update mono

software2023/mono/web_ui.py → software2023/mono/webui.py

 import gradio as gr
 import json
 
-    
+
 import sys
 import os
 from use_bio_html import run
@@ -12,7 +12,7 @@ HTML_BASE = """<div style="padding-left: 5px; padding-right: 0px;">
 
 </style>
 <div style="font-size:20px;display:flex;">
-	<strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;&nbsp;&nbsp;</strong>	
+	<strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Name&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</strong>	
 	<strong style="word-wrap:break-word;padding: 0px 50px 25px 60px;text-align: center;">Definition</strong>
 </div>
 <ol start="1" style="color:grey">
@@ -20,20 +20,18 @@ HTML_BASE = """<div style="padding-left: 5px; padding-right: 0px;">
 
 HTML_TEMPLATE = """
 <li style="font-size:20px;display:flex;" class="textspan svelte-ju12zg hl ">
-	<a href="http://parts.igem.org/Part:{name}" target="_blank"><u>{name}</u></a>	
+	<a href="http://parts.igem.org/Part:{name}" target="_blank" style="width:128px;"><u>{name}</u></a>	
 	<div style="word-wrap:break-word;padding: 0px 50px 25px 50px">{definition}</div>
 </li>
 """
 
 
-
-
 with open("target_words.txt") as f:
-    word_list=f.readlines()
+    word_list = f.readlines()
 
 
-with open("train.json") as f:
-    word_desriptions=json.load(f)
+with open("desc_pre.json") as f:
+    word_desriptions = json.load(f)
 
 
 def calcolor(p):
@@ -45,14 +43,18 @@ def calcolor(p):
 
 
 def predict(text):
-    res_index,res_score,res_name=run(text)
+    res_index, res_score, res_name = run(text)
 
     html_res = (
         HTML_BASE
         + "".join(
             [
                 HTML_TEMPLATE.format(
-                    color=calcolor(1-i/len(res_index)), definition=word_desriptions[res_index[i]]["definitions"], name=word_list[i]
+                    color=calcolor(1 - i / len(res_index)),
+                    definition=word_desriptions[word_list[res_index[i]].strip("\n")],
+                    name=word_list[
+                        res_index[i]
+                    ],  # .ljust(13,'\n').replace('\n','&nbsp;')
                 )
                 for i in range(len(res_index))
             ]
@@ -62,9 +64,11 @@ def predict(text):
     return html_res
 
 
-with gr.Blocks(title="Reverse Dictonary for Biobricks") as demo:
-    gr.Markdown("# Welcome to use Reverse Dictonary for Biobricks")
-    input = gr.Textbox(label="", placeholder="Please type what you want...")
+with gr.Blocks(title="Ask NOX") as demo:
+    gr.Markdown("# Welcome to use Ask NOX!")
+    input = gr.Textbox(
+        label="", placeholder="Please type the description of the biobricks you want..."
+    )
     greet_btn = gr.Button("Look up")
     output = gr.HTML()
     greet_btn.click(fn=predict, inputs=input, outputs=output, api_name="predict")