Update app.py

app.py

@@ -1,110 +1,33 @@
 import streamlit as st
-from sentence_transformers import SentenceTransformer, util
-from sklearn.decomposition import LatentDirichletAllocation
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.manifold import TSNE
+from sentence_transformers import SentenceTransformer
 from langdetect import detect, DetectorFactory
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
 
-st.set_page_config(page_title="Multilingual Text Analysis System", layout="wide")
+st.set_page_config(page_title="Simple Text Analysis", layout="wide")
 
 @st.cache_resource
 def load_model():
     return SentenceTransformer('distiluse-base-multilingual-cased-v1')
 
 DetectorFactory.seed = 0
-multi_embedding_model = load_model()
+model = load_model()
 
-class WordEmbeddingAgent:
-    def __init__(self, model):
-        self.model = model
-
-    def get_embeddings(self, words):
-        return self.model.encode(words)
-
-class SimilarityAgent:
-    def __init__(self, model):
-        self.model = model
-
-    def compute_similarity(self, text1, text2):
-        embedding1 = self.model.encode(text1, convert_to_tensor=True)
-        embedding2 = self.model.encode(text2, convert_to_tensor=True)
-        return util.pytorch_cos_sim(embedding1, embedding2).item()
-
-class TopicModelingAgent:
-    def __init__(self, n_components=5):
-        self.lda_model = LatentDirichletAllocation(n_components=n_components, random_state=42)
-
-    def fit_transform(self, texts, lang):
-        stop_words = 'english' if lang == 'en' else None
-        vectorizer = CountVectorizer(max_df=0.9, min_df=2, stop_words=stop_words)
-        dtm = vectorizer.fit_transform(texts)
-        self.lda_model.fit(dtm)
-        return self.lda_model.transform(dtm), vectorizer
-
-    def get_topics(self, vectorizer, num_words=5):
-        topics = {}
-        for idx, topic in enumerate(self.lda_model.components_):
-            topics[idx] = [vectorizer.get_feature_names_out()[i] for i in topic.argsort()[-num_words:]]
-        return topics
-
-def detect_language(text):
-    try:
-        return detect(text)
-    except:
-        return "unknown"
-
-@st.cache_data
-def tsne_visualization(embeddings, words):
-    tsne = TSNE(n_components=2, random_state=42)
-    embeddings_2d = tsne.fit_transform(embeddings)
-    df = pd.DataFrame(embeddings_2d, columns=['x', 'y'])
-    df['word'] = words
-    return df
-
-st.title("Multilingual Text Analysis System")
+st.title("Simple Text Analysis")
 user_input = st.text_area("Enter your text here:")
 
-if st.button("Analyze") or user_input:
+if st.button("Analyze"):
     if user_input:
-        lang = detect_language(user_input)
-        st.write(f"Detected language: {lang}")
-
-        embedding_agent = WordEmbeddingAgent(multi_embedding_model)
-        similarity_agent = SimilarityAgent(multi_embedding_model)
-        topic_modeling_agent = TopicModelingAgent()
-
-        words = user_input.split()
-        
-        with st.spinner("Generating word embeddings..."):
-            embeddings = embedding_agent.get_embeddings(words)
-        st.success("Word Embeddings Generated.")
-
-        with st.spinner("Creating t-SNE visualization..."):
-            tsne_df = tsne_visualization(embeddings, words)
-            fig, ax = plt.subplots()
-            ax.scatter(tsne_df['x'], tsne_df['y'])
-            for i, word in enumerate(tsne_df['word']):
-                ax.annotate(word, (tsne_df['x'][i], tsne_df['y'][i]))
-            st.pyplot(fig)
-
-        with st.spinner("Extracting topics..."):
-            texts = [user_input, "Another text to improve topic modeling."]
-            topic_distr, vectorizer = topic_modeling_agent.fit_transform(texts, lang)
-            topics = topic_modeling_agent.get_topics(vectorizer)
-            st.subheader("Topics Extracted:")
-            for topic, words in topics.items():
-                st.write(f"Topic {topic}: {', '.join(words)}")
-
-        with st.spinner("Computing similarity..."):
-            text2 = "Otro texto de ejemplo para comparación de similitud." if lang != 'en' else "Another example text for similarity comparison."
-            similarity_score = similarity_agent.compute_similarity(user_input, text2)
-            st.write(f"Similarity Score with example text: {similarity_score:.4f}")
-
+        try:
+            lang = detect(user_input)
+            st.write(f"Detected language: {lang}")
+            
+            embedding = model.encode(user_input)
+            st.write(f"Text embedding shape: {embedding.shape}")
+            st.write("First few values of the embedding:")
+            st.write(embedding[:5])
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
     else:
         st.warning("Please enter some text to analyze.")
 
 st.sidebar.title("About")
-st.sidebar.info("This app performs multilingual text analysis using various NLP techniques.")
+st.sidebar.info("This is a simple text analysis app.")