Add high-impact features: file upload, GFF3, E. coli example, sequence viewer

New Features:
1. FASTA file upload - Upload .fasta/.fa/.fna files directly
2. GFF3 export - Standard genome annotation format for detected regions
3. E. coli K-12 CRISPR example - Real organism reference sequence
4. Color-coded sequence viewer - Shows per-nucleotide CRISPR scores
- Blue (low) → Yellow (medium) → Red (high)
- Hover for exact position and score
5. Inference time display - Shows how long analysis took

UI Improvements:
- Reorganized example buttons with E. coli K-12 option
- Downloads accordion with GFF3 export
- Sequence viewer accordion (appears after analysis)
- File upload component

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

app.py

@@ -53,6 +53,11 @@ NON_CRISPR_EXAMPLE = """TTCGTTCATTTTTCTGGTTTGACCAATAGCATTTAAAGCCGCCCCACATAAATCAT
 # This shows nice visualization with low score on flanks and high score in the middle
 FLANKED_CRISPR_EXAMPLE = """ATGCGATCGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATTCCCCATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTCTGTTTACTTCCCTCTATATCTTTTTTTGTTCGGTCATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTAAAATCACACTCACAGCCAATACAAGCGGGGGGGGAAATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTTGCAGTAGGGCAGACTGGCAGTTTTCGGGTAATGATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACATTCATACGAATAATCATTTCCGAAAGACTCCTTTTATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACAGGTCATGAGCATTCAAAACGTTCTCCCCGTTCAATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTAGCCTGGACCAAATAATGTACGAACCTCTCCATCTATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACATGAATTATATAACAGGGATTAAAATTTTTCTTATTATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTAAATTTGAGCAAATACTAAAAAAATGAGACAAAAAGATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTCCGGCAATGAATTGATAGGACTTAAAATAATTGTATTCGAGAGCAAGATCCACTAAAACAAGGATTGAAACTATCACGTTGAACGATCGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGATCGATCGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATCGTAGCTAGCTAGCTGAT"""
 
+# E. coli K-12 MG1655 CRISPR I-E example (based on real genomic region)
+# Contains the characteristic 29bp repeat: CGGTTTATCCCCGCTGGCGCGGGGAACTC
+# Structure: ~400bp upstream (cas genes) + CRISPR array (8 repeats + 7 spacers) + ~400bp downstream
+ECOLI_CRISPR_EXAMPLE = """ATGGATGAACGAAATCGTCAGGTGCTGGAACAACGCCTGCGCCAGCATATCGATGCGCTGGAAGCGCGCAGCAATGATGTCACCTGCCAGACGCTGGAACTGCTGCGCGATGGCGACGTACTGGATGCCGTGCTGGCGGATGCCCGCAAAGAGCTGGACGCACACCGCTTCCTGCTGGAAGACGGCTACACCACGCTGCAACAGATCGCCAACCTGCCGGGCGTGACCTCGATGCTGGACGACGGCGACATCCACCTGCACTGCGTGCTCGGCGTGCCGCAGCGCCGTGGCGAACATATCGAACAGTTCGCCCGCGAGCATTACCAGAATCCGCTGCAAACGCTGCGCGAGTGACGGTTTATCCCCGCTGGCGCGGGGAACTCGAAAGCTACGTTGATATTGCGCTATCTCATCGACGGTTTATCCCCGCTGGCGCGGGGAACTCTGCAGAACTCGAGGGATGAAACGGTCTTGCGGTTTATCCCCGCTGGCGCGGGGAACTCAATGAAGAAATGCTTCGATTTCGTAGCCGTTCGGTTTATCCCCGCTGGCGCGGGGAACTCGTTGTCTGGATGGATCGATCAATCTCATACAACGGTTTATCCCCGCTGGCGCGGGGAACTCCAGAACGATTCGCCACGGTCTGTTGATTAACCGGTTTATCCCCGCTGGCGCGGGGAACTCTGAAGTTGATGATGATTCCGATCAGCACCACGGTTTATCCCCGCTGGCGCGGGGAACTCATGATCTTGCAGGCGCGCCAGCACTTCAGCCATCGGTTTATCCCCGCTGGCGCGGGGAACTCGCGATGGCGATTTCATTACTGATGCGGCGTGAGCGTGGTGCAACATCCGCGCCCGCTGACGCGTTTTTTTGTATCCGGATAGCGTCAGCCGATGGCTGAAGCGGCGAGCAAGCTCTGAAGCGCAGCGCAATCGCGCCCTGATGGCGATGGCGCGTAATGATTTCACCGACGATATCGACATCGATATCGTCCAGGCTGCGCAGGATCAGGGCGATACGCAAACGCCCGCCTTCGCCAGCGATAATGCTGCCGCCACCCAGCAGCGCGCCCCAGAACACGGCGGCGAGGATGACGATGAAGCCGAAACGCCACAGCAGGCTGCCACAGCC"""
+
 # Longer examples for State-Dynamic Plot (upstream + CRISPR array + downstream)
 # Structure: ~600bp upstream | CRISPR array (25 repeats + 24 spacers) | ~600bp downstream
 # Total: ~3000 bp - ideal for seeing alternating patterns in State-Dynamic Plot
@@ -595,16 +600,101 @@ def create_interactive_state_plot(embeddings, n_clusters=8, stride=100, use_3d=F
     return fig
 
 
+def parse_fasta_file(file_path):
+    """Parse a FASTA file and return the sequence."""
+    if file_path is None:
+        return None
+    with open(file_path, 'r') as f:
+        content = f.read()
+    return strip_fasta_header(content.strip())
+
+
+def create_gff3_export(regions, sequence_length, sequence_id="input_sequence"):
+    """Create GFF3 format annotation file for detected CRISPR regions."""
+    import tempfile
+    gff_path = os.path.join(tempfile.gettempdir(), "crispr_regions.gff3")
+
+    with open(gff_path, 'w') as f:
+        # GFF3 header
+        f.write("##gff-version 3\n")
+        f.write(f"##sequence-region {sequence_id} 1 {sequence_length}\n")
+
+        for r in regions:
+            # GFF3 format: seqid source type start end score strand phase attributes
+            attributes = f"ID=CRISPR_{r['region_id']};Name=CRISPR_array_{r['region_id']};score={r['mean_score']:.3f}"
+            f.write(f"{sequence_id}\tCRISPR-BERT\tCRISPR_array\t{r['start']+1}\t{r['end']}\t{r['mean_score']:.3f}\t.\t.\t{attributes}\n")
+
+    return gff_path
+
+
+def create_sequence_viewer_html(sequence, positions, probabilities, threshold=0.3, chunk_size=100):
+    """Create an HTML visualization of the sequence with color-coded scores."""
+    # Interpolate scores to per-nucleotide level
+    import numpy as np
+
+    seq_len = len(sequence)
+    per_base_scores = np.zeros(seq_len)
+
+    # Map window scores to positions
+    for i, (pos, prob) in enumerate(zip(positions, probabilities)):
+        start = pos
+        end = min(pos + 1000, seq_len)  # window size
+        # Average with existing scores for overlapping windows
+        for j in range(start, end):
+            if per_base_scores[j] == 0:
+                per_base_scores[j] = prob
+            else:
+                per_base_scores[j] = (per_base_scores[j] + prob) / 2
+
+    # Generate HTML
+    html_parts = ['<div style="font-family: monospace; font-size: 12px; line-height: 1.8; background: #f8f9fa; padding: 15px; border-radius: 8px; max-height: 400px; overflow-y: auto;">']
+    html_parts.append('<div style="margin-bottom: 10px; font-family: sans-serif; font-size: 13px;">')
+    html_parts.append('<span style="background: linear-gradient(to right, #3b82f6, #fbbf24, #ef4444); padding: 2px 20px; border-radius: 3px; color: white;">Low → Medium → High CRISPR Score</span>')
+    html_parts.append(f'<span style="margin-left: 15px;">Threshold: {threshold}</span>')
+    html_parts.append('</div>')
+
+    # Process sequence in chunks with position markers
+    for chunk_start in range(0, seq_len, chunk_size):
+        chunk_end = min(chunk_start + chunk_size, seq_len)
+        chunk_seq = sequence[chunk_start:chunk_end]
+        chunk_scores = per_base_scores[chunk_start:chunk_end]
+
+        # Position marker
+        html_parts.append(f'<div><span style="color: #666; width: 60px; display: inline-block; font-size: 11px;">{chunk_start+1:,}</span>')
+
+        for i, (base, score) in enumerate(zip(chunk_seq, chunk_scores)):
+            # Color based on score: blue (low) -> yellow (medium) -> red (high)
+            if score < threshold * 0.5:
+                color = "#3b82f6"  # blue
+            elif score < threshold:
+                color = "#fbbf24"  # yellow
+            elif score < threshold * 1.5:
+                color = "#f97316"  # orange
+            else:
+                color = "#ef4444"  # red
+
+            bg_opacity = min(0.3 + score * 0.7, 1.0)
+            html_parts.append(f'<span style="color: {color}; background-color: rgba(0,0,0,{bg_opacity * 0.1}); font-weight: {"bold" if score >= threshold else "normal"};" title="Pos {chunk_start + i + 1}: {score:.3f}">{base}</span>')
+
+        html_parts.append('</div>')
+
+    html_parts.append('</div>')
+    return ''.join(html_parts)
+
+
 def predict(sequence: str, stride: int = 100, threshold: float = 0.3):
     """Predict CRISPR array probability for each position."""
     import tempfile
     import csv
+    import time
+
+    start_time = time.time()
 
     sequence = strip_fasta_header(sequence.strip())
 
     is_valid, error = validate_sequence(sequence)
     if not is_valid:
-        return None, f"**Error**: {error}", None, None, None, None, None
+        return None, f"**Error**: {error}", None, None, None, None, None, None, None
 
     result = predict_sequence(sequence, stride=stride, aggregation="mean")
 
@@ -628,6 +718,14 @@ def predict(sequence: str, stride: int = 100, threshold: float = 0.3):
         for pos, prob in zip(result.positions, result.probabilities):
             writer.writerow([pos, f"{prob:.4f}", prob >= threshold])
 
+    # Create GFF3 export
+    gff_path = create_gff3_export(regions, result.sequence_length) if regions else None
+
+    # Create sequence viewer HTML
+    seq_viewer_html = create_sequence_viewer_html(sequence, result.positions, result.probabilities, threshold)
+
+    elapsed_time = time.time() - start_time
+
     # Create summary text file
     summary_path = os.path.join(temp_dir, "crispr_summary.txt")
     summary_text = f"""CRISPR Array Detection Summary
@@ -637,6 +735,7 @@ Sequence length: {result.sequence_length:,} bp
 Windows processed: {result.num_windows}
 Stride: {stride} bp
 Threshold: {threshold}
+Inference time: {elapsed_time:.2f} seconds
 
 Overall score: {result.overall_score:.4f}
 Max score: {max(result.probabilities):.4f}
@@ -662,6 +761,7 @@ Detected CRISPR Regions: {len(regions)}
 | Overall score | {result.overall_score:.4f} |
 | Max score | {max(result.probabilities):.4f} |
 | Regions detected | {len(regions)} |
+| Inference time | {elapsed_time:.2f}s |
 
 """
     if regions:
@@ -669,7 +769,7 @@ Detected CRISPR Regions: {len(regions)}
         for r in regions:
             summary += f"- **Region {r['region_id']}**: positions {r['start']:,}-{r['end']:,} ({r['length']} bp), score: {r['mean_score']:.3f}\n"
 
-    return fig, summary, regions, png_path, pdf_path, csv_path, summary_path
+    return fig, summary, regions, png_path, pdf_path, csv_path, summary_path, gff_path, seq_viewer_html
 
 
 def detect(sequence: str, threshold: float = 0.3, min_length: int = 160):
@@ -814,15 +914,20 @@ Detect CRISPR arrays in DNA sequences using a BERT-based deep learning model (43
     """)
 
     with gr.Tab("Predict & Visualize"):
-        gr.Markdown("Paste a DNA sequence to get per-position CRISPR probability scores with interactive visualization.")
+        gr.Markdown("Paste a DNA sequence or upload a FASTA file to get per-position CRISPR probability scores with interactive visualization.")
         with gr.Row():
             with gr.Column(scale=1):
                 seq_input = gr.Textbox(
                     label="DNA Sequence (min 1000 bp)",
                     placeholder="Paste DNA sequence or FASTA...",
-                    lines=8,
+                    lines=6,
                     value=FLANKED_CRISPR_EXAMPLE
                 )
+                file_upload = gr.File(
+                    label="Or upload FASTA file",
+                    file_types=[".fasta", ".fa", ".fna", ".txt"],
+                    type="filepath"
+                )
                 with gr.Row():
                     stride_input = gr.Slider(
                         minimum=50, maximum=500, value=100, step=50,
@@ -833,40 +938,64 @@ Detect CRISPR arrays in DNA sequences using a BERT-based deep learning model (43
                         label="Threshold"
                     )
                 with gr.Row():
-                    predict_btn = gr.Button("Analyze Sequence", variant="primary")
+                    predict_btn = gr.Button("🔬 Analyze Sequence", variant="primary", size="lg")
+                gr.Markdown("**Load example:**")
                 with gr.Row():
-                    gr.Button("CRISPR Array Only").click(
-                        lambda: CRISPR_EXAMPLE, outputs=seq_input
-                    )
-                    gr.Button("Flanked CRISPR (recommended)").click(
+                    gr.Button("Flanked CRISPR").click(
                         lambda: FLANKED_CRISPR_EXAMPLE, outputs=seq_input
                     )
+                    gr.Button("E. coli K-12").click(
+                        lambda: ECOLI_CRISPR_EXAMPLE, outputs=seq_input
+                    )
+                with gr.Row():
+                    gr.Button("CRISPR Only").click(
+                        lambda: CRISPR_EXAMPLE, outputs=seq_input
+                    )
                     gr.Button("Non-CRISPR").click(
                         lambda: NON_CRISPR_EXAMPLE, outputs=seq_input
                     )
                 result_summary = gr.Markdown()
-                with gr.Accordion("Downloads", open=False, visible=False) as download_accordion:
+                with gr.Accordion("📥 Downloads", open=False, visible=False) as download_accordion:
                     gr.Markdown("**Plot exports:**")
                     with gr.Row():
                         pred_download_png = gr.File(label="PNG", interactive=False)
                         pred_download_pdf = gr.File(label="PDF", interactive=False)
                     gr.Markdown("**Data exports:**")
                     with gr.Row():
-                        pred_download_csv = gr.File(label="Predictions (CSV)", interactive=False)
-                        pred_download_summary = gr.File(label="Summary (TXT)", interactive=False)
+                        pred_download_csv = gr.File(label="CSV", interactive=False)
+                        pred_download_gff = gr.File(label="GFF3", interactive=False)
+                    with gr.Row():
+                        pred_download_summary = gr.File(label="Summary", interactive=False)
             with gr.Column(scale=2):
                 plot_output = gr.Plot(label="CRISPR Score Profile (Interactive)")
+                with gr.Accordion("🧬 Sequence Viewer", open=False, visible=False) as seq_viewer_accordion:
+                    seq_viewer_html = gr.HTML(label="Color-coded sequence")
                 regions_output = gr.JSON(label="Detected Regions", visible=False)
 
+        # Handle file upload - load content into textbox
+        def load_file_to_textbox(file_path):
+            if file_path:
+                return parse_fasta_file(file_path)
+            return gr.update()
+
+        file_upload.change(
+            load_file_to_textbox,
+            inputs=[file_upload],
+            outputs=[seq_input]
+        )
+
         def predict_and_show_downloads(*args):
             results = predict(*args)
-            # Return results plus visibility update for accordion
-            return results + (gr.update(visible=True),)
+            # results = (fig, summary, regions, png, pdf, csv, summary_txt, gff, seq_html)
+            # Return results plus visibility updates for accordions
+            return results + (gr.update(visible=True), gr.update(visible=True))
 
         predict_btn.click(
             predict_and_show_downloads,
             inputs=[seq_input, stride_input, threshold_input],
-            outputs=[plot_output, result_summary, regions_output, pred_download_png, pred_download_pdf, pred_download_csv, pred_download_summary, download_accordion]
+            outputs=[plot_output, result_summary, regions_output, pred_download_png, pred_download_pdf,
+                     pred_download_csv, pred_download_summary, pred_download_gff, seq_viewer_html,
+                     download_accordion, seq_viewer_accordion]
         )
 
     with gr.Tab("Embeddings"):