import gradio as gr
import random
import time
from utils import load_model_util, load_corpus_util, build_index_util, search_util
model = None
corpus = None
faiss_index = None
data_dir = '/share/chaofan/code/bge_demo/data'
index_dir = '/share/chaofan/code/bge_demo/emb'
lang = 'en'
avaliable_queries = []
# Simulate loading a model
def load_model(model_name):
global model
"""
Function to load a model and provide status updates with a custom loading type.
"""
# 定义 HTML 样式
loading_style = "⏳" # 自定义加载中图标
success_style = "✔" # 成功图标
error_style = "✗" # 错误图标
try:
yield f"{loading_style} Loading model {model_name}..."
model = load_model_util(model, model_name)
yield f"{success_style} Model {model_name} has been successfully loaded!"
except Exception as e:
yield f"{error_style} Failed to load model {model_name}: {str(e)}"
# Simulate selecting a language
def choose_language(language):
global lang
lang = language
language_full = {
"zh": "Chinese",
"en": "English",
"ar": "Arabic",
"bn": "Bengali",
"es": "Spanish",
"fa": "Persian",
"fi": "Finnish",
"fr": "French",
"hi": "Hindi",
"id": "Indonesian",
"ja": "Japanese",
"ko": "Korean",
"ru": "Russian",
"sw": "Swahili",
"te": "Telugu",
"th": "Thai",
"de": "German",
"yo": "Yoruba"
}.get(language, language)
return f"⚡ Current Language: {language_full} ({language})"
# Simulate loading a corpus
def load_corpus(language):
global data_dir, corpus, avaliable_queries
# Initial loading status
corpus_name = f"miracl.{language}"
yield f"""⏳ Loading corpus {corpus_name} ..."""
# Simulate loading process
print(f"Loading corpus: {corpus_name}")
corpus, avaliable_queries = load_corpus_util(data_dir, language)
yield f"""✔ Corpus {corpus_name} has been successfully loaded!"""
def update_query_input(value):
global avaliable_queries
return gr.Dropdown(
label="Search Query",
choices=avaliable_queries, # 预定义的选项
value="", # 默认值
allow_custom_value=True, # 允许用户输入自定义值
# placeholder="Select or enter search keywords..."
)
# Simulate building an index
def build_index(language):
# Simulate progressive building process
global model, corpus, index_dir, faiss_index, lang
# 定义 HTML 样式
loading_style = "⏳" # 自定义加载中图标
success_style = "✔" # 成功图标
error_style = "✗" # 错误图标
if model is None:
yield f"{error_style} You need to load the model before building index!"
elif corpus is None:
yield f"{error_style} You need to load the corpus before building index!"
else:
try:
yield f"{loading_style} Building index..."
faiss_index = build_index_util(index_dir, lang, model, corpus)
yield f"{success_style} Index building complete!"
except Exception as e:
yield f"{error_style} Failed to build index: {str(e)}"
# Simulate retrieving results
def retrieve_results(query, language, top_k):
global model, corpus, faiss_index
yield f"""
⏳ Start to search ...
"""
error_style = "✗" # 错误图标
try:
scores, data = search_util(model, query, corpus, faiss_index, top_k)
# print(scores)
# print(data)
# Generate random results
results = []
for score, d in zip(scores, data):
doc_id = d['id']
title = d['title']
content = d['text']
results.append((float(score), doc_id, title, content))
# # Sort by score
# results.sort(reverse=True)
# Generate HTML display
html_result = f"""
Search Results: {top_k} items
Query: "{query}" | Language: {language}
"""
for i, (score, doc_id, title, content) in enumerate(results):
# Set gradient color
color = f"hsl({min(120, int(score*120))}, 80%, 40%)"
html_result += f"""
""")
with gr.Row(elem_classes="row"): # Use Row to place components in the same row
# Step 1: Load model
with gr.Group(elem_classes="step-card"):
gr.HTML('