import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from shiny import App, render, ui

# Initialize the sentence transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Sample queries
queries = [
    "What is the weather today?",
    "How to learn Python?",
    "Best practices for data science.",
    "What is the capital of France?",
    "How to cook pasta?",
    "Latest trends in technology.",
    "What is machine learning?",
    "Tips for healthy living.",
    "How to invest in stocks?",
    "Best programming languages to learn.",
    "How to start a business?",
    "What are the benefits of exercise?",
    "History of the internet.",
    "How to improve communication skills?",
    "Understanding blockchain technology.",
    "What is artificial intelligence?",
    "Effective study techniques.",
    "Travel tips for Europe.",
    "How to write a resume?",
    "What is the best way to learn math?"
]

# Precompute embeddings for the queries
query_embeddings = model.encode(queries)

# Define the UI
app_ui = ui.page_fluid(
    ui.h2("Sentence Similarity Finder"),
    ui.input_text("user_input", "Enter your text:", placeholder="Type here..."),
    ui.action_button("submit", "Get Similar Queries"),
    ui.output_ui("results")
)

# Define server logic
def server(input, output):
    @output
    @render.ui
    def results():
        if input.submit() > 0:
            user_text = input.user_input()
            if user_text:
                # Compute the embedding for the user input
                user_embedding = model.encode([user_text])
                
                # Compute cosine similarities
                similarities = cosine_similarity(user_embedding, query_embeddings).flatten()
                
                # Get the indices of the top 5 similar queries
                top_indices = np.argsort(similarities)[-5:][::-1]
                
                # Prepare the results to display
                result_boxes = ui.tag_list()
                for idx in top_indices:
                    result_boxes.append(ui.div(queries[idx], class_="result-box"))
                return result_boxes
        return ui.div("Please enter text and press the button.")

# Create the Shiny app
app = App(app_ui, server)

if __name__ == "__main__":
    app.run()