feat: display progress for finalising audio and summarising

src-tauri/src/ml/summarizer.rs

@@ -71,6 +71,14 @@ impl LlamaSummarizer {
 
     /// Generate a meeting summary from the transcript.
     pub fn summarize(&self, transcript: &str) -> Result<String, SummarizerError> {
+        self.summarize_with_progress(transcript, |_| {})
+    }
+
+    /// Generate a meeting summary with progress callback.
+    pub fn summarize_with_progress<F>(&self, transcript: &str, mut progress_fn: F) -> Result<String, SummarizerError>
+    where
+        F: FnMut(f64),
+    {
         let model = self
             .model
             .as_ref()
@@ -94,6 +102,9 @@ impl LlamaSummarizer {
 
         debug!("Generating summary for {} character transcript", transcript.len());
 
+        // Report initial progress
+        progress_fn(0.0);
+
         // Create context for inference
         let ctx_params = LlamaContextParams::default()
             .with_n_ctx(NonZeroU32::new(8192));
@@ -102,6 +113,9 @@ impl LlamaSummarizer {
             .new_context(self.backend.as_ref().unwrap(), ctx_params)
             .map_err(|e| SummarizerError::ContextError(e.to_string()))?;
 
+        // Report progress after context creation
+        progress_fn(0.1);
+
         // Tokenize the prompt
         let tokens = model
             .str_to_token(&prompt, llama_cpp_2::model::AddBos::Always)
@@ -109,6 +123,9 @@ impl LlamaSummarizer {
 
         debug!("Prompt tokenized to {} tokens", tokens.len());
 
+        // Report progress after tokenization
+        progress_fn(0.2);
+
         // Create batch and add tokens
         let mut batch = LlamaBatch::new(tokens.len(), 1);
 
@@ -122,6 +139,9 @@ impl LlamaSummarizer {
         ctx.decode(&mut batch)
             .map_err(|e| SummarizerError::SummarizationError(e.to_string()))?;
 
+        // Report progress after prompt decode
+        progress_fn(0.3);
+
         // Generate response tokens
         let mut output_tokens = Vec::new();
         let max_tokens = 1024;
@@ -134,7 +154,7 @@ impl LlamaSummarizer {
             LlamaSampler::dist(42),
         ]);
 
-        for _ in 0..max_tokens {
+        for i in 0..max_tokens {
             // Sample the next token using the sampler
             // The sampler.sample() takes context and the index of the last token in the batch
             let token = sampler.sample(&ctx, (batch.n_tokens() - 1) as i32);
@@ -146,6 +166,10 @@ impl LlamaSummarizer {
 
             output_tokens.push(token);
 
+            // Report progress during generation (from 30% to 90%)
+            let generation_progress = i as f64 / max_tokens as f64;
+            progress_fn(0.3 + (generation_progress * 0.6));
+
             // Prepare next batch
             batch.clear();
             batch.add(token, n_cur as i32, &[0], true)
@@ -164,6 +188,9 @@ impl LlamaSummarizer {
             .filter_map(|t| model.token_to_str(*t, llama_cpp_2::model::Special::Tokenize).ok())
             .collect::<String>();
 
+        // Report completion
+        progress_fn(1.0);
+
         info!("Summary generated: {} characters", output_text.len());
         Ok(output_text.trim().to_string())
     }