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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+DP.keras filter=lfs diff=lfs merge=lfs -text
+LS.keras filter=lfs diff=lfs merge=lfs -text
+RN.keras filter=lfs diff=lfs merge=lfs -text

BP.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff7a191c4476cc22a864ea6db1b84c43f72d25a45ad4fb60a9e482c6b261f1bf
+size 4300

BackPropogation.py

@@ -0,0 +1,53 @@
+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
+
+
+    
+    
+    
+ 

CN.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a91f84315ff09930a72ecce75814e2267433d1eb19760ce18d5b24514f4a8ff1
+size 391811360

DP.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:effe5efada6ccfaab2dc6ce3e189954b9c9b3abbaf86b2bdbe1f11d18d3684f0
+size 10735120

LS.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:705ae60751f9f288daf9486f5b3535e437fd7aabb96c5b79f908e7f5e68c9b02
+size 4194296

PP.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d490b9361db03dbd503b6a1424976d05a9865eefe505327b2ad342b989737eba
+size 2264

Perceptron.py

@@ -0,0 +1,46 @@
+import numpy as np
+from tqdm import tqdm
+
+
+class Perceptron:
+    
+    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):
+        n_features = X.shape[1]
+        self.weights = np.random.randint(n_features, size=(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)
+        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
+
+
+    
+    
+    
+ 

RN.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56f772e386a259788dabcb7189fbe4327b3a31924fd0104e9d52c1c626101262
+size 1548448

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+numpy
+Pillow
+tensorflow
+tqdm

streamlit1.py

@@ -0,0 +1,79 @@
+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}**")