por fin corregido problema links, añadido ejemplo nuevo

article_app.py

@@ -2,36 +2,39 @@ article = """
 <img src="https://www.iic.uam.es/wp-content/uploads/2017/12/IIC_logoP.png">
 <img src="https://drive.google.com/uc?export=view&id=1S8v94q39QRCfmVTMvjLCACmhMe9lJQdc">
 
-<p style="text-align: justify;"> This app is developed by <a href="https://www.iic.uam.es/">IIC - Instituto de Ingeniería del Conocimiento</a> as part of the <a href="https://www.eventbrite.com/e/registro-hackathon-de-pln-en-espanol-273014111557">Somos PLN Hackaton 2022.</a> 
+<p style="text-align: justify;"> This app is developed by <a href="https://www.iic.uam.es/">IIC - Instituto de Ingeniería del Conocimiento</a> as part of the <a href="https://www.eventbrite.com/e/registro-hackathon-de-pln-en-espanol-273014111557">Somos PLN Hackaton 2022.</a>
 
 Concretely, the members of the team are:
 
-1. Alejandro Vaca Serrano: <a href="https://huggingface.co/avacaondata">@avacaondata</a>
-2. David Betancur Sánchez: <a href="https://huggingface.co/Dabs">@Dabs</a>
-3. Alba Segurado: <a href="https://huggingface.co/alborotis">@alborotis</a>
-4. Álvaro Barbero Jiménez: <a href="https://huggingface.co/albarji">@albarji</a>
-5. Guillem García Subies: <a href="https://huggingface.co/GuillemGSubies">@GuillemGSubies</a>
+<ol>
+    <li>Alejandro Vaca Serrano: <a href="https://huggingface.co/avacaondata">@avacaondata</a></li>
+    <li>David Betancur Sánchez: <a href="https://huggingface.co/Dabs">@Dabs</a></li>
+    <li>Alba Segurado: <a href="https://huggingface.co/alborotis">@alborotis</a></li>
+    <li>Álvaro Barbero Jiménez: <a href="https://huggingface.co/albarji">@albarji</a></li>
+    <li>Guillem García Subies: <a href="https://huggingface.co/GuillemGSubies">@GuillemGSubies</a></li>
+</ol>
 
 The objective of this app is to expand the existing tools regarding long form question answering in Spanish. In fact, multiple novel methods (in Spanish)
 have been introduced to build this app. 
 The reason for including audio as a possible input and always as an output is because we wanted to make the App much more accessible to people that cannot read or write.
 Below you can find all the pieces that form the system.
-
-1. <a href="https://hf.co/IIC/wav2vec2-spanish-multilibrispeech">Speech2Text</a>: For this we finedtuned a multilingual Wav2Vec2, as explained in the attached link. We use this model to process audio questions.
-2. <a href="https://hf.co/IIC/dpr-spanish-passage_encoder-allqa-base">Dense Passage Retrieval for Context</a>: Dense Passage Retrieval is a methodology <a href="https://arxiv.org/abs/2004.04906">developed by Facebook</a> which is currently the SoTA for Passage Retrieval,
-that is, the task of getting the most relevant passages to answer a given question with. You can find details about how it was trained on the link attached to the name. 
-3. <a href="https://hf.co/IIC/dpr-spanish-question_encoder-allqa-base">Dense Passage Retrieval for Question</a>: It is actually part of the same thing as the above. For more details, go to the attached link.
-4. <a href="https://hf.co/sentence-transformers/distiluse-base-multilingual-cased-v1">Sentence Encoder Ranker</a>: To rerank the candidate contexts retrieved by dpr for the generative model to see. This also selects the top 5 passages for the model to read, it is the final filter before the generative model.
-5. <a href="https://hf.co/IIC/mt5-base-lfqa-es">Generative Long-Form Question Answering Model</a>: For this we used either mT5 (the one attached) or <a href="https://hf.co/IIC/mbart-large-lfqa-es">mBART</a>. This generative model receives the most relevant
-passages and uses them to generate an answer to the question. In the attached link there are more details about how we trained it etc.
+<ol>
+    <li><a href="https://hf.co/IIC/wav2vec2-spanish-multilibrispeech">Speech2Text</a>: For this we finedtuned a multilingual Wav2Vec2, as explained in the attached link. We use this model to process audio questions.</li>
+    <li><a href="https://hf.co/IIC/dpr-spanish-passage_encoder-allqa-base">Dense Passage Retrieval for Context</a>: Dense Passage Retrieval is a methodology <a href="https://arxiv.org/abs/2004.04906">developed by Facebook</a> which is currently the SoTA for Passage Retrieval, that is, the task of getting the most relevant passages to answer a given question with. You can find details about how it was trained on the link attached to the name. </li>
+    <li><a href="https://hf.co/IIC/dpr-spanish-question_encoder-allqa-base">Dense Passage Retrieval for Question</a>: It is actually part of the same thing as the above. For more details, go to the attached link.</li>
+    <li><a href="https://hf.co/sentence-transformers/distiluse-base-multilingual-cased-v1">Sentence Encoder Ranker</a>: To rerank the candidate contexts retrieved by dpr for the generative model to see. This also selects the top 5 passages for the model to read, it is the final filter before the generative model.</li>
+    <li><a href="https://hf.co/IIC/mt5-base-lfqa-es">Generative Long-Form Question Answering Model</a>: For this we used either mT5 (the one attached) or <a href="https://hf.co/IIC/mbart-large-lfqa-es">mBART</a>. This generative model receives the most relevant passages and uses them to generate an answer to the question. In the attached link there are more details about how we trained it etc.</li>
+</ol>
 
 On the other hand, we uploaded, and in some cases created, datasets in Spanish to be able to build such a system.
 
-1. <a href="https://hf.co/datasets/IIC/spanish_biomedical_crawled_corpus">Spanish Biomedical Crawled Corpus</a>. Used for finding answers to questions about biomedicine. (More info in the link.)
-2. <a href="https://hf.co/datasets/IIC/lfqa_spanish">LFQA_Spanish</a>. Used for training the generative model. (More info in the link.)
-3. <a href="https://hf.co/datasets/squad_es">SQUADES</a>. Used to train the DPR models. (More info in the link.)
-4. <a href="https://hf.co/datasets/IIC/bioasq22_es">BioAsq22-Spanish</a>. Used to train the DPR models. (More info in the link.)
-5. <a href="https://hf.co/datasets/PlanTL-GOB-ES/SQAC">SQAC (Spanish Question Answering Corpus)</a>. Used to train the DPR models. (More info in the link.)
+<ol>
+    <li><a href="https://hf.co/datasets/IIC/spanish_biomedical_crawled_corpus">Spanish Biomedical Crawled Corpus</a>. Used for finding answers to questions about biomedicine. (More info in the link.)</li>
+    <li><a href="https://hf.co/datasets/IIC/lfqa_spanish">LFQA_Spanish</a>. Used for training the generative model. (More info in the link.)</li>
+    <li><a href="https://hf.co/datasets/squad_es">SQUADES</a>. Used to train the DPR models. (More info in the link.)</li>
+    <li><a href="https://hf.co/datasets/IIC/bioasq22_es">BioAsq22-Spanish</a>. Used to train the DPR models. (More info in the link.)</li>
+    <li><a href="https://hf.co/datasets/PlanTL-GOB-ES/SQAC">SQAC (Spanish Question Answering Corpus)</a>. Used to train the DPR models. (More info in the link.)</li>
+</ol>
 </p>
 """
 # 1HOzvvgDLFNTK7tYAY1dRzNiLjH41fZks
@@ -79,6 +82,17 @@ examples = [
         250,
         False,
     ],
+    [
+        "¿Qué es la anorexia?",
+        "vacio.flac",
+        "vacio.flac",
+        60,
+        8,
+        3,
+        1.0,
+        250,
+        False,
+    ],
     [
         "¿Por qué sentimos ansiedad?",
         "vacio.flac",