first prototype

.python-version

@@ -0,0 +1 @@
+3.12

Dockerfile

@@ -1,13 +1,30 @@
-FROM python:3.12
+# Dockerfile for Weather Assistant Demo
+# This file configures how the application will run on Hugging Face Spaces
 
-WORKDIR /code
+# Start with Python 3.12 as our base image
+# This gives us a clean Python environment to work with
+FROM python:3.12
 
-COPY ./requirements.txt /code/requirements.txt
+# Install UV package manager
+# UV is a fast, reliable Python package installer written in Rust
+# It's much faster than pip for installing dependencies
+RUN pip install uv
 
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
+# Copy all files from our project into the container
+# This includes app.py, pyproject.toml, and other necessary files
 COPY . .
 
+# Use UV to install all dependencies specified in pyproject.toml
+# This ensures we have the same packages as in local development
+RUN uv sync
+
+# Configure the container to listen on port 7860
+# This is the default port that Hugging Face Spaces expects
 EXPOSE 7860
 
-CMD ["shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]
+# Command to run when the container starts
+# This launches the Shiny app with specific host and port settings
+# - host 0.0.0.0 allows connections from outside the container
+# - port 7860 matches our EXPOSE setting above
+CMD ["/.venv/bin/shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,162 +1,24 @@
-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="$",
-    )
-    ui.input_checkbox_group(
-        "time",
-        "Food service",
-        ["Lunch", "Dinner"],
-        selected=["Lunch", "Dinner"],
-        inline=True,
+from shiny import App
+from shiny.ui import page_navbar
+from shiny import ui
+import os
+
+from ui import (
+    blog
+)
+
+from server import (
+    blog as blog_srv
+)
+
+ui = ui.page_fluid(
+    ui.page_navbar(
+        blog.ui,
+        title="SEO Blog Writer"
     )
-    ui.input_action_button("reset", "Reset filter")
-
-# Add main content
-ICONS = {
-    "user": fa.icon_svg("user", "regular"),
-    "wallet": fa.icon_svg("wallet"),
-    "currency-dollar": fa.icon_svg("dollar-sign"),
-    "ellipsis": fa.icon_svg("ellipsis"),
-}
-
-with ui.layout_columns(fill=False):
-    with ui.value_box(showcase=ICONS["user"]):
-        "Total tippers"
-
-        @render.express
-        def total_tippers():
-            tips_data().shape[0]
-
-    with ui.value_box(showcase=ICONS["wallet"]):
-        "Average tip"
-
-        @render.express
-        def average_tip():
-            d = tips_data()
-            if d.shape[0] > 0:
-                perc = d.tip / d.total_bill
-                f"{perc.mean():.1%}"
-
-    with ui.value_box(showcase=ICONS["currency-dollar"]):
-        "Average bill"
-
-        @render.express
-        def average_bill():
-            d = tips_data()
-            if d.shape[0] > 0:
-                bill = d.total_bill.mean()
-                f"${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]
+)
 
+def server(input, output, session):
+    blog_srv.server(input, output, session)
 
-@reactive.effect
-@reactive.event(input.reset)
-def _():
-    ui.update_slider("total_bill", value=bill_rng)
-    ui.update_checkbox_group("time", selected=["Lunch", "Dinner"])
+app = App(ui, server)

code/llm_connect.py

@@ -0,0 +1,237 @@
+import os
+import pyrsm as rsm
+import google.generativeai as genai
+import requests
+
+# from google.genai import types
+from typing import List
+
+
+def query_llama(
+    messages: List[dict],
+    model: str = "llama-3",
+    max_tokens: int = 4000,
+    temperature: int = 0.4,
+    api_key: str = "",
+) -> dict:
+    """
+    Send a query to the Llama API
+
+    Args:
+        messages (list): List of dictionaries containing message role and content pairs
+        model (str): The model to use. Defaults to "llama-3"
+        max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 4000
+        temperature (int, optional): Controls randomness in the output. 0 is deterministic, higher values more random. Defaults to 0.4
+        api_key (str, optional): Authentication token for API access. Defaults to ""
+
+    Example:
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant"},
+            {"role": "user", "content": "Hello!"}
+        ]
+        response = query_llama(messages, api_key="your-api-key")
+
+    Returns:
+        dict: The model's response
+    """
+    url = "https://traip13.tgptinf.ucsd.edu/v1/chat/completions"
+    if not api_key or len(api_key) == 0:
+        raise ValueError("LLAMA: API key is required")
+
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+    data = {
+        "messages": messages,
+        "model": model,
+        "max_tokens": max_tokens,
+        "temperature": temperature,  # Adjust temperature for randomness (0 for deterministic)
+        "stream": False,
+        "n": 1,
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+    response.raise_for_status()  # Raise an exception for bad status codes
+
+    return response.json()
+
+
+def test_llama_connection(api_key: str, timeout: int = 20) -> bool:
+    """Test connection to Llama API with a basic request"""
+    import requests
+
+    url = "https://traip13.tgptinf.ucsd.edu/v1/chat/completions"
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+    data = {"messages": [], "model": "llama-3", "max_tokens": 1, "temperature": 0.4}
+
+    try:
+        response = requests.post(url, headers=headers, json=data, timeout=20)
+        print(f"Status code: {response.status_code}")
+        print(f"Response headers: {response.headers}")
+        try:
+            print(f"Response body: {response.json()}")
+        except Exception:
+            print(f"Response text: {response.text}")
+        if response.status_code == 401:
+            print("Authentication failed - check your API key")
+            return False
+        elif response.status_code == 404:
+            print("API endpoint not found")
+            return False
+        elif response.status_code == 200:
+            return True
+        else:
+            print(f"Unexpected status code: {response.status_code}")
+            return False
+    except requests.exceptions.Timeout:
+        print(f"Connection timed out after {timeout} seconds")
+        return False
+    except requests.exceptions.ConnectionError:
+        print("Could not connect to server")
+        return False
+
+
+def query_gemini(
+    messages: List[dict],
+    model: str = "gemini-2.0-flash",
+    max_tokens: int = 4000,
+    temperature: int = 0.4,
+    api_key: str = "",
+) -> dict:
+    """
+    Send a query to the Gemini API
+
+    Args:
+        messages (list): List of dictionaries containing message role and content pairs
+        model (str): The model to use. Defaults to "gemini-2.0-flash"
+        max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 4000
+        temperature (int, optional): Controls randomness in the output. 0 is deterministic, higher values more random. Defaults to 0.4
+        api_key (str, optional): Authentication token for API access. Defaults to ""
+
+    Returns:
+        dict: The model's response
+    """
+
+    if not api_key or len(api_key) == 0:
+        raise ValueError("Gemini: API key is required")
+
+    # Convert OpenAI-style messages to Gemini format
+    system_message = [msg["content"] for msg in messages if msg["role"] == "system"]
+    user_messages = [msg["content"] for msg in messages if msg["role"] == "user"]
+
+    # Combine system message (if any) with the user message
+    prompt = ". ".join(system_message + user_messages)
+
+    genai.configure(api_key=api_key)
+
+    # Initialize the model
+    model = genai.GenerativeModel(model_name=model)
+
+    # Define the generation configuration using the specific class
+    generation_config_obj = genai.types.GenerationConfig(
+        temperature=temperature,
+        max_output_tokens=max_tokens,
+    )
+
+    # Generate content using the correct parameter name 'generation_config'
+    response = model.generate_content(
+        contents=prompt, generation_config=generation_config_obj
+    )
+
+    return response.text
+
+
+def get_response(
+    input: str | List[str],
+    template: callable,
+    role: str = "You are a helpful assistant.",
+    temperature: float = 0.4,
+    max_tokens: int = 4000,
+    md: bool = True,
+    llm: str = "llama",
+    model_name: str = None,
+):
+    """
+    Function to get a response from the LLama API
+    """
+    messages = [
+        {"role": "system", "content": role},
+        {
+            "role": "user",
+            "content": template(input),
+        },
+    ]
+
+    if llm == "llama":
+        response = query_llama(
+            messages=messages,
+            api_key=os.getenv("LLAMA_API_KEY"),
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )["choices"][0]["message"]["content"]
+    elif llm == "gemini":
+        response = query_gemini(
+            messages=messages,
+            api_key=os.getenv("GEMINI_API_KEY"),
+            temperature=temperature,
+            max_tokens=max_tokens,
+            model=model_name if model_name else 'gemini-2.0-flash'
+        )
+    else:
+        raise ValueError("LLM: Invalid LLM specified")
+
+    if md:
+        return rsm.md(response)
+    else:
+        return response
+
+
+if __name__ == "__main__":
+    from dotenv import load_dotenv
+
+    # Load environment variables
+    load_dotenv()
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi, how are you? What is your name?"},
+    ]
+
+    # Testing Llama connection
+    try:
+        print("\nTesting Llama connection ...")
+        test_llama_connection(api_key=os.getenv("LLAMA_API_KEY"))
+    except Exception as e:
+        print(f"Error: {e}")
+
+    # Testing Llama connection
+    try:
+        print("\nQuerying Llama ...")
+        response = query_llama(messages, api_key=os.getenv("LLAMA_API_KEY"))
+        print(response)
+    except Exception as e:
+        print(f"Error: {e}")
+
+    try:
+        print("\nQuerying Gemini ...")
+        response = query_gemini(messages, api_key=os.getenv("GEMINI_API_KEY"))
+        print(response)
+    except Exception as e:
+        print(f"Error: {e}")
+
+    try:
+        print("\nTesting get_response ...")
+
+        def template(input):
+            return f"""Evaluate the following statement for factual accuracy. If it's incorrect, provide the correct information:
+            Statement: {input}
+            Evaluation:"""
+
+        response = get_response(
+            "The capital of the Netherlands is Utrecht.",
+            template=template,
+            md=False,
+            llm="llama",
+        )
+        print(response)
+
+    except Exception as e:
+        print(f"Error: {e}")

pyproject.toml

@@ -0,0 +1,22 @@
+[project]
+name = "ultima-seo"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "anthropic>=0.55.0",
+    "google-generativeai>=0.8.5",
+    "ipywidgets>=8.1.7",
+    "langchain>=0.3.26",
+    "langchain-anthropic>=0.3.16",
+    "langchain-community>=0.3.26",
+    "langchain-google-genai>=2.0.10",
+    "langchain-openai>=0.3.27",
+    "openai>=1.93.0",
+    "pydantic>=2.11.7",
+    "pydantic-ai>=0.3.5",
+    "pyrsm>=1.6.0",
+    "python-dotenv>=1.1.1",
+    "requests>=2.32.4",
+]

requirements.txt

@@ -1,6 +0,0 @@
-faicons
-shiny
-shinywidgets
-plotly
-pandas
-ridgeplot

server/blog.py

@@ -0,0 +1,28 @@
+from shiny import reactive, render
+from llm_connect import get_response
+
+def draft_blog(topic):
+    prompt = (
+        f"You are a blog writer for a hobby company called 'Ultima Supply'.\n"
+        f"Write me a SEO optimized blog with a title and subsections for the following topic in great detail:\n"
+        f"{topic}\n"
+        f"Make sure the blog that you create has SEO keywords throughout so it can be found via search engines."
+    )
+
+    return get_response(
+        input=prompt,
+        template=lambda x: x,
+        llm='llama',
+        md=False,
+        temperature=0.8,
+        max_tokens=1000
+    )
+
+def server(input, output, session):
+    @output
+    @render.text
+    def blog_output():
+        topic = input.topic()
+        if not topic:
+            return "Enter a topic to generate a blog."
+        return draft_blog(topic)

shared.py

@@ -1,6 +0,0 @@
-from pathlib import Path
-
-import pandas as pd
-
-app_dir = Path(__file__).parent
-tips = pd.read_csv(app_dir / "tips.csv")

styles.css

@@ -1,12 +0,0 @@
-:root {
-  --bslib-sidebar-main-bg: #f8f8f8;
-}
-
-.popover {
-  --bs-popover-header-bg: #222;
-  --bs-popover-header-color: #fff;
-}
-
-.popover .btn-close {
-  filter: var(--bs-btn-close-white-filter);
-}

tips.csv

@@ -1,245 +0,0 @@
-total_bill,tip,sex,smoker,day,time,size
-16.99,1.01,Female,No,Sun,Dinner,2
-10.34,1.66,Male,No,Sun,Dinner,3
-21.01,3.5,Male,No,Sun,Dinner,3
-23.68,3.31,Male,No,Sun,Dinner,2
-24.59,3.61,Female,No,Sun,Dinner,4
-25.29,4.71,Male,No,Sun,Dinner,4
-8.77,2.0,Male,No,Sun,Dinner,2
-26.88,3.12,Male,No,Sun,Dinner,4
-15.04,1.96,Male,No,Sun,Dinner,2
-14.78,3.23,Male,No,Sun,Dinner,2
-10.27,1.71,Male,No,Sun,Dinner,2
-35.26,5.0,Female,No,Sun,Dinner,4
-15.42,1.57,Male,No,Sun,Dinner,2
-18.43,3.0,Male,No,Sun,Dinner,4
-14.83,3.02,Female,No,Sun,Dinner,2
-21.58,3.92,Male,No,Sun,Dinner,2
-10.33,1.67,Female,No,Sun,Dinner,3
-16.29,3.71,Male,No,Sun,Dinner,3
-16.97,3.5,Female,No,Sun,Dinner,3
-20.65,3.35,Male,No,Sat,Dinner,3
-17.92,4.08,Male,No,Sat,Dinner,2
-20.29,2.75,Female,No,Sat,Dinner,2
-15.77,2.23,Female,No,Sat,Dinner,2
-39.42,7.58,Male,No,Sat,Dinner,4
-19.82,3.18,Male,No,Sat,Dinner,2
-17.81,2.34,Male,No,Sat,Dinner,4
-13.37,2.0,Male,No,Sat,Dinner,2
-12.69,2.0,Male,No,Sat,Dinner,2
-21.7,4.3,Male,No,Sat,Dinner,2
-19.65,3.0,Female,No,Sat,Dinner,2
-9.55,1.45,Male,No,Sat,Dinner,2
-18.35,2.5,Male,No,Sat,Dinner,4
-15.06,3.0,Female,No,Sat,Dinner,2
-20.69,2.45,Female,No,Sat,Dinner,4
-17.78,3.27,Male,No,Sat,Dinner,2
-24.06,3.6,Male,No,Sat,Dinner,3
-16.31,2.0,Male,No,Sat,Dinner,3
-16.93,3.07,Female,No,Sat,Dinner,3
-18.69,2.31,Male,No,Sat,Dinner,3
-31.27,5.0,Male,No,Sat,Dinner,3
-16.04,2.24,Male,No,Sat,Dinner,3
-17.46,2.54,Male,No,Sun,Dinner,2
-13.94,3.06,Male,No,Sun,Dinner,2
-9.68,1.32,Male,No,Sun,Dinner,2
-30.4,5.6,Male,No,Sun,Dinner,4
-18.29,3.0,Male,No,Sun,Dinner,2
-22.23,5.0,Male,No,Sun,Dinner,2
-32.4,6.0,Male,No,Sun,Dinner,4
-28.55,2.05,Male,No,Sun,Dinner,3
-18.04,3.0,Male,No,Sun,Dinner,2
-12.54,2.5,Male,No,Sun,Dinner,2
-10.29,2.6,Female,No,Sun,Dinner,2
-34.81,5.2,Female,No,Sun,Dinner,4
-9.94,1.56,Male,No,Sun,Dinner,2
-25.56,4.34,Male,No,Sun,Dinner,4
-19.49,3.51,Male,No,Sun,Dinner,2
-38.01,3.0,Male,Yes,Sat,Dinner,4
-26.41,1.5,Female,No,Sat,Dinner,2
-11.24,1.76,Male,Yes,Sat,Dinner,2
-48.27,6.73,Male,No,Sat,Dinner,4
-20.29,3.21,Male,Yes,Sat,Dinner,2
-13.81,2.0,Male,Yes,Sat,Dinner,2
-11.02,1.98,Male,Yes,Sat,Dinner,2
-18.29,3.76,Male,Yes,Sat,Dinner,4
-17.59,2.64,Male,No,Sat,Dinner,3
-20.08,3.15,Male,No,Sat,Dinner,3
-16.45,2.47,Female,No,Sat,Dinner,2
-3.07,1.0,Female,Yes,Sat,Dinner,1
-20.23,2.01,Male,No,Sat,Dinner,2
-15.01,2.09,Male,Yes,Sat,Dinner,2
-12.02,1.97,Male,No,Sat,Dinner,2
-17.07,3.0,Female,No,Sat,Dinner,3
-26.86,3.14,Female,Yes,Sat,Dinner,2
-25.28,5.0,Female,Yes,Sat,Dinner,2
-14.73,2.2,Female,No,Sat,Dinner,2
-10.51,1.25,Male,No,Sat,Dinner,2
-17.92,3.08,Male,Yes,Sat,Dinner,2
-27.2,4.0,Male,No,Thur,Lunch,4
-22.76,3.0,Male,No,Thur,Lunch,2
-17.29,2.71,Male,No,Thur,Lunch,2
-19.44,3.0,Male,Yes,Thur,Lunch,2
-16.66,3.4,Male,No,Thur,Lunch,2
-10.07,1.83,Female,No,Thur,Lunch,1
-32.68,5.0,Male,Yes,Thur,Lunch,2
-15.98,2.03,Male,No,Thur,Lunch,2
-34.83,5.17,Female,No,Thur,Lunch,4
-13.03,2.0,Male,No,Thur,Lunch,2
-18.28,4.0,Male,No,Thur,Lunch,2
-24.71,5.85,Male,No,Thur,Lunch,2
-21.16,3.0,Male,No,Thur,Lunch,2
-28.97,3.0,Male,Yes,Fri,Dinner,2
-22.49,3.5,Male,No,Fri,Dinner,2
-5.75,1.0,Female,Yes,Fri,Dinner,2
-16.32,4.3,Female,Yes,Fri,Dinner,2
-22.75,3.25,Female,No,Fri,Dinner,2
-40.17,4.73,Male,Yes,Fri,Dinner,4
-27.28,4.0,Male,Yes,Fri,Dinner,2
-12.03,1.5,Male,Yes,Fri,Dinner,2
-21.01,3.0,Male,Yes,Fri,Dinner,2
-12.46,1.5,Male,No,Fri,Dinner,2
-11.35,2.5,Female,Yes,Fri,Dinner,2
-15.38,3.0,Female,Yes,Fri,Dinner,2
-44.3,2.5,Female,Yes,Sat,Dinner,3
-22.42,3.48,Female,Yes,Sat,Dinner,2
-20.92,4.08,Female,No,Sat,Dinner,2
-15.36,1.64,Male,Yes,Sat,Dinner,2
-20.49,4.06,Male,Yes,Sat,Dinner,2
-25.21,4.29,Male,Yes,Sat,Dinner,2
-18.24,3.76,Male,No,Sat,Dinner,2
-14.31,4.0,Female,Yes,Sat,Dinner,2
-14.0,3.0,Male,No,Sat,Dinner,2
-7.25,1.0,Female,No,Sat,Dinner,1
-38.07,4.0,Male,No,Sun,Dinner,3
-23.95,2.55,Male,No,Sun,Dinner,2
-25.71,4.0,Female,No,Sun,Dinner,3
-17.31,3.5,Female,No,Sun,Dinner,2
-29.93,5.07,Male,No,Sun,Dinner,4
-10.65,1.5,Female,No,Thur,Lunch,2
-12.43,1.8,Female,No,Thur,Lunch,2
-24.08,2.92,Female,No,Thur,Lunch,4
-11.69,2.31,Male,No,Thur,Lunch,2
-13.42,1.68,Female,No,Thur,Lunch,2
-14.26,2.5,Male,No,Thur,Lunch,2
-15.95,2.0,Male,No,Thur,Lunch,2
-12.48,2.52,Female,No,Thur,Lunch,2
-29.8,4.2,Female,No,Thur,Lunch,6
-8.52,1.48,Male,No,Thur,Lunch,2
-14.52,2.0,Female,No,Thur,Lunch,2
-11.38,2.0,Female,No,Thur,Lunch,2
-22.82,2.18,Male,No,Thur,Lunch,3
-19.08,1.5,Male,No,Thur,Lunch,2
-20.27,2.83,Female,No,Thur,Lunch,2
-11.17,1.5,Female,No,Thur,Lunch,2
-12.26,2.0,Female,No,Thur,Lunch,2
-18.26,3.25,Female,No,Thur,Lunch,2
-8.51,1.25,Female,No,Thur,Lunch,2
-10.33,2.0,Female,No,Thur,Lunch,2
-14.15,2.0,Female,No,Thur,Lunch,2
-16.0,2.0,Male,Yes,Thur,Lunch,2
-13.16,2.75,Female,No,Thur,Lunch,2
-17.47,3.5,Female,No,Thur,Lunch,2
-34.3,6.7,Male,No,Thur,Lunch,6
-41.19,5.0,Male,No,Thur,Lunch,5
-27.05,5.0,Female,No,Thur,Lunch,6
-16.43,2.3,Female,No,Thur,Lunch,2
-8.35,1.5,Female,No,Thur,Lunch,2
-18.64,1.36,Female,No,Thur,Lunch,3
-11.87,1.63,Female,No,Thur,Lunch,2
-9.78,1.73,Male,No,Thur,Lunch,2
-7.51,2.0,Male,No,Thur,Lunch,2
-14.07,2.5,Male,No,Sun,Dinner,2
-13.13,2.0,Male,No,Sun,Dinner,2
-17.26,2.74,Male,No,Sun,Dinner,3
-24.55,2.0,Male,No,Sun,Dinner,4
-19.77,2.0,Male,No,Sun,Dinner,4
-29.85,5.14,Female,No,Sun,Dinner,5
-48.17,5.0,Male,No,Sun,Dinner,6
-25.0,3.75,Female,No,Sun,Dinner,4
-13.39,2.61,Female,No,Sun,Dinner,2
-16.49,2.0,Male,No,Sun,Dinner,4
-21.5,3.5,Male,No,Sun,Dinner,4
-12.66,2.5,Male,No,Sun,Dinner,2
-16.21,2.0,Female,No,Sun,Dinner,3
-13.81,2.0,Male,No,Sun,Dinner,2
-17.51,3.0,Female,Yes,Sun,Dinner,2
-24.52,3.48,Male,No,Sun,Dinner,3
-20.76,2.24,Male,No,Sun,Dinner,2
-31.71,4.5,Male,No,Sun,Dinner,4
-10.59,1.61,Female,Yes,Sat,Dinner,2
-10.63,2.0,Female,Yes,Sat,Dinner,2
-50.81,10.0,Male,Yes,Sat,Dinner,3
-15.81,3.16,Male,Yes,Sat,Dinner,2
-7.25,5.15,Male,Yes,Sun,Dinner,2
-31.85,3.18,Male,Yes,Sun,Dinner,2
-16.82,4.0,Male,Yes,Sun,Dinner,2
-32.9,3.11,Male,Yes,Sun,Dinner,2
-17.89,2.0,Male,Yes,Sun,Dinner,2
-14.48,2.0,Male,Yes,Sun,Dinner,2
-9.6,4.0,Female,Yes,Sun,Dinner,2
-34.63,3.55,Male,Yes,Sun,Dinner,2
-34.65,3.68,Male,Yes,Sun,Dinner,4
-23.33,5.65,Male,Yes,Sun,Dinner,2
-45.35,3.5,Male,Yes,Sun,Dinner,3
-23.17,6.5,Male,Yes,Sun,Dinner,4
-40.55,3.0,Male,Yes,Sun,Dinner,2
-20.69,5.0,Male,No,Sun,Dinner,5
-20.9,3.5,Female,Yes,Sun,Dinner,3
-30.46,2.0,Male,Yes,Sun,Dinner,5
-18.15,3.5,Female,Yes,Sun,Dinner,3
-23.1,4.0,Male,Yes,Sun,Dinner,3
-15.69,1.5,Male,Yes,Sun,Dinner,2
-19.81,4.19,Female,Yes,Thur,Lunch,2
-28.44,2.56,Male,Yes,Thur,Lunch,2
-15.48,2.02,Male,Yes,Thur,Lunch,2
-16.58,4.0,Male,Yes,Thur,Lunch,2
-7.56,1.44,Male,No,Thur,Lunch,2
-10.34,2.0,Male,Yes,Thur,Lunch,2
-43.11,5.0,Female,Yes,Thur,Lunch,4
-13.0,2.0,Female,Yes,Thur,Lunch,2
-13.51,2.0,Male,Yes,Thur,Lunch,2
-18.71,4.0,Male,Yes,Thur,Lunch,3
-12.74,2.01,Female,Yes,Thur,Lunch,2
-13.0,2.0,Female,Yes,Thur,Lunch,2
-16.4,2.5,Female,Yes,Thur,Lunch,2
-20.53,4.0,Male,Yes,Thur,Lunch,4
-16.47,3.23,Female,Yes,Thur,Lunch,3
-26.59,3.41,Male,Yes,Sat,Dinner,3
-38.73,3.0,Male,Yes,Sat,Dinner,4
-24.27,2.03,Male,Yes,Sat,Dinner,2
-12.76,2.23,Female,Yes,Sat,Dinner,2
-30.06,2.0,Male,Yes,Sat,Dinner,3
-25.89,5.16,Male,Yes,Sat,Dinner,4
-48.33,9.0,Male,No,Sat,Dinner,4
-13.27,2.5,Female,Yes,Sat,Dinner,2
-28.17,6.5,Female,Yes,Sat,Dinner,3
-12.9,1.1,Female,Yes,Sat,Dinner,2
-28.15,3.0,Male,Yes,Sat,Dinner,5
-11.59,1.5,Male,Yes,Sat,Dinner,2
-7.74,1.44,Male,Yes,Sat,Dinner,2
-30.14,3.09,Female,Yes,Sat,Dinner,4
-12.16,2.2,Male,Yes,Fri,Lunch,2
-13.42,3.48,Female,Yes,Fri,Lunch,2
-8.58,1.92,Male,Yes,Fri,Lunch,1
-15.98,3.0,Female,No,Fri,Lunch,3
-13.42,1.58,Male,Yes,Fri,Lunch,2
-16.27,2.5,Female,Yes,Fri,Lunch,2
-10.09,2.0,Female,Yes,Fri,Lunch,2
-20.45,3.0,Male,No,Sat,Dinner,4
-13.28,2.72,Male,No,Sat,Dinner,2
-22.12,2.88,Female,Yes,Sat,Dinner,2
-24.01,2.0,Male,Yes,Sat,Dinner,4
-15.69,3.0,Male,Yes,Sat,Dinner,3
-11.61,3.39,Male,No,Sat,Dinner,2
-10.77,1.47,Male,No,Sat,Dinner,2
-15.53,3.0,Male,Yes,Sat,Dinner,2
-10.07,1.25,Male,No,Sat,Dinner,2
-12.6,1.0,Male,Yes,Sat,Dinner,2
-32.83,1.17,Male,Yes,Sat,Dinner,2
-35.83,4.67,Female,No,Sat,Dinner,3
-29.03,5.92,Male,No,Sat,Dinner,3
-27.18,2.0,Female,Yes,Sat,Dinner,2
-22.67,2.0,Male,Yes,Sat,Dinner,2
-17.82,1.75,Male,No,Sat,Dinner,2
-18.78,3.0,Female,No,Thur,Dinner,2

ui/blog.py

@@ -0,0 +1,7 @@
+from shiny import ui
+
+ui = ui.nav_panel(
+    "Blog Writer",
+    ui.input_text("topic", "Enter Blog Topic", placeholder="e.g. Best One Piece Cards in 2025"),
+    ui.output_text_verbatim("blog_output"),
+)