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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 langdetect import detect, DetectorFactory
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
@st.cache_resource
def load_model():
return SentenceTransformer('distiluse-base-multilingual-cased-v1')
DetectorFactory.seed = 0
multi_embedding_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
def main():
st.title("Multilingual Text Analysis System")
user_input = st.text_area("Enter your text here:")
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}")
else:
st.warning("Please enter some text to analyze.")
if __name__ == "__main__":
main() |