Upload app.py

app.py

@@ -0,0 +1,176 @@
+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)
+               
+
+
+                
+
+