Added buttons

pages/📷 CritiSense.py

@@ -16,20 +16,51 @@ def clean(text):
     text = text.translate(str.maketrans('', '', string.punctuation))
     return text
 
-# Загрузка весов модели
+# Загрузка весов модели и векторизатора
+def load_model_ml() : # return model
+    model_filename = 'model_weights.pkl'
+    with open(model_filename, 'rb') as file:
+        model = pickle.load(file)
 
-model_filename = 'model_weights.pkl'
-with open(model_filename, 'rb') as file:
-    model = pickle.load(file)
+    vectorizer = CountVectorizer()
+    vectorizer_filename = 'vectorizer_weights.pkl'
+    with open(vectorizer_filename, 'rb') as file:
+        vectorizer = pickle.load(file)
+    
+    return model, vectorizer
 
-# Загрузка весов векторизатора
-vectorizer = CountVectorizer()
-vectorizer_filename = 'vectorizer_weights.pkl'
-with open(vectorizer_filename, 'rb') as file:
-    vectorizer = pickle.load(file)
+def predict_ml(model, vectorizer, user_review) :
+    user_review_clean = clean(user_review)
+    user_features = vectorizer.transform([user_review_clean])
+    start_ml=time.time()
+    prediction = model.predict(user_features)
+    end_ml=time.time()
+    st.write("Review:", user_review)
+    ml_time=end_ml-start_ml
 
-# Само приложение  
+    return prediction, ml_time
+
+#Placeholder for RNN
+def load_model_rnn() : # return model
+    return # model
+
+#Placeholder for RNN
+def predict_rnn(model, user_review) : 
+    prediction = 1
+    time = 0
+    return prediction, time
 
+#Placeholder for BERT
+def load_model_bert() : # return model
+    return # model
+
+#Placeholder for BERT
+def predict_bert(model, user_review) : 
+    prediction = 1
+    time = 0
+    return prediction, time
+
+# Само приложение  
 st.title("CritiSense")
 st.subheader("Movie Review Sentiment Analyzer")
 st.write("CritiSense is a powerful app that analyzes the sentiment of movie reviews.") 
@@ -37,21 +68,44 @@ st.write("Whether you want to know if a review is positive or negative, CritiSen
 st.write("Just enter the review, and our app will provide you with instant sentiment analysis.")
 st.write("Make informed decisions about movies with CritiSense!")
 user_review = st.text_input("Enter your review:", "")
-user_review_clean = clean(user_review)
-user_features = vectorizer.transform([user_review_clean])
-start_ml=time.time()
-prediction = model.predict(user_features)
-end_ml=time.time()
-st.write("Review:", user_review)
-ml_time=end_ml-start_ml
-
-execution_time_container = st.empty()  # Создаем пустой контейнер для отображения времени выполнения
-
-if st.button("Analyze Sentiment"):
-    if prediction == 1:
+
+# Создаем пустой контейнер для отображения времени выполнения
+execution_time_container = st.empty()
+if st.button("Analyze Sentiment using ML"):
+    ml_model, ml_vectorizer = load_model_ml()
+    ml_prediction, ml_time = predict_ml(ml_model, ml_vectorizer, user_review)
+
+    if ml_prediction == 1:
+        st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
+    else:
+        st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)
+
+    st.markdown(f"Execution Time: {ml_time:.5f} seconds")
+    execution_time_container.text(f"Execution Time: {ml_time:.5f} seconds")
+
+st.divider()
+
+if st.button("Analyze Sentiment using RNN"):
+    rnn_model = load_model_rnn()
+    rnn_prediction, rnn_time = predict_rnn(rnn_model, user_review)
+
+    if rnn_prediction == 1:
+        st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
+    else:
+        st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)
+    st.markdown(f"Execution Time: {rnn_time:.5f} seconds")
+    execution_time_container.text(f"Execution Time: {rnn_time:.5f} seconds")
+
+st.divider()
+
+if st.button("Analyze Sentiment using Bert"):
+    bert_model = load_model_bert()
+    bert_prediction, bert_time = predict_bert(bert_model, user_review)
+
+    if bert_prediction == 1:
         st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
     else:
         st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)
-st.markdown(f"Execution Time: {ml_time:.5f} seconds")
-execution_time_container.text(f"Execution Time: {ml_time:.5f} seconds")
+    st.markdown(f"Execution Time: {bert_time:.5f} seconds")
+    execution_time_container.text(f"Execution Time: {bert_time:.5f} seconds")