revert gene name dropdown and add plotly plddt

app/main.py

@@ -109,8 +109,8 @@ def visualize_AF2(tf_pair, a):
     # segpair.choices = list(a.pairs_data.keys())
     fig1, ax1 = a.plot_plddt_gene1()
     fig2, ax2 = a.plot_plddt_gene2()
-    fig3, ax3 = a.protein1.plot_plddt()
-    fig4, ax4 = a.protein2.plot_plddt()
+    fig3, ax3 = a.protein1.plotly_plddt()
+    fig4, ax4 = a.protein2.plotly_plddt()
     fig5, ax5 = a.plot_score_heatmap()
     plt.tight_layout()
     new_dropdown = update_dropdown(list(a.pairs_data.keys()), "Segment pair")
@@ -126,22 +126,12 @@ def update_dropdown(x, label):
     return gr.Dropdown.update(choices=x, label=label)
 
 
-def filter_gene_records(cell, str):
-    if str == '':
-        return cell.gene_annot.groupby('gene_name')[['pred', 'obs', 'accessibility']].mean().reset_index().head(5), cell
-    df = cell.gene_annot.query(f"gene_name == '{str}'").groupby('gene_name')[['pred', 'obs', 'accessibility']].mean().reset_index().head(5)
-    return df, cell
-
 def load_and_plot_celltype(celltype_name, GET_CONFIG, cell):
     celltype_id = cell_type_name_to_id[celltype_name]
     cell = GETCellType(celltype_id, GET_CONFIG)
     cell.celltype_name = celltype_name
-    # gene_name.choices = sorted(gene_exp_table.gene_name.unique()
     gene_exp_fig = cell.plotly_gene_exp()
-    gene_exp_table = cell.gene_annot.groupby('gene_name')[['pred', 'obs', 'accessibility']].mean().reset_index().head(5)
-    new_gene_dropdown = update_dropdown(sorted(cell.gene_annot.gene_name.unique()), "Gene name")
-    return gene_exp_fig, gene_exp_table, new_gene_dropdown, new_gene_dropdown, cell
-    
+    return gene_exp_fig, cell
 
 
 def plot_gene_regions(cell, gene_name, plotly=True):
@@ -184,7 +174,6 @@ if __name__ == "__main__":
         seg_pairs = gr.State([""])
         af = gr.State(None)
         cell = gr.State(None)
-        gene_names = gr.State([""])
 
         gr.Markdown(
             """# 🌟 GET: A Foundation Model of Transcription Across Human Cell Types 🌟
@@ -224,18 +213,6 @@ This section enables you to select different cell types and generates a plot tha
                 )
                 celltype_btn = gr.Button(value="Load & plot gene expression")
                 gene_exp_plot = gr.Plot(label="Gene expression prediction vs observation")
-                with gr.Row() as row:
-                    gene_name = gr.Dropdown(value="BCL11A")
-                    # Button to trigger the filter action
-                    filter_btn = gr.Button("Filter table by gene name")
-                gene_exp_table = gr.Dataframe(
-                    datatype=["str", "number", "number", "number"],
-                    row_count=5,
-                    col_count=(4, "fixed"),
-                    label='Gene expression table',
-                    max_rows=5
-                )
-
 
             # Right column: Plot gene motifs
             with gr.Column():
@@ -246,8 +223,8 @@ This section enables you to select different cell types and generates a plot tha
 In this section, you can choose a specific gene and access visualizations of its cell-type specific regulatory regions and motifs that promote gene expression. When you hover over the highlighted regions (the top 10%), you'll be able to view information about the motifs present in those regions and their corresponding scores. This feature allows for a detailed exploration of the regulatory elements influencing the expression of the selected gene.
 """
                 )
-                gene_name_for_region = gr.Dropdown(
-                    label="Get important regions or motifs for gene:", value="BCL11A"
+                gene_name_for_region = gr.Textbox(
+                    label="Get important regions or motifs for gene:", value="SOX2"
                 )
                 with gr.Row() as row:
                     region_plot_btn = gr.Button(value="Regions")
@@ -355,12 +332,7 @@ You can download specific segment pair PDB files by clicking 'Get PDB.'
         celltype_btn.click(
             load_and_plot_celltype,
             inputs=[celltype_name, gr.State(GET_CONFIG), cell],
-            outputs=[gene_exp_plot, gene_exp_table, gene_name, gene_name_for_region, cell],
-        )
-        filter_btn.click(
-            filter_gene_records,
-            inputs=[cell, gene_name],
-            outputs=[gene_exp_table, cell],
+            outputs=[gene_exp_plot, cell],
         )
         region_plot_btn.click(
             plot_gene_regions,