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app.py

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+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 PERCEPTRON model
+def load_perceptron_model():
+    with open('PP_model.pkl', 'rb') as file:
+        model = pickle.load(file)
+    return model
+# Load BACKPROPAGATION model
+def load_backpropagation_model():
+    with open('BP_model.pkl', 'rb') as file:
+        model = pickle.load(file)
+    return model
+
+# Load DNN model
+def load_dnn_model():
+    model_path = 'DNN_model.h5'
+    model = load_model(model_path)
+    return model
+
+# Load RNN model
+def load_rnn_model():
+    model_path = 'RNN_model.h5'
+    model = load_model(model_path)
+    return model
+
+# Load LSTM model
+def load_lstm_model():
+    model_path = 'LSTM_model.h5'
+    model = load_model(model_path)
+    return model
+
+# Load CNN model
+def load_cnn_model():
+    model_path = 'CNN_model.h5'
+    model = load_model(model_path)
+    return model
+
+# 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("Sentimental Analysis and Tumor Detection")
+
+# Choose between tasks
+task = st.radio("Select Task", ("Sentiment Classification", "Tumor Detection"))
+
+if task == "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
+        loaded_model = None  # Initialize loaded_model variable
+        if model_option == "Perceptron":
+            loaded_model = load_perceptron_model()
+        elif model_option == "BackPropagation":
+            loaded_model = load_backpropagation_model()
+        elif model_option == "DNN":
+            loaded_model = load_dnn_model()
+        elif model_option == "RNN":
+            loaded_model = load_rnn_model()
+        elif model_option == "LSTM":
+            loaded_model = load_lstm_model()
+
+        if loaded_model is not None:
+            if st.button("Classify Sentiment"):
+                result = sentiment_classification(new_review_text, loaded_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_cnn_model()
+        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}**")

requirements.txt

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+streamlit
+numpy
+Pillow
+tensorflow
+tqdm
+opencv-python
+scikit-learn