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BackPropogation.py

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+import numpy as np
+from tqdm import tqdm
+
+
+class BackPropogation:
+    def __init__(self,learning_rate=0.01, epochs=100,activation_function='step'):
+        self.bias = 0
+        self.learning_rate = learning_rate
+        self.max_epochs = epochs
+        self.activation_function = activation_function
+
+
+    def activate(self, x):
+        if self.activation_function == 'step':
+            return 1 if x >= 0 else 0
+        elif self.activation_function == 'sigmoid':
+            return 1 if (1 / (1 + np.exp(-x)))>=0.5 else 0
+        elif self.activation_function == 'relu':
+            return 1 if max(0,x)>=0.5 else 0
+
+    def fit(self, X, y):
+        error_sum=0
+        n_features = X.shape[1]
+        self.weights = np.zeros((n_features))
+        for epoch in tqdm(range(self.max_epochs)):
+            for i in range(len(X)):
+                inputs = X[i]
+                target = y[i]
+                weighted_sum = np.dot(inputs, self.weights) + self.bias
+                prediction = self.activate(weighted_sum)
+                
+                # Calculating loss and updating weights.
+                error = target - prediction
+                self.weights += self.learning_rate * error * inputs
+                self.bias += self.learning_rate * error
+                
+            print(f"Updated Weights after epoch {epoch} with {self.weights}")
+        print("Training Completed")
+
+    def predict(self, X):
+        predictions = []
+        for i in range(len(X)):
+            inputs = X[i]
+            weighted_sum = np.dot(inputs, self.weights) + self.bias
+            prediction = self.activate(weighted_sum)
+            predictions.append(prediction)
+        return predictions
+
+
+    
+    
+    
+ 

app.py

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+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 tensorflow.keras.models import save_model, load_model
+from BackPropogation import BackPropogation
+from sklearn.model_selection import train_test_split
+
+
+
+
+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='Image Processing', use_column_width=True)
+    loaded_model = tf.keras.models.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('perceptron_model.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('back-propagation_model.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 dnn_model():
+    user_input = st.text_input('Enter a sentence:')
+    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 = tf.keras.models.load_model('dnn_model.h5')
+    if st.button('Predict'):
+            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 rnn_model():
+    loaded_model = tf.keras.models.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:')
+    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 = tf.keras.models.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}')  
+
+
+
+
+st.title('Model Prediction')
+option = st.selectbox("Select 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=='DNN':
+        st.title('DNN Text Classification')
+        dnn_model()
+    else:
+        st.title('LSTM Sentiment Analysis')
+        lstm_model()

back-propagation_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25ea2c6a11d5e28877ca644fe12d8da841b60df39cf1defffe654f23a1100530
+size 4300

dnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:681d680affa87af6fe68547754edd9e7209e6cabcaf3c4d7ba5d2c7cf236cf01
+size 10735120