añadidos examples y mejorada app

app.py

@@ -117,7 +117,7 @@ def sort_on_similarity(question, contexts, include_rank: int = 5):
     ]
     text_pairs = [[question, ctx] for ctx in contexts]
     similarity_scores = crossencoder.predict(text_pairs)
-    similarity_scores = sim_scores_ss * similarity_scores
+    similarity_scores = np.array(sim_scores_ss) * similarity_scores
     similarity_ranking_idx = np.flip(np.argsort(similarity_scores))
     return [contexts[idx] for idx in similarity_ranking_idx][:include_rank]
 
@@ -174,7 +174,7 @@ def search_and_answer(
         )
         print(f"Transcripted question: *** {question} ****")
     if any([any([term in word.lower() for term in covidterms]) for word in question.split(" ")]):
-        return "Del COVID no queremos saber ya más nada, lo sentimos, pregúntame sobre otra cosa :P ", "ni contexto ni contexta.", "tmptdsnrh_8.flac"
+        return "Del COVID no queremos saber ya más nada, lo sentimos, pregúntame sobre otra cosa :P ", "ni contexto ni contexta.", "audio_troll.flac"
     contexts = query_index(question)
     contexts = sort_on_similarity(question, contexts)
     context = create_context(contexts)

article_app.py

@@ -86,6 +86,18 @@ examples = [
         "wav2vec2-iic",
         False,
     ],
+    [
+        "¿Qué es la gripe aviar?",
+        "vacio.flac",
+        "vacio.flac",
+        50,
+        8,
+        3,
+        1.0,
+        250,
+        "wav2vec2-iic",
+        False,
+    ],
     [
         "¿Qué es la tecnología CRISPR?",
         "vacio.flac",