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()