Modificaciones menores en archivos del pipeline de ia

backend/src/signals/finnhub.client.js

@@ -2,8 +2,8 @@
  * Servicio de integracion con Finnhub REST API.
  *
  * Responsabilidades:
- *   - fetchFinancialNews(count) → obtiene titulares de noticias financieras.
- *   - filterNewsByRelevance(articles, question) → filtra por keywords del mercado.
+ *   - Obtener titulares de noticias financieras.
+ *   - Filtrar por keywords del mercado.
  *
  * Restricciones:
  *   - Free tier: maximo 60 llamadas/minuto.
@@ -14,27 +14,3 @@
  *   - aiPipeline.js → fase 1 de filtrado de noticias.
  */
 
-import { httpGet } from '../utils/httpClient.js';
-import { config } from '../config.js';
-
-export async function fetchFinancialNews(count = 30) {
-  if (!config.FINNHUB_API_KEY) return [];
-  const url = `https://finnhub.io/api/v1/news?category=general&token=${config.FINNHUB_API_KEY}`;
-  const articles = await httpGet(url);
-  return articles.slice(0, count).map((a) => ({
-    headline: a.headline ?? '',
-    summary: a.summary ?? '',
-  }));
-}
-
-export function filterNewsByRelevance(articles, question) {
-  const keywords = question
-    .toLowerCase()
-    .split(/\W+/)
-    .filter((w) => w.length > 4);
-  if (keywords.length === 0) return articles;
-  return articles.filter((a) => {
-    const text = `${a.headline} ${a.summary}`.toLowerCase();
-    return keywords.some((kw) => text.includes(kw));
-  });
-}

spaces/modernfinbert/app.py

@@ -2,9 +2,6 @@ import gradio as gr
 import spaces
 from transformers import pipeline
 
-# Load model on CUDA at module level.
-# Outside @spaces.GPU a PyTorch CUDA emulation is active,
-# so this works even when no real GPU is allocated yet.
 print("Loading tabularisai/ModernFinBERT on cuda...")
 classifier = pipeline(
     "text-classification",
@@ -23,15 +20,12 @@ def predict_sentiment(text_block):
     if not text_block:
         return []
 
-    # Split by newline, strip, drop empties
     texts = [t.strip() for t in text_block.splitlines() if t.strip()]
     if not texts:
         return []
 
-    # Batch inference
     raw_results = classifier(texts, batch_size=32)
 
-    # Normalise output
     results = [
         {"label": r["label"], "score": float(r["score"])}
         for r in raw_results

spaces/qwen3-8b/app.py

@@ -7,8 +7,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"
 
 print(f"Loading {MODEL_ID}...")
-# Load model on CUDA at module level.
-# Outside @spaces.GPU a PyTorch CUDA emulation is active.
+
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
@@ -31,13 +30,12 @@ SYSTEM_PROMPT = (
 
 def extract_json(text: str) -> dict:
     """Try to extract a JSON object from the model output."""
-    # Try direct JSON parse first
+
     try:
         return json.loads(text)
     except json.JSONDecodeError:
         pass
 
-    # Look for JSON block inside markdown or raw text
     match = re.search(r"\{.*\}", text, re.DOTALL)
     if match:
         try: