jason
69 lines
2.1 KiB
Rust
69 lines
2.1 KiB
Rust
//! Glm-OCR exec implementation for CLI `run` subcommand
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use std::time::Instant;
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use anyhow::{Ok, Result};
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use crate::exec::ExecModel;
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use crate::models::{GenerateModel, glm_ocr::generate::GlmOcrGenerateModel};
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pub struct GlmOcrExec;
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impl ExecModel for GlmOcrExec {
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fn run(input: &[String], output: Option<&str>, weight_path: &str) -> Result<()> {
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let url = &input[0];
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let input_url = if url.starts_with("http://")
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|| url.starts_with("https://")
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|| url.starts_with("file://")
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{
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url.clone()
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} else {
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format!("file://{}", url)
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};
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let i_start = Instant::now();
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let mut model = GlmOcrGenerateModel::init(weight_path, None, None)?;
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let i_duration = i_start.elapsed();
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println!("Time elapsed in load model is: {:?}", i_duration);
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let message = format!(
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r#"{{
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"model": "glm-ocr",
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"messages": [
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{{
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"role": "user",
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"content": [
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{{
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"type": "image_url",
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"image_url": {{
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"url": "{}"
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}}
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}},
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{{
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"type": "text",
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"text": "Text Recognition:"
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}}
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]
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}}
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],
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"max_tokens": 1024
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}}"#,
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input_url
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);
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let mes = serde_json::from_str(&message)?;
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let i_start = Instant::now();
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let result = model.generate(mes)?;
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let i_duration = i_start.elapsed();
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println!("Time elapsed in generate is: {:?}", i_duration);
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println!("Result: {:?}", result);
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if let Some(out) = output {
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std::fs::write(out, format!("{:?}", result))?;
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println!("Output saved to: {}", out);
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}
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Ok(())
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}
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} |