prelim code test

app.py

@@ -1,162 +1,72 @@
-import faicons as fa
-import plotly.express as px
-
-# Load data and compute static values
-from shared import app_dir, tips
-from shinywidgets import render_plotly
-
-from shiny import reactive, render
-from shiny.express import input, ui
-
-bill_rng = (min(tips.total_bill), max(tips.total_bill))
-
-# Add page title and sidebar
-ui.page_opts(title="Restaurant tipping", fillable=True)
-
-with ui.sidebar(open="desktop"):
-    ui.input_slider(
-        "total_bill",
-        "Bill amount",
-        min=bill_rng[0],
-        max=bill_rng[1],
-        value=bill_rng,
-        pre="$",
-    )
-    ui.input_checkbox_group(
-        "time",
-        "Food service",
-        ["Lunch", "Dinner"],
-        selected=["Lunch", "Dinner"],
-        inline=True,
-    )
-    ui.input_action_button("reset", "Reset filter")
-
-# Add main content
-ICONS = {
-    "user": fa.icon_svg("user", "regular"),
-    "wallet": fa.icon_svg("wallet"),
-    "currency-dollar": fa.icon_svg("dollar-sign"),
-    "ellipsis": fa.icon_svg("ellipsis"),
-}
-
-with ui.layout_columns(fill=False):
-    with ui.value_box(showcase=ICONS["user"]):
-        "Total tippers"
-
-        @render.express
-        def total_tippers():
-            tips_data().shape[0]
-
-    with ui.value_box(showcase=ICONS["wallet"]):
-        "Average tip"
-
-        @render.express
-        def average_tip():
-            d = tips_data()
-            if d.shape[0] > 0:
-                perc = d.tip / d.total_bill
-                f"{perc.mean():.1%}"
-
-    with ui.value_box(showcase=ICONS["currency-dollar"]):
-        "Average bill"
-
-        @render.express
-        def average_bill():
-            d = tips_data()
-            if d.shape[0] > 0:
-                bill = d.total_bill.mean()
-                f"${bill:.2f}"
-
-
-with ui.layout_columns(col_widths=[6, 6, 12]):
-    with ui.card(full_screen=True):
-        ui.card_header("Tips data")
-
-        @render.data_frame
-        def table():
-            return render.DataGrid(tips_data())
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Total bill vs tip"
-            with ui.popover(title="Add a color variable", placement="top"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "scatter_color",
-                    None,
-                    ["none", "sex", "smoker", "day", "time"],
-                    inline=True,
-                )
-
-        @render_plotly
-        def scatterplot():
-            color = input.scatter_color()
-            return px.scatter(
-                tips_data(),
-                x="total_bill",
-                y="tip",
-                color=None if color == "none" else color,
-                trendline="lowess",
-            )
-
-    with ui.card(full_screen=True):
-        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
-            "Tip percentages"
-            with ui.popover(title="Add a color variable"):
-                ICONS["ellipsis"]
-                ui.input_radio_buttons(
-                    "tip_perc_y",
-                    "Split by:",
-                    ["sex", "smoker", "day", "time"],
-                    selected="day",
-                    inline=True,
-                )
-
-        @render_plotly
-        def tip_perc():
-            from ridgeplot import ridgeplot
-
-            dat = tips_data()
-            dat["percent"] = dat.tip / dat.total_bill
-            yvar = input.tip_perc_y()
-            uvals = dat[yvar].unique()
-
-            samples = [[dat.percent[dat[yvar] == val]] for val in uvals]
-
-            plt = ridgeplot(
-                samples=samples,
-                labels=uvals,
-                bandwidth=0.01,
-                colorscale="viridis",
-                colormode="row-index",
-            )
-
-            plt.update_layout(
-                legend=dict(
-                    orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
-                )
-            )
-
-            return plt
-
-
-ui.include_css(app_dir / "styles.css")
-
-# --------------------------------------------------------
-# Reactive calculations and effects
-# --------------------------------------------------------
-
-
-@reactive.calc
-def tips_data():
-    bill = input.total_bill()
-    idx1 = tips.total_bill.between(bill[0], bill[1])
-    idx2 = tips.time.isin(input.time())
-    return tips[idx1 & idx2]
-
-
-@reactive.effect
-@reactive.event(input.reset)
-def _():
-    ui.update_slider("total_bill", value=bill_rng)
-    ui.update_checkbox_group("time", selected=["Lunch", "Dinner"])
+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()