Update modules/ui/ui.py

modules/ui/ui.py

@@ -630,36 +630,84 @@ def display_videos_and_info(lang_code, landing_t):
             Competing for <strong>$950K USD</strong> in prizes across multiple prestigious competitions.
             </div>
             
-            <div class="section-title">NVIDIA Inception Demo Pitch Application – GTC 2026</div>
-            <strong>March 16–19 in San Jose, California</strong><br>
-            <em>This is a unique opportunity for startups leveraging NVIDIA technologies in innovative ways to showcase their work. 
-            By completing the form below, you’ll be considered to present a live, 5-minute demo or pitch at GTC, 
-            highlighting your startup’s progress and how NVIDIA technology is accelerating your success</em><br>
-            More info: https://www.nvidia.com/gtc/?ncid=GTC-NV7HIY70L
-
-            <div class="section-title">Kaggle/Google - $100K</div>
-            <strong>Explainable AI Models</strong><br>
-            <em>Google Tunix Hack - Train a model to show its work</em><br>
-            <p>Most open-source or open-weight language models can give you an answer. But they typically don’t ‘show their work’ 
-            - the steps they went through to arrive at that conclusion in a consistent manner. Here, you’ll use Tunix, 
-            Google’s new JAX-native library for LLM post-training, to train a model to show its work by laying out a 
-            reasoning trace before landing on an answer.</p>
-            More info: https://kaggle.com/competitions/google-tunix-hackathon
+            <details class="competition-details">
+                <summary class="competition-summary">
+                    <strong>NVIDIA Inception Demo Pitch – GTC 2026</strong> - March 16–19 in San Jose, California
+                </summary>
+                <div class="competition-content">
+                <p>This is a unique opportunity for startups leveraging NVIDIA technologies in innovative ways to showcase their work. 
+                By completing the form below, you'll be considered to present a live, 5-minute demo or pitch at GTC, 
+                highlighting your startup's progress and how NVIDIA technology is accelerating your success.</p>
+                <p>More info: https://www.nvidia.com/gtc/?ncid=GTC-NV7HIY70L</p>
+                </div>
+            </details>
             
-            <div class="section-title">Learning Tools Competition - $100K</div>
-            <strong>Datasets for Education Innovation</strong><br>
-            <em> Phase II </em><br>
-            <p>The 2026 Tools Competition launches at a time of rapid change, rising urgency, 
-            and widespread demand for understanding and direction amidst evolving technology. 
-            Learners of all ages face new demands driven by AI, 
-            shifting labor markets, and persistent inequities. From early learning to workforce development, 
-            they need tools that are more effective, inclusive, and future-ready.</p>
-            More info: https://tools-competition.org/26-overview/
-
+            <details class="competition-details">
+                <summary class="competition-summary">
+                    <strong>Kaggle/Google Tunix Hackathon</strong> - Explainable AI Models
+                </summary>
+                <div class="competition-content">
+                <p>Most open-source or open-weight language models can give you an answer. But they typically don't 'show their work' 
+                - the steps they went through to arrive at that conclusion in a consistent manner. Here, you'll use Tunix, 
+                Google's new JAX-native library for LLM post-training, to train a model to show its work by laying out a 
+                reasoning trace before landing on an answer.</p>
+                <p>More info: https://kaggle.com/competitions/google-tunix-hackathon</p>
+                </div>
+            </details>
+            
+            <details class="competition-details">
+                <summary class="competition-summary">
+                    <strong>Tools Competition – Dataset Track</strong> - Datasets for Education Innovation
+                </summary>
+                <div class="competition-content">
+                <p>The 2026 Tools Competition launches at a time of rapid change, rising urgency, 
+                and widespread demand for understanding and direction amidst evolving technology. 
+                Learners of all ages face new demands driven by AI, 
+                shifting labor markets, and persistent inequities. From early learning to workforce development, 
+                they need tools that are more effective, inclusive, and future-ready.</p>
+                <p>More info: https://tools-competition.org/26-overview/</p>
+                </div>
+            </details>            
             </div>
             """
         }
         
+        # Si necesitas agregar estilos CSS para los detalles
+        st.markdown("""
+        <style>
+        .competition-details {
+            margin-bottom: 15px;
+            border: 1px solid #e0e0e0;
+            border-radius: 8px;
+            padding: 5px;
+        }
+        
+        .competition-summary {
+            padding: 12px 15px;
+            background-color: #f8f9fa;
+            cursor: pointer;
+            font-weight: 500;
+            border-radius: 6px;
+            transition: background-color 0.3s;
+        }
+        
+        .competition-summary:hover {
+            background-color: #e9ecef;
+        }
+        
+        .competition-content {
+            padding: 15px;
+            background-color: white;
+            border-top: 1px solid #e0e0e0;
+            border-radius: 0 0 8px 8px;
+        }
+        
+        .competition-content p {
+            margin-bottom: 8px;
+        }
+        </style>
+        """, unsafe_allow_html=True)
+        
         content = competitions_text.get(lang_code, competitions_text['en'])
         st.markdown(content, unsafe_allow_html=True)