Update app.py

app.py

@@ -15,7 +15,7 @@ if task=='Tumor Detection':
     # CNN
     #with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\CNN\cnn_model.pkl",'rb') as file:
         #cnn_model = pickle.load(file)
-    cnn_model = load_model(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\CNN\cnn_model1.h5")
+    cnn_model = load_model("cnn_model1.h5")
 
     img = st.file_uploader('Upload image', type=['jpeg', 'jpg', 'png'])
 
@@ -44,9 +44,9 @@ if task=='Sentiment Classification':
 
     if arc == arcs[0]:
         # Perceptron
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\Perceptron\pnn_model.pkl",'rb') as file:
+        with open("pnn_model.pkl",'rb') as file:
             perceptron = pickle.load(file)
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\Perceptron\pnn_tokeniser.pkl",'rb') as file:
+        with open("pnn_tokeniser.pkl",'rb') as file:
             ppn_tokeniser = pickle.load(file)
 
         def ppn_make_predictions(inp, model):
@@ -66,9 +66,9 @@ if task=='Sentiment Classification':
 
     elif arc == arcs[1]:
         # BackPropogation
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\BackPropagation\bpn_model.pkl",'rb') as file:
+        with open("bpn_model.pkl",'rb') as file:
             backprop = pickle.load(file)
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\BackPropagation\bpn_tokeniser.pkl",'rb') as file:
+        with open("bpn_tokeniser.pkl",'rb') as file:
             bp_tokeniser = pickle.load(file)
 
         def bp_make_predictions(inp, model):
@@ -89,9 +89,9 @@ if task=='Sentiment Classification':
 
     elif arc == arcs[2]:
         # DNN
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\DNN\dnn_model.pkl",'rb') as file:
+        with open("dnn_model.pkl",'rb') as file:
             dnn_model = pickle.load(file)
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\DNN\dnn_tokeniser.pkl",'rb') as file:
+        with open("dnn_tokeniser.pkl",'rb') as file:
             dnn_tokeniser = pickle.load(file)
 
         def dnn_make_predictions(inp, model):
@@ -112,10 +112,10 @@ if task=='Sentiment Classification':
 
     elif arc == arcs[3]:
         # RNN 
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\RNN\rnn_model.pkl",'rb') as file:
+        with open("rnn_model.pkl",'rb') as file:
             rnn_model = pickle.load(file) 
 
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\RNN\rnn_tokeniser.pkl",'rb') as file:
+        with open("rnn_tokeniser.pkl",'rb') as file:
             rnn_tokeniser = pickle.load(file)
 
         def rnn_make_predictions(inp, model):
@@ -136,10 +136,10 @@ if task=='Sentiment Classification':
 
     elif arc == arcs[4]:
         # LSTM
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\LSTM\lstm_model.pkl",'rb') as file:
+        with open("lstm_model.pkl",'rb') as file:
             lstm_model = pickle.load(file) 
 
-        with open(r"E:\DUK\DUKSEM3\DEEP_LEARNING\ASSIGN1\Multi-Modal_classifier_Image_Classification_Sentiment_Sentiment_Analysis\LSTM\lstm_tokeniser.pkl",'rb') as file:
+        with open("lstm_tokeniser.pkl",'rb') as file:
             lstm_tokeniser = pickle.load(file)
 
         def lstm_make_predictions(inp, model):