prediction = loaded_model.predict(np.array(padded_sequence))
sentiment = 'Positive' if prediction > 0.5 else 'Negative'
st.success(f'Sentiment: {sentiment}, Confidence: {prediction[0][0]:.4f}')
st.title('Model Prediction')
option = st.selectbox("Choose One",['Tumor Detection','Sentiment Classification'])
if option=='Tumor Detection':
st.title('CNN Tumor Detection Model')
img=st.file_uploader("Upload your file here...", type=['png', 'jpeg', 'jpg'])
cnn_tumor(img)
else:
opt=st.radio("Select your prediction",key="visibility",options=["Perceptron",'Backpropogation','DNN','RNN','LSTM'])
if opt=="Perceptron":
st.title('Perceptron Model')
perceptron()
elif opt=="Backpropogation":
st.title('Backpropogation Model')
backprop()
elif opt=='RNN':
st.title('RNN Spam Detection')
rnn_model()
elif opt=='LSTM':
st.title('LSTM Model')
lstm_model()
else:
st.title('DNN Model')
dnn_model() |