Delete app.py

app.py

@@ -1,176 +0,0 @@
-import pickle
-import numpy as np 
-import streamlit as  st 
-import cv2
-import tensorflow as tf
-from tqdm import tqdm 
-from PIL import Image
-import os 
-from tensorflow.keras.preprocessing import sequence
-from tensorflow.keras.saving import load_model
-
-st.title("DL-Classifier")
-
-task = st.selectbox('Select One',("Choose any","Sentiment Classification", 'Tumor Detection'))
-
-
-#choosing tumor detection
-#CNN
-if task=="Tumor Detection":
-     st.subheader("Tumor Detection")
-     model_path = os.path.join(os.getcwd(), 'cnn_model.h5')
-     cnn_model = load_model(model_path)
-
-     img =st.file_uploader("choose the image",type=('jpg','jpeg','png'))
-     def cnn_make_prediction(img,cnn_model):
-            img=Image.open(img)
-            img=img.resize((128,128))
-            img=np.array(img)
-            input_img = np.expand_dims(img, axis=0)
-            res = cnn_model.predict(input_img)
-            if res:
-                return"Tumor"
-            else:
-               return"No Tumor"
-     #if img != None:
-         #img_f="D:/SEM 3/DL/DL-ALGORITHMS/CNN/tumor_detection/tumordata/"
-         #sub_dir=os.listdir(img_f)
-         # cel_path=os.path.join(sub_dir,img_f)DL-models/app.py
-         #cel_img=os.listdir(cel_path)
-         #img_p=cel_img + img.name
-         #pred=cnn_make_prediction(img_p,cnn_model)
-         #st.write(pred)
-
-     if img is not None:
-        
-        st.image(img, caption="Uploaded Image.", use_column_width=False, width=200)
-        st.write("")
-
-        if st.button("Detect Tumor"):
-            result =cnn_make_prediction(img, cnn_model)
-            st.subheader("Tumor Detection Result")
-            st.write(f"**{result}**")
-
-
-#choosing sentiment classification 
-
-if task=="Sentiment Classification":
-     st.subheader("Sentiment Classification")
-     clss_model= st.radio("Select Classification Model:",("RNN","DNN","Backpropagation",'Perceptron','LSTM'))
-     select_model=None
-
-     if clss_model=="RNN":
-     
-          model_path = os.path.join(os.getcwd(), 'rnn_model.h5')
-          rnn_model = load_model(model_path)
-          with open("rnn_tokeniser.pkl",'rb') as tokeniser_file:
-                rnn_tokeniser=pickle.load(tokeniser_file)
-                
-                st.subheader('RNN Spam Classification')
-                
-                input=st.text_area("Enter your message here:")
-                def rnn_pred(input):
-                    max_length=10
-                    encoded_test = rnn_tokeniser.texts_to_sequences(input)
-                    padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=max_length, padding='post') 
-                    predict= (rnn_model.predict(padded_test) > 0.5).astype("int32")
-                    if predict:
-                         return "Spam "
-                    else: 
-                         return "Not Spam"        
-                if st.button('Check'):
-                    pred=rnn_pred([input]) 
-                    st.write(pred)
-
-     if clss_model=='Perceptron':
-          with open("perceptron_model_saved.pkl",'rb') as model_file:
-               percep_model=pickle.load(model_file)
-          with open('perceptron_tokeniser_saved.pkl','rb') as model_file:
-               percep_token=pickle.load(model_file)
-          st.subheader('Perceptron Spam Classification')
-          input= st.text_area("Enter your text here")
-
-          def percep_pred(input):
-               encoded_test_p = percep_token.texts_to_sequences([input])
-               padded_test_p = tf.keras.preprocessing.sequence.pad_sequences(encoded_test_p, maxlen=10) 
-               predict_p= percep_model.predict(padded_test_p)
-               if predict_p:
-                    return "Spam"
-               else:
-                    return "Not Spam"
-          if st.button("Check"):
-               pred=percep_pred([input])
-               st.write(pred)
-
-     
-     if clss_model=="Backpropagation":
-          with open('bp_model.pkl','rb') as model_file:
-               bp_model=pickle.load(model_file)
-          with open('backrpop_tokeniser.pkl','rb') as model_file:
-               bp_tokeniser=pickle.load(model_file)
-          st.subheader('Backpropagation Spam Classification')
-          input= st.text_area("Enter your text here")
-          
-          
-          def back_pred(input):
-               encoded_test = bp_tokeniser.texts_to_sequences([input])
-               padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=10) 
-               predict= bp_model.predict(padded_test)
-               if predict:
-                    return "Spam"
-               else:
-                    return "Not Spam"
-          if st.button("Check"):
-               pred=back_pred([input])
-               st.write(pred)
-
-     if clss_model=="DNN":
-          
-          model_path = os.path.join(os.getcwd(), 'dnn_model.h5')
-          dnn_model = load_model(model_path) 
-
-          with open("dnn_tokeniser.pkl",'rb') as file:
-               dnn_tokeniser=pickle.load(file)
-          st.subheader('DNN Spam Classification')
-          input= st.text_area("Enter your text here")
-
-          def dnn_pred(input):
-               encoded_test = dnn_tokeniser.texts_to_sequences([input])
-               padded_test = tf.keras.preprocessing.sequence.pad_sequences(encoded_test, maxlen=500) 
-               predict= dnn_model.predict(padded_test)
-               if predict:
-                    return "Spam"
-               else:
-                    return "Not Spam"
-          if st.button('Check'):
-                    pred=dnn_pred([input]) 
-                    st.write(pred)
-         
-     
-     if clss_model=="LSTM":
-          model_path = os.path.join(os.getcwd(), 'lstm_model.h5')
-          lstm_model = load_model(model_path)
-
-          with open("lstm_tokeniser.pkl",'rb') as file:
-               lstm_tokeniser=pickle.load(file)
-          st.subheader('Movie Review Classification')
-          inp=st.text_area("Enter your review")
-          def lstm_make_predictions(inp, model):
-               inp = lstm_tokeniser.texts_to_sequences(inp)
-               inp = sequence.pad_sequences(inp, maxlen=500)
-               res = (model.predict(inp) > 0.5).astype("int32")
-               if res:
-                    return "Negative"
-               else:
-                    return "Positive"
-
-          
-          if st.button('Check'):
-               pred = lstm_make_predictions([inp], lstm_model)
-               st.write(pred)
-               
-
-
-                
-
-