feat: improve main page

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 assets/paper_summary.jpg filter=lfs diff=lfs merge=lfs -text
 assets/paper_summary.png filter=lfs diff=lfs merge=lfs -text
+assets/output_tracks.png filter=lfs diff=lfs merge=lfs -text

index.html

@@ -233,6 +233,11 @@
       </p>
     </div>
 
+    <div class="paper-summary">
+		<!-- <h2>📄 A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction</h2> -->
+		<img src="assets/paper_summary.png" alt="NTv3 Paper Summary" />
+    </div>
+
     <div class="why-ntv3">
       <h2>✨ Why NTv3?</h2>
       <ul>
@@ -348,11 +353,14 @@ print(out.logits.shape)       # (B, L, V = 11)
 print(len(out.hidden_states)) # convs + transformers + deconvs
 print(len(out.attentions))    # equals transformer layers = 12
 </code></pre></div>
+      <p>Model embeddings can be used for fine-tuning on downstream tasks.</p>
+
+      <p style="margin-top: 40px;">TO DO: add pipeline for fine-tuning on functional tracks or genome annotation.</p>
       </div>
       
       <div class="card">
         <h2>💻 Use a post-trained model</h2>
-        <p>Here is a quick example of how to use the post-trained NTv3 650M model on a human genomic window.</p>
+        <p>Here is a quick example of how to use the post-trained NTv3 650M model to predict tracks for a human genomic window.</p>
         <div class="code"><pre><code class="language-python">from transformers import AutoConfig
 
 model_name = "InstaDeepAI/NTv3_650M"
@@ -375,13 +383,33 @@ out = pipe(
 print(out.bigwig_tracks_logits.shape)   # functional track predictions
 print(out.bed_tracks_logits.shape)      # genome annotation predictions
 print(out.mlm_logits.shape)             # MLM logits: (B, L, V = 11)</code></pre></div>
+      <p>Predictions can also be plotted for a subset of functional tracks and genomic elements:</p>
+      <div class="code"><pre><code class="language-python">tracks_to_plot = {
+    "K562 RNA-seq": "ENCSR056HPM",
+    "K562 DNAse": "ENCSR921NMD",
+    "K562 H3k4me3": "ENCSR000DWD",
+    "K562 CTCF": "ENCSR000AKO",
+    "HepG2 RNA-seq": "ENCSR561FEE_P",
+    "HepG2 DNAse": "ENCSR000EJV",
+    "HepG2 H3k4me3": "ENCSR000AMP",
+    "HepG2 CTCF": "ENCSR000BIE",
+}
+elements_to_plot = ["protein_coding_gene", "exon", "intron", "splice_donor", "splice_acceptor"]
+
+out = pipe(
+    {"chrom": "chr19", "start": 6_700_000, "end": 6_831_072, "species": "human"},
+    plot=True,
+    tracks_to_plot=tracks_to_plot,
+    elements_to_plot=elements_to_plot,
+)</code></pre></div>
+      <img src="assets/output_tracks.png" alt="Output tracks visualization" style="max-width: 100%; margin-top: 20px;" />
           </div>
         </div>
           
-    <div class="paper-summary">
+    <!-- <div class="paper-summary">
 		<h2>📄 A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction</h2>
 		<img src="assets/paper_summary.png" alt="NTv3 Paper Summary" />
-    </div>
+    </div> -->
 
     <p class="footer">
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