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.gitattributes

@@ -33,3 +33,4 @@ 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
+LS.keras filter=lfs diff=lfs merge=lfs -text

BP.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff7a191c4476cc22a864ea6db1b84c43f72d25a45ad4fb60a9e482c6b261f1bf
+size 4300

CN.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:044f4a3e7ce2ec824e453b130efa2ca2174aa6505d6c7f41a6a3455f5185eb48
+size 391811360

DP.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff1c90c2ee58ab7499b7c136dfc284e403a19fb0091672e8b51e6ffc5bde7578
+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

RN.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58d8eb5511fb98d6791afb55d1d4ce2700cc80272bf8f3117f4b1587dade831c
+size 1548440

Streamlit.py

@@ -0,0 +1,162 @@
+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)
+    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")
+st.sidebar.header("Options")
+
+# Choose between tasks
+task = st.sidebar.radio("Select Task", ("Sentiment Classification", "Tumor Detection"))
+
+if task == "Sentiment Classification":
+    st.subheader("Sentiment Classification")
+    # Input box for new review
+    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.keras')
+        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}**")
+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)
+    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")
+st.sidebar.header("Options")
+
+# Choose between tasks
+task = st.sidebar.radio("Select Task", ("Sentiment Classification", "Tumor Detection"))
+
+if task == "Sentiment Classification":
+    st.subheader("Sentiment Classification")
+    # Input box for new review
+    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.h5')
+        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}**")

imdb_backpropogation.py

@@ -0,0 +1,21 @@
+from tensorflow.keras.datasets import imdb
+from BackPropogation import BackPropogation
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from sklearn.metrics import accuracy_score
+import pickle
+
+top_words = 5000
+(X_train, y_train), (X_test,y_test) = imdb.load_data(num_words=top_words)
+
+max_review_length = 500
+
+X_train = pad_sequences(X_train, maxlen=max_review_length)
+X_test = pad_sequences(X_test, maxlen=max_review_length)
+
+backprop = BackPropogation(epochs=100,learning_rate=0.01,activation_function='sigmoid')
+backprop.fit(X_train, y_train)
+pred = backprop.predict(X_test)
+print(f"Accuracy : {accuracy_score(pred, y_test)}")
+
+with open(r'C:\Users\Sreya\Desktop\deeplearning\Predictionsdl\Deep-Prediction-Hub\BP.pkl','wb') as file:
+    pickle.dump(backprop, file)

imdb_perceptron.py

@@ -0,0 +1,22 @@
+from tensorflow.keras.datasets import imdb
+from Perceptron import  Perceptron
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from sklearn.metrics import accuracy_score
+import pickle
+
+top_words = 5000
+(X_train, y_train), (X_test,y_test) = imdb.load_data(num_words=top_words)
+
+max_review_length = 500
+X_train = pad_sequences(X_train, maxlen=max_review_length)
+X_test = pad_sequences(X_test, maxlen=max_review_length)
+
+percep = Perceptron(epochs=100)
+
+percep.fit(X_train, y_train)
+pred = percep.predict(X_test)
+
+print(f"Accuracy : {accuracy_score(pred, y_test)}")
+
+with open(r'C:\Users\Sreya\Desktop\deeplearning\Predictionsdl\Deep-Prediction-Hub\PP.pkl','wb') as file:
+    pickle.dump(percep, file)

requirements.txt

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