Delete chck.py

chck.py

@@ -1,80 +0,0 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.applications.inception_v3 import preprocess_input
-import tensorflow as tf
-import joblib
-
-# Load saved models
-image_model = load_model('tumor_detection_model.h5')
-dnn_model = load_model('sms_spam_detection_dnnmodel.h5')
-rnn_model = load_model('spam_detection_rnn_model.h5')
-perceptron_model = joblib.load('imdb_perceptron_model.pkl')
-backprop_model = joblib.load('backprop_model.pkl')
-LSTM_model = load_model('imdb_LSTM.h5')
-
-# Streamlit app
-st.title("Classification")
-
-# Sidebar
-task = st.sidebar.selectbox("Select Task", ["Tumor Detection", "Sentiment Classification"])
-
-def preprocess_message_dnn(message, tokeniser, max_length):
-    encoded_message = tokeniser.texts_to_sequences([message])
-    padded_message = pad_sequences(encoded_message, maxlen=max_length, padding='post')
-    return padded_message
-
-def predict_dnnspam(message, tokeniser, max_length):
-    processed_message = preprocess_message_dnn(message, tokeniser, max_length)
-    prediction = dnn_model.predict(processed_message)
-    return "Spam" if prediction >= 0.5 else "Ham"
-
-# Other prediction functions for sentiment analysis can follow a similar pattern
-
-# Function for CNN prediction
-def preprocess_image(image):
-    image = image.resize((299, 299))
-    image_array = np.array(image)
-    preprocessed_image = preprocess_input(image_array)
-    return preprocessed_image
-
-def make_prediction_cnn(image, model):
-    img = image.resize((128, 128))
-    img_array = np.array(img)
-    img_array = img_array.reshape((1, img_array.shape[0], img_array.shape[1], img_array.shape[2]))
-    preprocessed_image = preprocess_input(img_array)
-    prediction = model.predict(preprocessed_image)
-    return "Tumor Detected" if prediction > 0.5 else "No Tumor"
-
-if task == "Sentiment Classification":
-    st.subheader("Choose Model")
-    model_choice = st.radio("Select Model", ["DNN", "RNN", "Perceptron", "Backpropagation", "LSTM"])
-
-    st.subheader("Text Input")
-    text_input = st.text_area("Enter Text")
-
-    if st.button("Predict"):
-        if model_choice == "DNN":
-            # You need to define tokeniser and max_length for DNN model
-            prediction_result = predict_dnnspam(text_input, tokeniser, max_length)
-            st.write(f"The message is classified as: {prediction_result}")
-        # Other model choices should call respective prediction functions similarly
-
-else:
-    st.subheader("Choose Model")
-    model_choice = st.radio("Select Model", ["CNN"])
-
-    st.subheader("Image Input")
-    image_input = st.file_uploader("Choose an image...", type="jpg")
-
-    if image_input is not None:
-        image = Image.open(image_input)
-        st.image(image, caption="Uploaded Image.", use_column_width=True)
-
-        if st.button("Predict"):
-            if model_choice == "CNN":
-                prediction_result = make_prediction_cnn(image, image_model)
-                st.write(prediction_result)