use crate::context::AppContext;
use crate::errors::XmasterError;
use crate::intel::store::IntelStore;
use crate::output::{self, CsvRenderable, OutputFormat, Tableable};
use serde::Serialize;
use std::sync::Arc;
#[derive(Serialize)]
struct InspireResults {
query: String,
count: usize,
library_size: i64,
posts: Vec<InspireRow>,
}
#[derive(Serialize)]
struct InspireRow {
id: String,
author: String,
text: String,
likes: i64,
impressions: i64,
source: String,
}
impl Tableable for InspireResults {
fn to_table(&self) -> comfy_table::Table {
let mut table = comfy_table::Table::new();
table.set_header(vec!["ID", "Author", "Text", "Likes", "Views", "Via"]);
for p in &self.posts {
let truncated = if p.text.len() > 120 {
let boundary = p.text.floor_char_boundary(117);
format!("{}...", &p.text[..boundary])
} else {
p.text.clone()
};
table.add_row(vec![
&p.id, &p.author, &truncated,
&p.likes.to_string(), &p.impressions.to_string(), &p.source,
]);
}
table
}
}
impl CsvRenderable for InspireResults {
fn csv_headers() -> Vec<&'static str> {
vec!["id", "author", "text", "likes", "impressions", "source"]
}
fn csv_rows(&self) -> Vec<Vec<String>> {
self.posts.iter().map(|p| vec![
p.id.clone(), p.author.clone(), p.text.clone(),
p.likes.to_string(), p.impressions.to_string(), p.source.clone(),
]).collect()
}
}
pub async fn execute(
_ctx: Arc<AppContext>,
format: OutputFormat,
topic: Option<&str>,
author: Option<&str>,
min_likes: Option<i64>,
count: usize,
) -> Result<(), XmasterError> {
let store = IntelStore::open()
.map_err(|e| XmasterError::Config(format!("DB error: {e}")))?;
let library_size = store.discovered_posts_count()
.map_err(|e| XmasterError::Config(format!("DB error: {e}")))?;
let rows = store.query_discovered_posts(topic, author, min_likes, count)
.map_err(|e| XmasterError::Config(format!("Query error: {e}")))?;
if rows.is_empty() {
let hint = if library_size == 0 {
"Library is empty. Run `xmaster search`, `xmaster timeline`, or `xmaster read` to start building it."
} else {
"No posts match your filters. Try broader criteria or omit --min-likes."
};
return Err(XmasterError::NotFound(hint.into()));
}
let display = InspireResults {
query: topic.unwrap_or("all").to_string(),
count: rows.len(),
library_size,
posts: rows.into_iter().map(|r| InspireRow {
id: r.tweet_id,
author: if r.author_username.is_empty() { "?".into() } else { format!("@{}", r.author_username) },
text: r.text,
likes: r.like_count,
impressions: r.impression_count,
source: r.last_source,
}).collect(),
};
output::render_csv(format, &display, None);
Ok(())
}