import gradio as gr
import sqlite3
import pandas as pd
import plotly.express as px
from utils import *
from huggingface_hub import hf_hub_download
db_path = hf_hub_download(
repo_id="hemantn/antibody-paired-sequences",
filename="antibody_data_year.db",
repo_type="dataset"
)
conn = sqlite3.connect(db_path, check_same_thread=False)
#conn = sqlite3.connect('/data/hn533621/OAS/Paired_Data_Analysis/antibody_data_year.db', check_same_thread=False)
all_df = pd.read_sql("SELECT * FROM antibody_data", conn)
# Compute min / max for each column
vh_min, vh_max = int(all_df["vh_length"].min()), int(all_df["vh_length"].max())
vl_min, vl_max = int(all_df["vl_length"].min()), int(all_df["vl_length"].max())
def apply_filters(dropdown_1, dropdown_2, dropdown_3, dropdown_4, slider_vh, slider_vl):
"""
Build a query to filter the data based on the user's input
"""
clauses = []
params = {}
if dropdown_1: clauses.append('v_call_heavy_first = :vh_germline'); params['vh_germline'] = dropdown_1
if dropdown_2: clauses.append('v_call_light_first = :vl_germline'); params['vl_germline'] = dropdown_2
if dropdown_3: clauses.append('BType = :btype'); params['btype'] = dropdown_3
if dropdown_4: clauses.append('Disease = :disease'); params['disease'] = dropdown_4
if slider_vh:
clauses.append("vh_length BETWEEN :vh_min AND :vh_max")
params["vh_min"] = 80
params["vh_max"] = slider_vh
if slider_vl:
clauses.append("vl_length BETWEEN :vl_min AND :vl_max")
params["vl_min"] = 80
params["vl_max"] = slider_vl
sql = "SELECT * FROM antibody_data"
if clauses:
sql += " WHERE " + " AND ".join(clauses)
df = pd.read_sql_query(sql, conn, params=params)
#print(df.columns)
# ---- Reorder and rename columns ----
# 1. Rename to your desired display names
rename_map = {
"BType": "BType",
"Disease": "Disease",
"v_call_heavy_first": "vcall_VH",
"v_call_light_first": "vcall_VL",
"vh_length": "VH_length",
"vl_length": "VL_length",
"sequence_alignment_aa_heavy": "VH",
"sequence_alignment_aa_light": "VL",
"Year": "Year",
}
df = df.rename(columns=rename_map)
# 2. Reorder the columns
desired_order = [
"BType",
"Disease",
"vcall_VH",
"vcall_VL",
"VH_length",
"VL_length",
"Year",
"VH",
"VL",
]
# Only keep columns that actually exist (avoids errors if some missing)
df = df[[c for c in desired_order if c in df.columns]]
total_rows = conn.execute("SELECT COUNT(*) FROM antibody_data").fetchone()[0]
#vcall_vh_bar = bar_vcall_vh(df, total_rows, dropdown_1)
year_bar = bar_year_count(df)
#vcall_vl_bar = bar_vcall_vl(df, total_rows, dropdown_2)
combined_vh_vl_bar = bar_vh_vl_combined(df, total_rows, dropdown_1, dropdown_2)
#disease_bar = bar_disease_count(df, total_rows, dropdown_4)
#btype_bar = bar_btype_count(df, total_rows, dropdown_3)
vh_fig, vl_fig = hist_vh_vl_separate(df)
fasta_file = make_fasta_file(df)
total_sequences = 2*len(df)
return df.head(5), combined_vh_vl_bar, year_bar, vh_fig, vl_fig, fasta_file, total_sequences
with gr.Blocks(theme=gr.themes.Ocean()) as demo:
gr.Markdown(
"""
🔎 Antibody Database Dashboard
Filter sequences, explore counts, and download custom FASTA files.
"""
)
with gr.Row():
with gr.Column(scale=1):
dropdown_1 = gr.Dropdown(choices = [""] + sorted(all_df['v_call_heavy_first'].unique()), \
value = None, label = 'VH germline')
dropdown_2 = gr.Dropdown(choices = [""] + sorted(all_df['v_call_light_first'].unique()), \
value = None, label = 'VL germline')
dropdown_3 = gr.Dropdown(choices = [""] + sorted(all_df['BType'].unique()), \
value = None, label = 'B-Type')
dropdown_4 = gr.Dropdown(choices = [""] + sorted(all_df['Disease'].unique()), \
value = None, label = 'Disase')
slider_vh = gr.Slider(value=vh_max, minimum=vh_min, maximum=vh_max, step=1, label = 'VH length')
slider_vl = gr.Slider(value=vl_max, minimum=vl_min, maximum=vl_max, step=1, label = 'VL length')
button = gr.Button('Apply Filters', variant='primary')
with gr.Column(scale=2):
with gr.Row():
with gr.Column(scale=1):
combined_vh_vl_bar = gr.Plot(label = 'VH and VL Germline')
with gr.Column(scale=1):
vh_fig = gr.Plot(label = 'VH Length Distribution')
with gr.Row():
with gr.Column(scale=1):
year_bar = gr.Plot(label = 'Year Wise Distribution')
with gr.Column(scale=1):
vl_fig = gr.Plot(label = 'VL Length Distribution')
with gr.Row():
#with gr.Column(scale=0.3):
#gr.Plot(label= 'Yearwise distribution of antibodies')
with gr.Column(scale=1):
with gr.Row():
with gr.Column(scale=2):
df_out = gr.Dataframe()
with gr.Column(scale=1):
fasta_file = gr.File(label= 'Download Antibody Data Fasta file', interactive=False)
with gr.Row():
total_sequences = gr.Textbox(label= 'No of Sequences in Fasta file', value=0, interactive=False)
slider_vh.input(update_vh, inputs=slider_vh, outputs=slider_vh, queue=False)
slider_vl.input(update_vl, inputs=slider_vl, outputs=slider_vl, queue=False)
inputs = [dropdown_1, dropdown_2, dropdown_3, dropdown_4, slider_vh, slider_vl]
#outputs = df,
#outputs = [plot_vh_germline, plot_vl_germline, plot_disease_count, plot_btype_count, \
# plot_year_data, dataframe, download_fasta]
button.click(
fn=apply_filters,
inputs = inputs,
outputs = [df_out, combined_vh_vl_bar, year_bar, vh_fig, vl_fig, fasta_file, total_sequences]
)
demo.launch()