Update app.py

app.py

@@ -107,113 +107,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-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
-
-DetectorFactory.seed = 0
-
-# Load models for embedding and similarity
-multi_embedding_model = SentenceTransformer('distiluse-base-multilingual-cased-v1')
-
-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=10):
-        self.lda_model = LatentDirichletAllocation(n_components=n_components, random_state=42)
-
-    def fit_transform(self, texts, lang):
-        stop_words = 'english' if lang == 'en' else 'spanish'
-        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=10):
-        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"
-
-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()
-
-            # Tokenize the input text into words
-            words = user_input.split()
-            
-            # Generate Embeddings
-            embeddings = embedding_agent.get_embeddings(words)
-            st.write("Word Embeddings Generated.")
-
-            # 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)
-
-            # Topic Modeling
-            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.write("Topics Extracted:")
-            for topic, words in topics.items():
-                st.write(f"Topic {topic}: {', '.join(words)}")
-
-            # Sentence Similarity (example with another text)
-            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()
-