feat: fix hotspot test
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@@ -1,6 +1,6 @@
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use axum::{
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Json,
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extract::{Path, Query, State},
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extract::{Path, Query, State, Multipart},
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http::StatusCode,
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};
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use bcrypt::{DEFAULT_COST, hash, verify};
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@@ -991,8 +991,9 @@ pub async fn check_deadlines_and_notify(pool: PgPool) {
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'deadline',
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'/courses/' || c.id || '/lessons/' || l.id
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FROM enrollments e
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JOIN lessons l ON l.course_id = e.course_id
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JOIN courses c ON c.id = l.course_id
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JOIN courses c ON c.id = e.course_id
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JOIN modules m ON m.course_id = c.id
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JOIN lessons l ON l.module_id = m.id
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WHERE l.due_date BETWEEN NOW() AND NOW() + INTERVAL '24 hours'
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AND NOT EXISTS (
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SELECT 1 FROM notifications n
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@@ -1054,7 +1055,7 @@ pub async fn update_user(
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}
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pub async fn get_recommendations(
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Org(org_ctx): Org,
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Org(_org_ctx): Org,
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claims: Claims,
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State(pool): State<PgPool>,
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Path(course_id): Path<Uuid>,
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@@ -1221,3 +1222,126 @@ pub async fn evaluate_audio_response(
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Ok(Json(grading))
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}
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pub async fn evaluate_audio_file(
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Org(_org_ctx): Org,
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_claims: Claims,
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mut multipart: Multipart,
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) -> Result<Json<AudioGradingResponse>, (StatusCode, String)> {
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let mut prompt = String::new();
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let mut keywords_str = String::new();
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let mut audio_data = Vec::new();
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let mut filename = "audio.webm".to_string();
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while let Some(field) = multipart.next_field().await.map_err(|e| (StatusCode::BAD_REQUEST, e.to_string()))? {
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let name = field.name().unwrap_or_default().to_string();
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match name.as_str() {
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"prompt" => prompt = field.text().await.unwrap_or_default(),
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"keywords" => keywords_str = field.text().await.unwrap_or_default(),
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"file" => {
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filename = field.file_name().unwrap_or("audio.webm").to_string();
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audio_data = field.bytes().await.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?.to_vec();
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},
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_ => {}
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}
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}
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if audio_data.is_empty() {
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return Err((StatusCode::BAD_REQUEST, "No audio file provided".into()));
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}
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// 1. Send to Whisper
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let whisper_url = env::var("LOCAL_WHISPER_URL").unwrap_or_else(|_| "http://localhost:8000".to_string());
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let client = reqwest::Client::new();
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let form = reqwest::multipart::Form::new()
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.part("file", reqwest::multipart::Part::bytes(audio_data).file_name(filename))
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.text("model", "whisper-1")
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.text("response_format", "json");
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let response = client.post(format!("{}/v1/audio/transcriptions", whisper_url))
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.multipart(form)
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.send()
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.await
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.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Whisper request failed: {}", e)))?;
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if !response.status().is_success() {
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let err_body = response.text().await.unwrap_or_default();
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tracing::error!("Whisper error: {}", err_body);
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return Err((StatusCode::INTERNAL_SERVER_ERROR, format!("Whisper API error: {}", err_body)));
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}
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let transcription_result: serde_json::Value = response.json().await
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.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Failed to parse Whisper response: {}", e)))?;
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let transcript = transcription_result["text"].as_str().unwrap_or("").to_string();
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if transcript.is_empty() {
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return Err((StatusCode::BAD_REQUEST, "Whisper could not detect any speech. Please speak louder or check your mic.".into()));
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}
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let keywords: Vec<String> = if keywords_str.trim().starts_with('[') {
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serde_json::from_str(&keywords_str).unwrap_or_default()
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} else {
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keywords_str.split(',').map(|s| s.trim().to_string()).filter(|s| !s.is_empty()).collect()
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};
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// 2. Perform AI Grading
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let provider = env::var("AI_PROVIDER").unwrap_or_else(|_| "openai".to_string());
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let (url, auth_header, model) = if provider == "local" {
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let base_url = env::var("LOCAL_OLLAMA_URL").unwrap_or_else(|_| "http://ollama:11434".to_string());
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let model = env::var("LOCAL_LLM_MODEL").unwrap_or_else(|_| "llama3:8b".to_string());
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(format!("{}/v1/chat/completions", base_url), "".to_string(), model)
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} else {
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(
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"https://api.openai.com/v1/chat/completions".to_string(),
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format!("Bearer {}", env::var("OPENAI_API_KEY").unwrap_or_default()),
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"gpt-4-turbo".to_string(),
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)
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};
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let system_prompt = "You are an expert Teacher. Evaluate the student's spoken response transcript. \
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Compare it against the prompt and expected keywords. \
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Provide a score from 0 to 100. \
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Identify which keywords were used. \
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Give constructive feedback in Spanish about their pronunciation (based on the transcript quality) and content. \
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Return ONLY a JSON object: { \"score\": number, \"found_keywords\": [string], \"feedback\": string }.";
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let user_content = format!(
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"Prompt: {}\nExpected Keywords: {:?}\nStudent Transcript: {}",
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prompt, keywords, transcript
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);
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let response = client.post(&url)
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.header("Content-Type", "application/json")
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.header("Authorization", auth_header)
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.json(&serde_json::json!({
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"model": model,
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"messages": [
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{ "role": "system", "content": system_prompt },
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{ "role": "user", "content": user_content }
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],
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"response_format": { "type": "json_object" }
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}))
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.send()
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.await
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.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
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let ai_data: serde_json::Value = response.json().await
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.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("AI response parse failed: {}", e)))?;
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let grading: AudioGradingResponse = serde_json::from_value(
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ai_data["choices"][0]["message"]["content"]
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.as_str()
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.and_then(|c| serde_json::from_str(c).ok())
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.unwrap_or_else(|| {
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serde_json::json!({
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"score": 50,
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"found_keywords": vec![] as Vec<String>,
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"feedback": "Lo siento, tuve un problema analizando tu respuesta con Whisper. ¡Sigue practicando!"
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})
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})
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).map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Mapping failed: {}", e)))?;
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Ok(Json(grading))
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}
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@@ -80,6 +80,7 @@ async fn main() {
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get(handlers::get_lesson_heatmap),
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)
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.route("/audio/evaluate", post(handlers::evaluate_audio_response))
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.route("/audio/evaluate-file", post(handlers::evaluate_audio_file))
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.route("/notifications", get(handlers::get_notifications))
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.route(
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"/notifications/{id}/read",
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