adjusted visuals of the app

app.py

@@ -1,14 +1,14 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-from app_pages import page1_model_comparison, page2_rubert_toxicity, page3_gpt_model
+# from app_pages import page1_model_comparison, page2_rubert_toxicity, page3_gpt_model
 
-st.sidebar.title('Navigation')
-selection = st.sidebar.radio("Go to", ["Model Comparison", "RuBERT Toxicity Detection", "GPT Model"])
+st.title('LSTM Team Natuaral Language Processing Project')
+# selection = st.sidebar.radio("Go to", ["Model Comparison", "RuBERT Toxicity Detection", "GPT Model"])
 
-if selection == "Model Comparison":
-    page1_model_comparison.run()
-elif selection == "RuBERT Toxicity Detection":
-    page2_rubert_toxicity.run()
-elif selection == "GPT Model":
-    page3_gpt_model.run()
+# if selection == "Model Comparison":
+#     page1_model_comparison.run()
+# elif selection == "RuBERT Toxicity Detection":
+#     page2_rubert_toxicity.run()
+# elif selection == "GPT Model":
+#     page3_gpt_model.run()

app_pages/page1_model_comparison.py

@@ -1,43 +0,0 @@
-import streamlit as st
-from app_models.rubert_MODEL import classify_text
-from app_models.bag_of_words_MODEL import predict
-from app_models.lstm_MODEL import predict_review
-import time
-
-class_prefix =  'This review is likely...'
-
-def run():
-    st.title("Movie Review Classification")
-    st.write("This page will compare three models: Bag of Words/TF-IDF, LSTM, and BERT.")
-    
-    # Example placeholder for user input
-    user_input = st.text_area("")
-
-
-    if st.button('Classify with All Models'):
-        # Measure and display Bag of Words/TF-IDF prediction time
-        start_time = time.time()
-        bow_tfidf_result = predict(user_input)
-        end_time = time.time()
-        st.write(f'{class_prefix} {bow_tfidf_result} according to Bag of Words/TF-IDF. Time taken: {end_time - start_time:.2f} seconds.')
-        
-        # Measure and display LSTM prediction time
-        start_time = time.time()
-        lstm_result = predict_review(user_input)
-        end_time = time.time()
-        st.write(f'{class_prefix} {lstm_result} according to LSTM. Time taken: {end_time - start_time:.2f} seconds.')
-        
-        # Measure and display ruBERT prediction time
-        start_time = time.time()
-        rubert_result = classify_text(user_input)
-        end_time = time.time()
-        st.write(f'{class_prefix} {rubert_result} according to ruBERT. Time taken: {end_time - start_time:.2f} seconds.')
-
-
-    # Placeholder buttons for model selection
-    # if st.button('Classify with BoW/TF-IDF'):
-    #     st.write(f'{class_prefix}{predict(user_input)}')
-    # if st.button('Classify with LSTM'):
-    #     st.write(f'{class_prefix}{predict_review(user_input)}')
-    # if st.button('Classify with ruBERT'):
-    #     st.write(f'{class_prefix}{classify_text(user_input)}')

app_pages/page2_rubert_toxicity.py

@@ -1,20 +0,0 @@
-import streamlit as st
-
-from app_models.toxicity_MODEL import text2toxicity
-
-
-def run():
-    st.title('Toxicity Detection')
-    st.write('This tool classifies text as toxic or non-toxic using RuBERT.')
-
-    user_input = st.text_area("Enter text to classify", "Type your text here...")
-
-    if st.button('Classify'):
-        toxicity_score = text2toxicity(user_input)
-        st.write('Toxicity score:', toxicity_score)
-
-        # Optional: Interpret the score for the user
-        if toxicity_score > 0.5:
-            st.write("This text is likely to be considered toxic.")
-        else:
-            st.write("This text is likely to be considered non-toxic.")

app_pages/page3_gpt_model.py

@@ -1,15 +0,0 @@
-import streamlit as st
-from app_models.gpt_MODEL import generate_text
-
-
-def run():
-    st.title('GPT Text Generation')
-    prompt_text = st.text_area("Input Text", "Type here...")
-    length = st.slider("Length of Generated Text", min_value=50, max_value=500, value=200)
-    temperature = st.slider("Temperature", min_value=0.1, max_value=2.0, value=0.7, step=0.1)
-    beams = st.slider("Number of Generations", min_value=2, max_value=10, value=4, step=1)
-
-    if st.button('Generate Text'):
-        with st.spinner('Generating...'):
-            generated_text = generate_text(prompt_text, length, temperature, beams)
-            st.text_area("Generated Text", generated_text, height=250)

pages/PalanikGPT.py

@@ -0,0 +1,14 @@
+import streamlit as st
+from models.gpt_MODEL import generate_text
+
+
+st.title('GPT Text Generation')
+prompt_text = st.text_area("Input Text", "Type here...")
+length = st.slider("Length of Generated Text", min_value=50, max_value=500, value=200)
+temperature = st.slider("Temperature", min_value=0.1, max_value=2.0, value=0.7, step=0.1)
+beams = st.slider("Number of Generations", min_value=2, max_value=10, value=4, step=1)
+
+if st.button('Generate Text'):
+    with st.spinner('Generating...'):
+        generated_text = generate_text(prompt_text, length, temperature, beams)
+        st.text_area("Generated Text", generated_text, height=250)

pages/ReviewClassification.py

@@ -0,0 +1,43 @@
+import streamlit as st
+from models.rubert_MODEL import classify_text
+from models.bag_of_words_MODEL import predict
+from models.lstm_MODEL import predict_review
+import time
+
+class_prefix =  'This review is likely...'
+
+
+st.title("Movie Review Classification")
+st.write("This page will compare three models: Bag of Words/TF-IDF, LSTM, and BERT.")
+
+# Example placeholder for user input
+user_input = st.text_area("")
+
+
+if st.button('Classify with All Models'):
+    # Measure and display Bag of Words/TF-IDF prediction time
+    start_time = time.time()
+    bow_tfidf_result = predict(user_input)
+    end_time = time.time()
+    st.write(f'{class_prefix} {bow_tfidf_result} according to Bag of Words/TF-IDF. Time taken: {end_time - start_time:.2f} seconds.')
+    
+    # Measure and display LSTM prediction time
+    start_time = time.time()
+    lstm_result = predict_review(user_input)
+    end_time = time.time()
+    st.write(f'{class_prefix} {lstm_result} according to LSTM. Time taken: {end_time - start_time:.2f} seconds.')
+    
+    # Measure and display ruBERT prediction time
+    start_time = time.time()
+    rubert_result = classify_text(user_input)
+    end_time = time.time()
+    st.write(f'{class_prefix} {rubert_result} according to ruBERT. Time taken: {end_time - start_time:.2f} seconds.')
+
+
+    # Placeholder buttons for model selection
+    # if st.button('Classify with BoW/TF-IDF'):
+    #     st.write(f'{class_prefix}{predict(user_input)}')
+    # if st.button('Classify with LSTM'):
+    #     st.write(f'{class_prefix}{predict_review(user_input)}')
+    # if st.button('Classify with ruBERT'):
+    #     st.write(f'{class_prefix}{classify_text(user_input)}')

pages/ToxicCommentDetector.py

@@ -0,0 +1,19 @@
+import streamlit as st
+
+from models.toxicity_MODEL import text2toxicity
+
+
+st.title('Toxicity Detection')
+st.write('This tool classifies text as toxic or non-toxic using RuBERT.')
+
+user_input = st.text_area("Enter text to classify", "Type your text here...")
+
+if st.button('Classify'):
+    toxicity_score = text2toxicity(user_input)
+    st.write('Toxicity score:', toxicity_score)
+
+    # Optional: Interpret the score for the user
+    if toxicity_score > 0.5:
+        st.write("This text is likely to be considered toxic.")
+    else:
+        st.write("This text is likely to be considered non-toxic.")