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assignment.py

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+import pandas as pd
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import pickle
+
+
+st.header('Demo')
+task = st.selectbox('Select Task', ["Select One",'Sentiment Classification', 'Tumor Detection'])
+
+
+if task == "Tumor Detection":
+        def cnn(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)
+            if res:
+                return "Tumor Detected"
+            else:
+                return "No Tumor" 
+            
+        cnn_model = tf.keras.models.load_model("tumor_detection_model.h5")
+        uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"])
+        if uploaded_file is not None:
+            st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+            if st.button("Submit"):
+                result=cnn(uploaded_file, cnn_model)
+                st.write(result)
+
+        
+elif task == "Sentiment Classification":
+        types = ["Perceptron","BackPropagation", "RNN","DNN", "LSTM"]
+        input_text2 = st.radio("Select", types, horizontal=True)
+
+        if input_text2 == "Perceptron":
+                with open("ppn_model.pkl",'rb') as file:
+                    perceptron = pickle.load(file)
+                with open("ppn_tokeniser.pkl",'rb') as file:
+                    ppn_tokeniser = pickle.load(file)
+
+                def ppn_make_predictions(inp, model):
+                    encoded_inp = ppn_tokeniser.texts_to_sequences([inp])
+                    padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500)
+                    res = model.predict(padded_inp)
+                    if res:
+                        return "Negative"
+                    else:
+                        return "Positive"       
+                
+                st.subheader('Movie Review Classification using Perceptron')
+                inp = st.text_area('Enter message')
+                if st.button('Check'):
+                    pred = ppn_make_predictions([inp], perceptron)
+                    st.write(pred)
+
+        if input_text2 == "BackPropagation":
+                with open("bp_model.pkl",'rb') as file:
+                    backprop = pickle.load(file)
+                with open("bp_tokeniser.pkl",'rb') as file:
+                    bp_tokeniser = pickle.load(file)
+
+                def bp_make_predictions(inp, model):
+                    encoded_inp = bp_tokeniser.texts_to_sequences([inp])
+                    padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=500)
+                    res = model.predict(padded_inp)
+                    if res:
+                        return "Negative"
+                    else:
+                        return "Positive"     
+                       
+                st.subheader('Movie Review Classification using BackPropagation')
+                inp = st.text_area('Enter message')
+                if st.button('Check'):
+                    pred = bp_make_predictions([inp], backprop)
+                    st.write(pred)
+        
+
+        elif input_text2 == "RNN":
+                with open("spam_model.pkl", 'rb') as model_file:
+                    rnn_model=pickle.load(model_file)
+                with open("spam_tokeniser.pkl", 'rb') as model_file:
+                    rnn_tokeniser=pickle.load(model_file)
+
+                def rnn_make_predictions(inp, model):
+                    encoded_inp = rnn_tokeniser.texts_to_sequences([inp])
+                    padded_inp = tf.keras.preprocessing.sequence.pad_sequences(encoded_inp, maxlen=10, padding='post')
+                    res = (model.predict(padded_inp) > 0.5).astype("int32")
+                    if res:
+                        return "Spam"
+                    else:
+                        return "Ham"
+
+                st.subheader('Spam message Classification using RNN')
+                input = st.text_area("Give message")
+                if st.button('Check'):
+                    pred = rnn_make_predictions([input], rnn_model)
+                    st.write(pred)
+
+
+
+        elif input_text2 == "DNN":
+                        with open("dnn_model.pkl",'rb') as file:
+                            dnn_model = pickle.load(file)
+                        with open("dnn_tokeniser.pkl",'rb') as file:
+                            dnn_tokeniser = pickle.load(file)
+
+                        def dnn_make_predictions(inp, model):
+
+                            inp = dnn_tokeniser.texts_to_sequences([inp])
+                            inp = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500)
+                            res = model.predict([inp]) 
+                            if res:
+                                return "Negative"
+                            else:
+                                return "Positive"       
+                        
+                        st.subheader('Movie Review Classification using DNN')
+                        inp = st.text_area('Enter message')
+                        if st.button('Check'):
+                            pred = dnn_make_predictions([inp], dnn_model)
+                            st.write(pred)
+
+                            
+
+        elif input_text2 == "LSTM":
+                with open("lstm_model.pkl",'rb') as file:
+                    lstm_model = pickle.load(file) 
+
+                with open("lstm_tokeniser.pkl",'rb') as file:
+                    lstm_tokeniser = pickle.load(file)
+
+                def lstm_make_predictions(inp, model):
+                    inp = lstm_tokeniser.texts_to_sequences([inp])
+                    inp = tf.keras.preprocessing.sequence.pad_sequences(inp, maxlen=500)
+                    res = (model.predict(inp) > 0.5).astype("int32")
+                    if res:
+                        return "Negative"
+                    else:
+                        return "Positive"
+                st.subheader('Movie Review Classification using LSTM')
+                inp = st.text_area('Enter message')
+                if st.button('Check'):
+                    pred = lstm_make_predictions([inp], lstm_model)
+                    st.write(pred)  
+
+
+
+
+                
+
+
+
+

bp_model.pkl

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+size 4300

bp_tokeniser.pkl

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+size 4992453

dnn_model.pkl

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dnn_tokeniser.pkl

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lstm_model.pkl

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lstm_tokeniser.pkl

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ppn_model.pkl

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ppn_tokeniser.pkl

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spam_model.pkl

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+size 2255782

spam_tokeniser.pkl

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tumor_detection_model.h5

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+size 391811360

tumor_detection_model.pkl

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