Update app.py

app.py

@@ -173,107 +173,3 @@ def main():
                         epochs=100,
                         bucket=2000000,
                         min_n=3,
-                        max_n=6
-                    )
-                    end_time = time.time()
-                    
-                    # Calculate the elapsed time
-                    elapsed_time = end_time - start_time
-                    st.write("Time taken: {:.2f} minutes".format(elapsed_time / 60))
-                    
-                    st.write("Model trained successfully!")
-                    
-                    # Zip the model files in memory
-                    zip_buffer = zip_model(model)
-                    
-                    # Provide download link
-                    st.download_button(
-                        label="Download Model",
-                        data=zip_buffer,
-                        file_name="fasttext_model.zip",
-                        mime="application/zip"
-                    )
-                except Exception as e:
-                    st.error(f"An error occurred: {str(e)}")
-                    st.error("Check the server logs for more details.")
-    
-    elif option == "Generate Embeddings":
-        st.header("Generate Embeddings with Pretrained FastText Model")
-        
-        # Specify the path to the model folder
-        model_folder = "Fast_text_50_dim"
-
-        # Load the model from the specified folder
-        model = load_fasttext_model(model_folder)
-
-        st.subheader("Generate Word Embedding")
-        word = st.text_input("Enter a word:")
-        if word:
-            embedding = generate_word_embedding(word, model)
-            if embedding is not None:
-                st.write(f"Embedding for '{word}':", embedding)
-            else:
-                st.write(f"'{word}' not in vocabulary")
-
-        st.subheader("Find Similar Words")
-        word_for_similar = st.text_input("Enter a word to find similar words:")
-        if word_for_similar:
-            similar_words = find_similar_words(word_for_similar, model)
-            if similar_words:
-                st.write("Similar words:")
-                for word, similarity in similar_words:
-                    st.write(f"{word}: {similarity}")
-            else:
-                st.write(f"No similar words found for '{word_for_similar}'")
-
-        st.subheader("Generate Embeddings for Words in a Sentence")
-        sentence = st.text_input("Enter a sentence:")
-        if sentence:
-            word_embeddings = generate_embeddings_for_sentence(sentence, model, r'\b\w+\b')
-            if word_embeddings:
-                for idx, embedding in enumerate(word_embeddings):
-                    st.write(f"Word {idx+1} embedding:", embedding)
-            else:
-                st.write("No embeddings could be generated for the words in the sentence.")
-
-        st.subheader("Generate Embedding for a Sentence")
-        sentence_for_embedding = st.text_input("Enter a sentence to generate its embedding:")
-        if sentence_for_embedding:
-            sentence_embedding = generate_sentence_embedding(sentence_for_embedding, model, r'\b\w+\b')
-            if sentence_embedding is not None:
-                st.write("Sentence embedding:", sentence_embedding)
-            else:
-                st.write("No embedding could be generated for the sentence.")
-
-        st.subheader("Find Most Similar Sentence Pairs")
-        uploaded_sentences_file = st.file_uploader("Upload a text file with sentences (one per line)", type=["txt"])
-        if uploaded_sentences_file:
-            sentences = uploaded_sentences_file.read().decode('utf-8').splitlines()
-            sentence_embeddings = generate_sentence_embeddings(sentences, model, r'\b\w+\b')
-            sentence_pairs = []
-            for i in range(len(sentences)):
-                for j in range(i + 1, len(sentences)):
-                    if sentence_embeddings[i] is not None and sentence_embeddings[j] is not None:
-                        similarity = cosine_similarity([sentence_embeddings[i]], [sentence_embeddings[j]])[0][0]
-                        sentence_pairs.append((sentences[i], sentences[j], similarity))
-            sentence_pairs = sorted(sentence_pairs, key=lambda x: x[2], reverse=True)
-            st.write("Most similar sentence pairs:")
-            for sent1, sent2, sim in sentence_pairs[:5]:
-                st.write(f"Sentence 1: {sent1}")
-                st.write(f"Sentence 2: {sent2}")
-                st.write(f"Similarity: {sim}")
-                st.write("-----")
-
-    # Sub-sidebar under "Generate Embeddings" option
-    if option == "Generate Embeddings":
-        st.sidebar.title("Embeddings Operations")
-        operation = st.sidebar.radio("Select an operation", ("Operation 1", "Operation 2", "Operation 3"))
-        if operation == "Operation 1":
-            st.write("You selected Operation 1")
-        elif operation == "Operation 2":
-            st.write("You selected Operation 2")
-        elif operation == "Operation 3":
-            st.write("You selected Operation 3")
-
-if __name__ == "__main__":
-    main()