chore: add announcements

app.py

@@ -5,45 +5,55 @@ from datetime import datetime
 # Organization details
 ORG_NAME = "Narrative I/O"
 DESCRIPTION = """
-Narrative I/O is at the forefront of data transformation and enrichment. We specialize in:
+Narrative I/O builds tools and infrastructure for data processing and ML model development:
 
-* Building scalable data enrichment pipelines
-* Developing cutting-edge machine learning models
-* Creating efficient data processing solutions
-* Enabling businesses to leverage their data assets effectively/
+* Data normalization and schema standardization
+* LLM fine-tuning and deployment infrastructure
+* Function calling and tool integration frameworks
+* Open-source ML model and dataset publishing
 
-Our platform combines advanced ML capabilities with robust data processing to deliver 
-actionable insights and enhanced data quality for our clients.
+Our platform focuses on solving practical data integration challenges through 
+standardized APIs, normalized schemas, and deployable ML models.
 """
 
 # News/Announcements
 ANNOUNCEMENTS = [
     {
-        "date": "2024-03-15",
-        "title": "New Model Release",
-        "content": "Introducing our latest data enrichment model for enhanced entity resolution."
+        "date": "2025-01-07",
+        "title": "🚀Narrative Model Studio Released",
+        "content": "Today we're making Narrative Model Studio available to the public.  [Model Studio](https://www.narrative.io/products/model-studio) is a platform to make it easy for *anyone* to train a LLM.  Model Studio build's on top of our best in class Data Collaboration Platoform and Data Marketplace to all users to source, normalize, and package data for training, with not technical expertise required.  That data can then be used to fine-tune an LLM for enterprise specific use cases and further deployed to inference endpoints."
     },
     {
-        "date": "2024-02-28",
-        "title": "Dataset Publication",
-        "content": "Released a new benchmark dataset for entity matching evaluation."
-    }
-]
-
-# Public Models
-PUBLIC_MODELS = [
+        "date": "2025-01-07",
+        "title": "🔄 Rosetta Stone 2.0 Private Model Release",
+        "content": "Today we are releasing our [Rosetta Stone 2.0](https://www.narrative.io/products/rosetta-stone) Private Model.  When we started Narrative we set out to make it easier for everyone to work with data.  One of the biggest challenges we found users faced was the lack of a normalization layer across different data sources, both internally and externally.  We build Rosetta Stone to tackle this problem by using AI to normalize data to a common ontology and syntax, and making that normalized data directly queryable.  Rosetta Stone 2.0 builds on the first version by leveraging the latest LLM Models and fine tuning them to perform even better than the first version.  This release also marks the first time that the model is available for users to host on their own infrastructure, further reducing governance and compliance burdens."
+    },
     {
-        "name": "entity-matcher-v1",
-        "description": "High-performance entity matching model",
-        "link": "https://huggingface.co/Narrative-IO/entity-matcher-v1"
+        "date": "2025-01-07",
+        "title": "🎯 Public Function Calling Models + Dataset Released",
+        "content": "Today we're excited to announce the release of our specialized function calling models and accompanying dataset. We've launched both 3B and 8B parameter models (with a 70B version coming soon) that have been meticulously fine-tuned for function calling applications. These models are built on our newly created normalized function calling dataset, which standardizes the approach to tool use in LLMs.  Our implementation adopts a more standardized JSON schema for function calls, coupled with token enforcement through our [LLM Tools](https://github.com/narrative-io/narrative-llm-tools) to ensure models consistently respond using available functions. This makes these models particularly well-suited for enterprise applications where LLMs operate as backend processors, making tool calls on users' behalf rather than generating direct responses (though user interaction is still possible through a dedicated respond_to_user tool)."
     },
     {
-        "name": "data-enrichment-v2",
-        "description": "Advanced data enrichment pipeline",
-        "link": "https://huggingface.co/Narrative-IO/data-enrichment-v2"
+        "date": "2025-01-07",
+        "title": "🛠️ Narrative LLM Tools Released",
+        "content": "Today, we're excited to announce the release of our [LLM Tools Github repository](https://github.com/narrative-io/narrative-llm-tools).  Narrative LLM Tools are a collection of tools that make it easier to use LLMs trained in Narrative's Model Studio alongside HuggingFace's Inference Endpoints."
     }
 ]
 
+# Public Models
+# PUBLIC_MODELS = [
+#     {
+#         "name": "entity-matcher-v1",
+#         "description": "High-performance entity matching model",
+#         "link": "https://huggingface.co/Narrative-IO/entity-matcher-v1"
+#     },
+#     {
+#         "name": "data-enrichment-v2",
+#         "description": "Advanced data enrichment pipeline",
+#         "link": "https://huggingface.co/Narrative-IO/data-enrichment-v2"
+#     }
+# ]
+
 # Public Datasets
 PUBLIC_DATASETS = [
     {
@@ -56,15 +66,8 @@ PUBLIC_DATASETS = [
 # Demo configurations
 DEMOS = [
     {
-        "title": "Entity Matching Demo",
-        "description": "Try our entity matching model with your own data",
-        "function": lambda x, y: f"Similarity score: {0.85}"  # Placeholder function
+        "title": "Coming Soon",
     },
-    {
-        "title": "Data Quality Assessment",
-        "description": "Check your data quality metrics",
-        "function": lambda x: {"completeness": 0.95, "accuracy": 0.92}  # Placeholder function
-    }
 ]
 
 # Custom CSS for styling
@@ -101,12 +104,16 @@ h2, strong, h3 {
     margin-bottom: 15px;
 }
 
-p, span, li {
+p, span, li, em {
     font-size: 14px;
     color: #415290;  /* text-color-secondary */
     margin-bottom: 15px;
 }
 
+em {
+    font-style: italic;
+}
+
 .news-item {
     padding: 10px 0;
     border-bottom: 1px solid #ced4da;  /* surface-d */
@@ -143,8 +150,20 @@ def create_organization_page():
                     gr.Markdown(f"# {ORG_NAME}", elem_classes="title")
                     gr.Markdown(DESCRIPTION)
                     with gr.Row():
-                        gr.Button("Website", link="https://narrative.io", elem_classes="gr-button")
-                        gr.Button("GitHub", link="https://github.com/narrative-io", elem_classes="gr-button")
+                        gr.HTML("""
+                            <div style="display: flex; gap: 20px;">
+                                <a href="https://narrative.io" target="_blank" class="icon-link">
+                                    <svg xmlns="http://www.w3.org/2000/svg" height="24" width="24" viewBox="0 0 24 24" fill="#0922A6">
+                                        <path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm-1 17.93c-3.95-.49-7-3.85-7-7.93 0-.62.08-1.21.21-1.79L9 15v1c0 1.1.9 2 2 2v1.93zm6.9-2.54c-.26-.81-1-1.39-1.9-1.39h-1v-3c0-.55-.45-1-1-1H8v-2h2c.55 0 1-.45 1-1V7h2c1.1 0 2-.9 2-2v-.41c2.93 1.19 5 4.06 5 7.41 0 2.08-.8 3.97-2.1 5.39z"/>
+                                    </svg>
+                                </a>
+                                <a href="https://github.com/narrative-io" target="_blank" class="icon-link">
+                                    <svg xmlns="http://www.w3.org/2000/svg" height="24" width="24" viewBox="0 0 24 24" fill="#0922A6">
+                                        <path d="M12 2A10 10 0 0 0 2 12c0 4.42 2.87 8.17 6.84 9.5.5.08.66-.23.66-.5v-1.69c-2.77.6-3.36-1.34-3.36-1.34-.46-1.16-1.11-1.47-1.11-1.47-.91-.62.07-.6.07-.6 1 .07 1.53 1.03 1.53 1.03.87 1.52 2.34 1.07 2.91.83.09-.65.35-1.09.63-1.34-2.22-.25-4.55-1.11-4.55-4.92 0-1.11.38-2 1.03-2.71-.1-.25-.45-1.29.1-2.64 0 0 .84-.27 2.75 1.02.79-.22 1.65-.33 2.5-.33.85 0 1.71.11 2.5.33 1.91-1.29 2.75-1.02 2.75-1.02.55 1.35.2 2.39.1 2.64.65.71 1.03 1.6 1.03 2.71 0 3.82-2.34 4.66-4.57 4.91.36.31.69.92.69 1.85V21c0 .27.16.59.67.5C19.14 20.16 22 16.42 22 12A10 10 0 0 0 12 2z"/>
+                                    </svg>
+                                </a>
+                            </div>
+                        """)
                 
                 # News and Announcements
                 with gr.Column(elem_classes="card"):
@@ -153,27 +172,28 @@ def create_organization_page():
                         with gr.Row(elem_classes="news-item"):
                             gr.Markdown(f"""
                             **{announcement['title']}**  
-                            <span class="news-date">{format_date(announcement['date'])}</span>  
+                            ### {format_date(announcement['date'])} 
                             {announcement['content']}
                             """)
                 
                 # Interactive Demos
                 with gr.Column(elem_classes="card"):
                     gr.Markdown("## Interactive Demos")
+                    gr.Markdown("Coming Soon")
 
              # Right section (1/3 width)
             with gr.Column(scale=1):
-                # Public Models card
-                with gr.Column(elem_classes="card"):
-                    gr.Markdown("## Public Models")
-                    for model in PUBLIC_MODELS:
-                        with gr.Row(elem_classes="model-item"):
-                            gr.Markdown(f"""
-                            **{model['name']}**  
-                            {model['description']}  
-                            [View Model]({model['link']})
-                            """)
-                    gr.Markdown("[View All Models →](https://huggingface.co/collections/narrative-io/public-models-6777e756fe3748f33c403a31)", elem_classes="view-all-link")
+                # # Public Models card
+                # with gr.Column(elem_classes="card"):
+                #     gr.Markdown("## Public Models")
+                #     for model in PUBLIC_MODELS:
+                #         with gr.Row(elem_classes="model-item"):
+                #             gr.Markdown(f"""
+                #             **{model['name']}**  
+                #             {model['description']}  
+                #             [View Model]({model['link']})
+                #             """)
+                #     gr.Markdown("[View All Models →](https://huggingface.co/collections/narrative-io/public-models-6777e756fe3748f33c403a31)", elem_classes="view-all-link")
                 
                 # Public Datasets card
                 with gr.Column(elem_classes="card"):