Fix

README.md

@@ -11,10 +11,10 @@ pinned: false
 license: mit
 short_description: Demo of inference and SPARQL execution
 preload_from_hub:
-- daniel-dona/sparql-model-era-lora-128-qwen3-0.6b 
-- daniel-dona/sparql-model-era-lora-128-qwen3-1.7b 
+#- daniel-dona/sparql-model-era-lora-128-qwen3-0.6b 
+#- daniel-dona/sparql-model-era-lora-128-qwen3-1.7b 
 - daniel-dona/sparql-model-era-lora-128-qwen3-4b 
-- daniel-dona/sparql-model-era-lora-128-qwen3-0.6b-grpo1
+#- daniel-dona/sparql-model-era-lora-128-qwen3-0.6b-grpo1
 ---
 
 Deployment demo for ERA finetuned model for SPARQL generation 

app.py

@@ -147,7 +147,7 @@ def generate_sparql(message, model_name, model_temperature, semantic_results, mo
 
     if semantic_results is not None:
 
-        prompt = prompt_sparql_semantic.replace("%nlq", message).replace("%semantic", semantic_results)
+        prompt = prompt_sparql_semantic.replace("%nlq", message).replace("%semantic", json.dumps(semantic_results))
 
     else:
 

src/SemanticSearch.py

@@ -7,6 +7,8 @@ import requests
 import itertools
 import torch
 
+import spaces
+
 import chromadb
 
 from SPARQLWrapper import SPARQLWrapper, JSON
@@ -24,7 +26,7 @@ WHERE {
   ?ne_uri a ?class_uri.
   ?ne_uri ne:prefLabel|ne:altLabel|rdfs:label ?ne_label .
   ?class_uri ne:prefLabel|ne:altLabel|rdfs:label ?class_label .
-  ?ne_uri era:inCountry <http://publications.europa.eu/resource/authority/country/ESP> .
+  #?ne_uri era:inCountry <http://publications.europa.eu/resource/authority/country/ESP> .
   
   #FILTER(STRSTARTS(STR(?ne_uri), "http://data.europa.eu/949/"))
   #FILTER(lang(?ne_label) = "en" || lang(?ne_label) = "")
@@ -199,17 +201,19 @@ class SemanticSearch:
 
         self.collection = client.get_collection(name=self.collection_name)
 
-
-    def extract(self, nlq: str, n_results:int=25, str_match:bool=True, rerank:bool=True):
+    @spaces.GPU
+    def extract(self, nlq: str, n_results:int=10, n_candidates:int=100, str_match:bool=True, rerank:bool=True):
 
         embedding = self.get_text_embeddings_local([nlq])[0].tolist()
 
         results = self.collection.query(
             query_embeddings=[embedding],
-            n_results=n_results,
+            n_results=n_candidates,
             include=["documents", "distances", "metadatas", "uris"]
         )
 
+        print(results)
+
         if rerank:
 
             documents = [f"'{item.lower()}' ({cls['class_label']})" for item, cls in zip(results["documents"][0], results["metadatas"][0])]