fix file upload handle, update readme

README.md

@@ -1,8 +1,8 @@
 ---
 title: ARGnet UI
-emoji: 📈
-colorFrom: gray
-colorTo: gray
+emoji: 🧬
+colorFrom: indigo
+colorTo: pink
 sdk: gradio
 sdk_version: 4.14.0
 app_file: app.py

app.py

@@ -7,6 +7,7 @@ import gradio as gr
 from helper import *
 import scripts.script as script
 
+
 def process_data(input_type, input_text, input_file):
     print(input_type)
     if input_type == "Text":
@@ -26,6 +27,7 @@ def process_data(input_type, input_text, input_file):
     else:
         if input_file:
             sequence = []
+            input_text = open(input_file.name, "r").read()
             with open(input_file.name, "r") as f:
                 for line in f:
                     if line.startswith(">"):
@@ -70,7 +72,7 @@ with gr.Blocks() as whole_block:
                 """
                 )
                 input_textbox = gr.Textbox(label="Sequence")
-                input_textbox_2 = gr.Textbox(label="Sequence",visible=False)
+                input_textbox_2 = gr.Textbox(label="Sequence", visible=False)
                 gr.Examples(
                     examples=[
                         ["Amino Acid Long Sequence (>51aa)"],
@@ -97,7 +99,14 @@ with gr.Blocks() as whole_block:
             ## Output
             """
             )
-            table = gr.Dataframe(headers=["Test ID", "ARG Prediction", "Resistance Category", "Probability"])
+            table = gr.Dataframe(
+                headers=[
+                    "Test ID",
+                    "ARG Prediction",
+                    "Resistance Category",
+                    "Probability",
+                ]
+            )
             pie_chart = gr.Plot(container=True)
 
     text_tab.select(lambda: "Text", inputs=None, outputs=tab_selected)

scripts/script.py

@@ -5,50 +5,85 @@ import plotly.graph_objects as go
 
 import os
 
+
 def plot_pie_chart(df):
     ARG_prediction_counts = dict(df["ARG_prediction"].value_counts())
-    ARG_prediction_df = pd.DataFrame.from_dict(ARG_prediction_counts, orient='index', columns=['count'])
+    ARG_prediction_df = pd.DataFrame.from_dict(
+        ARG_prediction_counts, orient="index", columns=["count"]
+    )
     resistance_category_counts = dict(df["resistance_category"].value_counts())
-    resistance_category_df = pd.DataFrame.from_dict(resistance_category_counts, orient='index', columns=['count'])
+    resistance_category_df = pd.DataFrame.from_dict(
+        resistance_category_counts, orient="index", columns=["count"]
+    )
 
     number_of_catgeory = len(df["resistance_category"].value_counts())
 
     colors = [
-    '#f9b4ab',
-    '#fdebd3',
-    '#264e70',
-    '#679186',
-    '#bbd4ce',
+        "#f9b4ab",
+        "#fdebd3",
+        "#264e70",
+        "#679186",
+        "#bbd4ce",
     ]
     full_colors = []
-    for i in range(math.ceil(number_of_catgeory/5)):
+    for i in range(math.ceil(number_of_catgeory / 5)):
         full_colors += colors
 
     # colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen']
 
-    fig = sp.make_subplots(rows=1, cols=2, subplot_titles=("ARG/non-ARG", "Resistance category"), specs=[[{'type': 'domain'}, {'type': 'domain'}]])
-
-    fig.add_trace(go.Pie(labels=ARG_prediction_df.index, values=ARG_prediction_df['count'], legendgroup = '1', title="ARG/non-ARG"), row=1, col=1)
-    fig.add_trace(go.Pie(labels=resistance_category_df.index, values=resistance_category_df['count'], legendgroup = '2', title="Resistance category"), row=1, col=2)
+    fig = sp.make_subplots(
+        rows=1,
+        cols=2,
+        subplot_titles=("ARG/non-ARG", "Resistance category"),
+        specs=[[{"type": "domain"}, {"type": "domain"}]],
+    )
+
+    fig.add_trace(
+        go.Pie(
+            labels=ARG_prediction_df.index,
+            values=ARG_prediction_df["count"],
+            legendgroup="1",
+            title="ARG/non-ARG",
+        ),
+        row=1,
+        col=1,
+    )
+    fig.add_trace(
+        go.Pie(
+            labels=resistance_category_df.index,
+            values=resistance_category_df["count"],
+            legendgroup="2",
+            title="Resistance category",
+        ),
+        row=1,
+        col=2,
+    )
 
     fig.update_layout(showlegend=False, margin=dict(l=200, r=200, t=100, b=100))
-    fig.update_traces(textposition='inside',hoverinfo='label+percent', textinfo='label', marker=dict(colors=colors, line=dict(color='#38496e', width=1)))
-    
+    fig.update_traces(
+        textposition="inside",
+        hoverinfo="label+percent",
+        textinfo="label",
+        marker=dict(colors=full_colors, line=dict(color="#38496e", width=1)),
+    )
+
     return fig
 
+
 def view_stat(output_name):
-    df = pd.read_csv(f"results/{output_name}", delimiter='\t')
+    df = pd.read_csv(f"results/{output_name}", delimiter="\t")
     # change df header
     new_headers = ["Test ID", "ARG Prediction", "Resistance Category", "Probability"]
     fig = plot_pie_chart(df)
-    
+
     # delete the output file
     if os.path.exists(f"results/{output_name}"):
         os.remove(f"results/{output_name}")
 
     return [df.rename(columns=dict(zip(df.columns, new_headers))), fig]
 
-def run_argnet(input, output_name,sequence_type,sequence_length_type):
+
+def run_argnet(input, output_name, sequence_type, sequence_length_type):
     with open("input.txt", "w") as f:
         f.write(input)
 
@@ -58,16 +93,20 @@ def run_argnet(input, output_name,sequence_type,sequence_length_type):
 
     if sequence_type == "aa" and sequence_length_type == "s":
         from . import argnet_ssaa_chunk as ssaa
+
         ssaa.argnet_ssaa("input.txt", output_name)
     elif sequence_type == "nt" and sequence_length_type == "s":
-        from . import  argnet_ssnt_new_chunk as ssnt
+        from . import argnet_ssnt_new_chunk as ssnt
+
         ssnt.argnet_ssnt("input.txt", output_name)
     elif sequence_type == "aa" and sequence_length_type == "l":
         from . import argnet_lsaa_speed_sgpu as lsaa
+
         lsaa.argnet_lsaa("input.txt", output_name)
     elif sequence_type == "nt" and sequence_length_type == "l":
         from . import argnet_lsnt as lsnt
-        lsnt.argnet_lsnt("input.txt", output_name)
-
 
+        lsnt.argnet_lsnt("input.txt", output_name)
 
+    if os.path.exists("input.txt"):
+        os.remove("input.txt")