ethanLeaderboard

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smile commited on 17 days ago

Commit

7e055ad

1 Parent(s): e7d518b

test build

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Files changed (13) hide show

app.py +57 -11
app/backend/constant.py +3 -2
app/backend/data_engine.py +63 -12
app/static/css/style.css +516 -0
app/static/js/main.js +258 -0
app/templates/index.html +175 -0
app/ui/component/subtabs_component.py +6 -6
mock_data/datasets.json +0 -17
mock_data/models.json +0 -113
mock_data/results.json +0 -0
requirements.txt +4 -0
utils/cache_decorator.py +21 -0
utils/http_utils.py +7 -0

app.py CHANGED Viewed

@@ -1,14 +1,60 @@
-from app.ui.home import init_home
-from app.ui.about import init_about
-import gradio as gr
-home = init_home()
-about = init_about()
-demo = gr.TabbedInterface(
-    [home, about],
-    ["Home", "About"],
-    css="footer {display: none} .sort-button.svelte-1bvc1p0.svelte-1bvc1p0.svelte-1bvc1p0 {display: none;}"
-)
-demo.launch()

+from fastapi import FastAPI, Request
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import HTMLResponse
+import pandas as pd
+from pathlib import Path
+from app.backend.constant import Navigation, ModelProvider, EvaluationMetric, EmbdDtype, EmbdDim, Similarity
+from app.backend.data_engine import DataEngine
+app = FastAPI()
+# Mount static files
+app.mount("/static", StaticFiles(directory="app/static"), name="static")
+# Templates
+templates = Jinja2Templates(directory="app/templates")
+# Initialize data engine
+data_engine = DataEngine()
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request, tab: str = "text"):
+    # Get initial data based on tab
+    if tab == "multimodal":
+        data = data_engine.get_filtered_data(navigation="multimodal")
+    else:
+        data = data_engine.get_data()
+    # Convert data to list for template rendering
+    data_list = data.values.tolist()
+    columns = data.columns.tolist()
+    return templates.TemplateResponse("index.html", {
+        "request": request,
+        "active_tab": tab,
+        "data": data_list,
+        "columns": columns,
+        "navigations": [e.value for e in Navigation],
+        "embd_types": [e.value for e in EmbdDtype],
+        "embd_dims": [e.value for e in EmbdDim],
+        "similarities": [e.value for e in Similarity]
+    })
+@app.post("/api/filter")
+async def filter_data(filters: dict):
+    # Apply filters to data
+    data = data_engine.get_filtered_data(
+        navigation=filters.get('navigation'),
+        embd_type=filters.get('embd_type'),
+        embd_dims=filters.get('embd_dims'),
+        similarity=filters.get('similarity')
+    )
+    # Convert to list for JSON response
+    return data.values.tolist()
+import uvicorn
+uvicorn.run(app, host="0.0.0.0", port=7890)

app/backend/constant.py CHANGED Viewed

@@ -68,7 +68,8 @@ class Similarity(Enum):
 LEADERBOARD_MAP = {
-    "text": [
         "law",
         "long-context",
         "finance",
@@ -78,7 +79,7 @@ LEADERBOARD_MAP = {
         "code",
         "healthcare"
     ],
-    "multimodal": [
         "text-to-photo",
         "document-screenshot",
         "figures-and-tables",

 LEADERBOARD_MAP = {
+    "Text": [
+        "text",
         "law",
         "long-context",
         "finance",
         "code",
         "healthcare"
     ],
+    "Multimodal": [
         "text-to-photo",
         "document-screenshot",
         "figures-and-tables",

app/backend/data_engine.py CHANGED Viewed

@@ -7,7 +7,8 @@ from typing import List
 import pandas as pd
 from app.backend.constant import ModelProvider
-from utils.cache_decorator import cache_df_with_custom_key
 COLUMNS = ['model_name', 'group_name', 'leaderboard', 'dataset_name',
            'embd_dtype', 'embd_dim', 'num_params', 'max_tokens', 'similarity',
@@ -29,6 +30,11 @@ COLUMNS_TYPES = ["markdown", "str", 'str', 'str',
                  'number', 'number', 'number',
                  'number']
 class DataEngine:
@@ -36,35 +42,78 @@ class DataEngine:
         self.df = self.init_dataframe()
     @property
     def models(self):
         """
         Get models data
         """
-        with open('./mock_data/models.json', 'r') as f:
-            return json.load(f)
     @property
     def datasets(self):
         """
         Get tasks data
         """
-        with open('./mock_data/datasets.json', 'r') as f:
-            return json.load(f)
     @property
     def results(self):
         """
         Get results data
         """
-        with open('./mock_data/results.json', 'r') as f:
-            return json.load(f)
     def init_dataframe(self):
         """
         Initialize DataFrame
         """
-        d = {"hello": [123], "world": [456]}
-        return pd.DataFrame(d)
     def _check_providers(self, organization: str, providers: List):
         if not providers:
@@ -89,10 +138,10 @@ class DataEngine:
         df_datasets_list = []
         for item in self.datasets:
-            dataset_names = item["dataset_names"]
             df_dataset_row = pd.DataFrame(
                 {
-                    "group_name": [item["group_name"] for _ in range(len(dataset_names))],
                     "dataset_name": dataset_names,
                     "leaderboard": [item["leaderboard"] for _ in range(len(dataset_names))]
                 }
@@ -104,12 +153,14 @@ class DataEngine:
         df_model = pd.DataFrame(models_list)
-        df = pd.merge(df_result, df_model, on="model_name", how="inner")
         df = pd.merge(df, df_dataset, on="dataset_name", how="inner")
         df["model_name"] = df.apply(lambda
                                         x: f"""<a target=\"_blank\" style=\"text-decoration: underline\" href=\"{x["reference"]}\">{x["model_name"]}</a>""",
                                     axis=1)
         return df[COLUMNS]
     def filter_df(self, df_result: pd.DataFrame, embd_dtype: str, embd_dims: List, similarity: str, max_tokens: int):

 import pandas as pd
 from app.backend.constant import ModelProvider
+from utils.cache_decorator import cache_df_with_custom_key, cache_dict_with_custom_key
+from utils.http_utils import get
 COLUMNS = ['model_name', 'group_name', 'leaderboard', 'dataset_name',
            'embd_dtype', 'embd_dim', 'num_params', 'max_tokens', 'similarity',
                  'number', 'number', 'number',
                  'number']
+GIT_URL = "https://raw.githubusercontent.com/embedding-benchmark/ebr/refs/heads/main/results/"
+DATASET_URL = f"{GIT_URL}datasets.json"
+MODEL_URL = f"{GIT_URL}models.json"
+RESULT_URL = f"{GIT_URL}results.json"
 class DataEngine:
         self.df = self.init_dataframe()
     @property
+    @cache_dict_with_custom_key("models")
     def models(self):
         """
         Get models data
         """
+        res = get(MODEL_URL)
+        if res.status_code == 200:
+            return res.json()
+        return {}
     @property
+    @cache_dict_with_custom_key("datasets")
     def datasets(self):
         """
         Get tasks data
         """
+        res = get(DATASET_URL)
+        if res.status_code == 200:
+            return res.json()
+        return {}
     @property
+    @cache_dict_with_custom_key("results")
     def results(self):
         """
         Get results data
         """
+        res = get(RESULT_URL)
+        if res.status_code == 200:
+            return res.json()
+        return {}
     def init_dataframe(self):
         """
         Initialize DataFrame
         """
+        return self.jsons_to_df()
+    def get_data(self):
+        """
+        Get the full dataset
+        """
+        df = self.df.copy()
+        # 移除指定列
+        columns_to_remove = ['group_name', 'leaderboard', 'dataset_name']
+        df = df.drop(columns=columns_to_remove)
+        # 按 NDCG@10 降序排序
+        return df.sort_values(by='ndcg_at_10', ascending=False)
+    def get_filtered_data(self, navigation=None, embd_type=None, embd_dims=None, similarity=None):
+        """
+        Get filtered dataset based on criteria
+        """
+        filtered_df = self.df.copy()
+        if navigation and navigation != "all":
+            filtered_df = filtered_df[filtered_df['leaderboard'] == navigation]
+        if embd_type and embd_type != "all":
+            filtered_df = filtered_df[filtered_df['embd_dtype'] == embd_type]
+        if similarity and similarity != "all":
+            filtered_df = filtered_df[filtered_df['similarity'] == similarity]
+        if embd_dims and isinstance(embd_dims, list) and len(embd_dims) > 0:
+            filtered_df = filtered_df[filtered_df['embd_dim'].isin(embd_dims)]
+        # 移除指定列
+        columns_to_remove = ['group_name', 'leaderboard', 'dataset_name']
+        filtered_df = filtered_df.drop(columns=columns_to_remove)
+        # 按 NDCG@10 降序排序
+        return filtered_df.sort_values(by='ndcg_at_10', ascending=False)
     def _check_providers(self, organization: str, providers: List):
         if not providers:
         df_datasets_list = []
         for item in self.datasets:
+            dataset_names = item["datasets"]
             df_dataset_row = pd.DataFrame(
                 {
+                    "group_name": [item["name"] for _ in range(len(dataset_names))],
                     "dataset_name": dataset_names,
                     "leaderboard": [item["leaderboard"] for _ in range(len(dataset_names))]
                 }
         df_model = pd.DataFrame(models_list)
+        df = pd.merge(df_result, df_model, on=["model_name", "embd_dim", "embd_dtype"], how="inner")
         df = pd.merge(df, df_dataset, on="dataset_name", how="inner")
         df["model_name"] = df.apply(lambda
                                         x: f"""<a target=\"_blank\" style=\"text-decoration: underline\" href=\"{x["reference"]}\">{x["model_name"]}</a>""",
                                     axis=1)
+        if df.empty:
+            return pd.DataFrame(columns=COLUMNS)
         return df[COLUMNS]
     def filter_df(self, df_result: pd.DataFrame, embd_dtype: str, embd_dims: List, similarity: str, max_tokens: int):

app/static/css/style.css ADDED Viewed

	@@ -0,0 +1,516 @@

+/* Global styles */
+body {
+    margin: 0;
+    padding: 0;
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+    background-color: #ffffff;
+    color: #1C1F23;
+}
+/* App Container */
+.app-container {
+    display: flex;
+    min-height: 100vh;
+    background: linear-gradient(135deg, #ffffff 0%, #f8f9fa 100%);
+}
+/* Sidebar styles */
+.sidebar {
+    width: 280px;
+    background: rgba(255, 255, 255, 0.95);
+    backdrop-filter: blur(10px);
+    border-right: 1px solid rgba(0, 0, 0, 0.1);
+    box-shadow: 2px 0 20px rgba(0, 0, 0, 0.05);
+    padding: 24px 0;
+    flex-shrink: 0;
+}
+.sidebar-header {
+    padding: 0 24px 20px;
+    border-bottom: 1px solid rgba(0, 0, 0, 0.08);
+}
+.sidebar-header h2 {
+    margin: 0;
+    font-size: 20px;
+    font-weight: 600;
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    letter-spacing: 0.5px;
+}
+.sidebar-nav {
+    padding: 20px 0;
+}
+.nav-item {
+    margin: 8px 16px;
+    border-radius: 12px;
+    overflow: hidden;
+    transition: transform 0.2s ease;
+}
+.nav-item:hover {
+    transform: translateX(4px);
+}
+.nav-link {
+    display: flex;
+    align-items: center;
+    padding: 14px 16px;
+    color: #1C1F23;
+    text-decoration: none;
+    border-radius: 12px;
+    transition: all 0.3s ease;
+    font-weight: 500;
+}
+.nav-link:hover {
+    background: linear-gradient(120deg, rgba(33, 150, 243, 0.1), rgba(0, 188, 212, 0.1));
+}
+.nav-item.active .nav-link {
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    color: #fff;
+    box-shadow: 0 4px 15px rgba(33, 150, 243, 0.3);
+}
+.nav-icon {
+    margin-right: 12px;
+    font-size: 18px;
+}
+/* Main Content Wrapper */
+.main-wrapper {
+    flex: 1;
+    padding: 24px;
+    max-width: calc(100% - 280px);
+}
+/* Header styles */
+.header {
+    background: rgba(255, 255, 255, 0.95);
+    padding: 24px;
+    border-radius: 16px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.08);
+    margin-bottom: 24px;
+    backdrop-filter: blur(10px);
+    border: 1px solid rgba(255, 255, 255, 0.4);
+}
+.header h1 {
+    margin: 0;
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-size: 28px;
+    text-align: center;
+    letter-spacing: 0.5px;
+}
+/* Filter section styles */
+.filter-section {
+    background-color: #fff;
+    padding: 20px;
+    border-radius: 8px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    margin-bottom: 20px;
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+    gap: 20px;
+}
+.filter-group {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+.filter-group label {
+    font-weight: 600;
+    color: #1C1F23;
+    margin-bottom: 4px;
+}
+.semi-select {
+    width: 100%;
+    padding: 8px 12px;
+    border: 1px solid #e0e0e0;
+    border-radius: 4px;
+    background-color: #fff;
+    font-size: 14px;
+    transition: border-color 0.2s;
+}
+.semi-select:hover {
+    border-color: #0077FF;
+}
+.semi-select:focus {
+    border-color: #0077FF;
+    outline: none;
+    box-shadow: 0 0 0 2px rgba(0,119,255,0.2);
+}
+.checkbox-group {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px;
+}
+.semi-checkbox {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    cursor: pointer;
+}
+.semi-checkbox input[type="checkbox"] {
+    width: 16px;
+    height: 16px;
+    margin: 0;
+}
+.semi-checkbox span {
+    font-size: 14px;
+}
+/* Table styles */
+.table-container {
+    background: rgba(255, 255, 255, 0.95);
+    padding: 24px;
+    border-radius: 16px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.08);
+    overflow-x: auto;
+    backdrop-filter: blur(10px);
+    border: 1px solid rgba(255, 255, 255, 0.4);
+}
+.semi-table {
+    width: 100%;
+    border-collapse: separate;
+    border-spacing: 0;
+    font-size: 14px;
+}
+/* Column Header Styles */
+.column-header {
+    position: relative;
+}
+.header-content {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 16px;
+    background: linear-gradient(120deg, rgba(33, 150, 243, 0.05), rgba(0, 188, 212, 0.05));
+    font-weight: 600;
+    color: #1C1F23;
+    white-space: nowrap;
+    border-bottom: 2px solid transparent;
+    transition: all 0.3s ease;
+}
+.header-content:hover {
+    background: linear-gradient(120deg, rgba(33, 150, 243, 0.1), rgba(0, 188, 212, 0.1));
+    border-bottom: 2px solid #2196F3;
+}
+.header-actions {
+    display: flex;
+    gap: 8px;
+}
+.sort-btn,
+.filter-btn {
+    background: none;
+    border: none;
+    padding: 6px;
+    cursor: pointer;
+    color: #666;
+    border-radius: 8px;
+    transition: all 0.3s ease;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+.sort-btn:hover,
+.filter-btn:hover {
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    color: #fff;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 12px rgba(33, 150, 243, 0.2);
+}
+/* Filter Dropdown Styles */
+.filter-dropdown {
+    position: absolute;
+    top: 100%;
+    left: 0;
+    right: 0;
+    background: rgba(255, 255, 255, 0.98);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: 12px;
+    box-shadow: 0 12px 32px rgba(0, 0, 0, 0.12);
+    z-index: 1000;
+    min-width: 240px;
+    backdrop-filter: blur(10px);
+    transform-origin: top;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+.filter-content {
+    padding: 16px;
+}
+.filter-search {
+    width: 90%;
+    padding: 12px;
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: 8px;
+    margin-bottom: 12px;
+    font-size: 14px;
+    background: rgba(255, 255, 255, 0.9);
+    transition: all 0.3s ease;
+}
+.filter-search:focus {
+    outline: none;
+    border-color: #2196F3;
+    box-shadow: 0 0 0 3px rgba(33, 150, 243, 0.2);
+}
+.filter-options {
+    max-height: 240px;
+    overflow-y: auto;
+    margin-bottom: 12px;
+    padding: 4px;
+}
+.filter-options::-webkit-scrollbar {
+    width: 8px;
+}
+.filter-options::-webkit-scrollbar-track {
+    background: rgba(0, 0, 0, 0.05);
+    border-radius: 4px;
+}
+.filter-options::-webkit-scrollbar-thumb {
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    border-radius: 4px;
+}
+.filter-option {
+    display: flex;
+    align-items: center;
+    padding: 10px 12px;
+    cursor: pointer;
+    border-radius: 8px;
+    transition: all 0.3s ease;
+}
+.filter-option:hover {
+    background: linear-gradient(120deg, rgba(33, 150, 243, 0.1), rgba(0, 188, 212, 0.1));
+}
+.filter-option input[type="checkbox"] {
+    margin-right: 12px;
+    width: 18px;
+    height: 18px;
+    border-radius: 4px;
+    border: 2px solid #2196F3;
+    transition: all 0.3s ease;
+    position: relative;
+    cursor: pointer;
+}
+.filter-option input[type="checkbox"]:checked {
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    border-color: transparent;
+}
+.filter-actions {
+    display: flex;
+    justify-content: flex-end;
+    gap: 12px;
+    padding-top: 12px;
+    border-top: 1px solid rgba(0, 0, 0, 0.08);
+}
+.filter-actions button {
+    padding: 10px 16px;
+    border: none;
+    border-radius: 8px;
+    cursor: pointer;
+    font-size: 14px;
+    font-weight: 500;
+    transition: all 0.3s ease;
+}
+.filter-actions button:first-child {
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    color: #fff;
+}
+.filter-actions button:first-child:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 4px 12px rgba(33, 150, 243, 0.3);
+}
+.filter-actions button:last-child {
+    background: rgba(0, 0, 0, 0.05);
+    color: #1C1F23;
+}
+.filter-actions button:last-child:hover {
+    background: rgba(0, 0, 0, 0.1);
+}
+/* Table Body Styles */
+.semi-table td {
+    padding: 16px;
+    border-bottom: 1px solid rgba(0, 0, 0, 0.08);
+    transition: all 0.3s ease;
+}
+.semi-table tbody tr {
+    transition: all 0.3s ease;
+}
+.semi-table tbody tr:hover {
+    background: linear-gradient(120deg, rgba(33, 150, 243, 0.05), rgba(0, 188, 212, 0.05));
+    transform: translateY(-1px);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
+}
+/* Links in table */
+.semi-table a {
+    color: #2196F3;
+    text-decoration: none;
+    transition: all 0.3s ease;
+    position: relative;
+}
+.semi-table a:hover {
+    color: #00BCD4;
+}
+.semi-table a::after {
+    content: '';
+    position: absolute;
+    width: 100%;
+    height: 2px;
+    bottom: -2px;
+    left: 0;
+    background: linear-gradient(120deg, #2196F3, #00BCD4);
+    transform: scaleX(0);
+    transition: transform 0.3s ease;
+}
+.semi-table a:hover::after {
+    transform: scaleX(1);
+}
+/* Responsive design */
+@media (max-width: 1024px) {
+    .sidebar {
+        width: 240px;
+    }
+    .main-wrapper {
+        max-width: calc(100% - 240px);
+        padding: 16px;
+    }
+}
+@media (max-width: 768px) {
+    .app-container {
+        flex-direction: column;
+    }
+    .sidebar {
+        width: 100%;
+        border-right: none;
+        border-bottom: 1px solid rgba(0, 0, 0, 0.1);
+        padding: 16px 0;
+    }
+    .main-wrapper {
+        max-width: 100%;
+        padding: 16px;
+    }
+    .header {
+        padding: 16px;
+        margin-bottom: 16px;
+    }
+    .header h1 {
+        font-size: 24px;
+    }
+    .table-container {
+        padding: 16px;
+        border-radius: 12px;
+    }
+    .header-content {
+        padding: 12px;
+    }
+    .semi-table td,
+    .semi-table th {
+        padding: 12px;
+        font-size: 13px;
+    }
+    .filter-dropdown {
+        position: fixed;
+        top: 50%;
+        left: 50%;
+        transform: translate(-50%, -50%);
+        width: 90%;
+        max-width: 320px;
+        margin: 0 auto;
+    }
+}
+/* Dark mode support */
+@media (prefers-color-scheme: dark) {
+    body {
+        background-color: #121212;
+        color: #ffffff;
+    }
+    .app-container {
+        background: linear-gradient(135deg, #121212 0%, #1a1a1a 100%);
+    }
+    .sidebar,
+    .header,
+    .table-container {
+        background: rgba(255, 255, 255, 0.05);
+        border-color: rgba(255, 255, 255, 0.1);
+    }
+    .semi-table td {
+        border-color: rgba(255, 255, 255, 0.1);
+    }
+    .filter-dropdown {
+        background: rgba(18, 18, 18, 0.98);
+    }
+    .filter-search {
+        background: rgba(255, 255, 255, 0.05);
+        border-color: rgba(255, 255, 255, 0.1);
+        color: #ffffff;
+    }
+    .filter-option:hover {
+        background: rgba(255, 255, 255, 0.05);
+    }
+}

app/static/js/main.js ADDED Viewed

	@@ -0,0 +1,258 @@

+// Global variables for sorting and filtering
+let currentSortColumn = -1;
+let isAscending = true;
+let columnFilters = {};
+let columnValues = {};
+// Function to update the table based on filters
+async function updateTable() {
+    const navigation = document.getElementById('navigation').value;
+    const embdType = document.getElementById('embdType').value;
+    const similarity = document.getElementById('similarity').value;
+    // Get all checked embedding dimensions
+    const embdDims = Array.from(document.querySelectorAll('input[name="embdDim"]:checked'))
+        .map(checkbox => checkbox.value);
+    // Prepare filter data
+    const filters = {
+        navigation: navigation,
+        embd_type: embdType,
+        embd_dims: embdDims,
+        similarity: similarity
+    };
+    try {
+        const response = await fetch('/api/filter', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify(filters)
+        });
+        if (!response.ok) {
+            throw new Error(`HTTP error! status: ${response.status}`);
+        }
+        const data = await response.json();
+        updateTableContent(data);
+    } catch (error) {
+        console.error('Error updating table:', error);
+        alert('Failed to update table. Please try again.');
+    }
+}
+// Function to update table content
+function updateTableContent(data) {
+    const tbody = document.querySelector('#leaderboardTable tbody');
+    tbody.innerHTML = '';
+    data.forEach(row => {
+        const tr = document.createElement('tr');
+        row.forEach(cell => {
+            const td = document.createElement('td');
+            td.innerHTML = cell; // Use innerHTML instead of textContent to handle HTML content
+            tr.appendChild(td);
+        });
+        tbody.appendChild(tr);
+    });
+}
+// Initialize when document is ready
+document.addEventListener('DOMContentLoaded', () => {
+    initializeColumnValues();
+    document.addEventListener('click', handleClickOutside);
+});
+// Initialize unique values for each column
+function initializeColumnValues() {
+    const table = document.getElementById('leaderboardTable');
+    const headers = Array.from(table.querySelectorAll('th'));
+    const rows = Array.from(table.querySelectorAll('tbody tr'));
+    headers.forEach((header, columnIndex) => {
+        const columnName = header.querySelector('span').textContent;
+        const values = new Set();
+        rows.forEach(row => {
+            const cell = row.cells[columnIndex];
+            const value = cell.textContent.trim();
+            if (value) values.add(value);
+        });
+        columnValues[columnName] = Array.from(values).sort();
+    });
+}
+// Handle clicking outside filter dropdowns
+function handleClickOutside(event) {
+    const dropdowns = document.querySelectorAll('.filter-dropdown');
+    dropdowns.forEach(dropdown => {
+        const filterBtn = dropdown.parentElement.querySelector('.filter-btn');
+        if (!dropdown.contains(event.target) && !filterBtn.contains(event.target)) {
+            dropdown.style.display = 'none';
+        }
+    });
+}
+// Toggle filter dropdown
+function toggleFilter(event, columnName) {
+    event.stopPropagation();
+    const dropdown = document.getElementById(`filter-${columnName}`);
+    const options = document.getElementById(`options-${columnName}`);
+    // Close other dropdowns
+    document.querySelectorAll('.filter-dropdown').forEach(d => {
+        if (d !== dropdown) d.style.display = 'none';
+    });
+    if (dropdown.style.display === 'none') {
+        dropdown.style.display = 'block';
+        if (!options.children.length) {
+            populateFilterOptions(columnName);
+        }
+    } else {
+        dropdown.style.display = 'none';
+    }
+}
+// Populate filter options
+function populateFilterOptions(columnName) {
+    const options = document.getElementById(`options-${columnName}`);
+    options.innerHTML = '';
+    columnValues[columnName].forEach(value => {
+        const option = document.createElement('div');
+        option.className = 'filter-option';
+        const checkbox = document.createElement('input');
+        checkbox.type = 'checkbox';
+        checkbox.value = value;
+        checkbox.checked = columnFilters[columnName]?.includes(value) || false;
+        const label = document.createElement('span');
+        label.textContent = value;
+        option.appendChild(checkbox);
+        option.appendChild(label);
+        options.appendChild(option);
+    });
+}
+// Filter options based on search input
+function filterOptions(columnName) {
+    const searchInput = document.querySelector(`#filter-${columnName} .filter-search`);
+    const searchTerm = searchInput.value.toLowerCase();
+    const options = document.getElementById(`options-${columnName}`);
+    Array.from(options.children).forEach(option => {
+        const text = option.textContent.toLowerCase();
+        option.style.display = text.includes(searchTerm) ? '' : 'none';
+    });
+}
+// Apply filter
+function applyFilter(columnName) {
+    const options = document.getElementById(`options-${columnName}`);
+    const checkedValues = Array.from(options.querySelectorAll('input[type="checkbox"]:checked'))
+        .map(cb => cb.value);
+    columnFilters[columnName] = checkedValues;
+    updateTableVisibility();
+    document.getElementById(`filter-${columnName}`).style.display = 'none';
+}
+// Clear filter
+function clearFilter(columnName) {
+    const options = document.getElementById(`options-${columnName}`);
+    options.querySelectorAll('input[type="checkbox"]').forEach(cb => cb.checked = false);
+    delete columnFilters[columnName];
+    updateTableVisibility();
+    document.getElementById(`filter-${columnName}`).style.display = 'none';
+}
+// Update table visibility based on filters
+function updateTableVisibility() {
+    const table = document.getElementById('leaderboardTable');
+    const rows = Array.from(table.querySelectorAll('tbody tr'));
+    const headers = Array.from(table.querySelectorAll('th'));
+    rows.forEach(row => {
+        let visible = true;
+        Object.entries(columnFilters).forEach(([columnName, selectedValues]) => {
+            if (!selectedValues.length) return;
+            const columnIndex = headers.findIndex(header =>
+                header.querySelector('span').textContent === columnName
+            );
+            const cellValue = row.cells[columnIndex].textContent.trim();
+            if (!selectedValues.includes(cellValue)) {
+                visible = false;
+            }
+        });
+        row.style.display = visible ? '' : 'none';
+    });
+}
+// Sort table
+function sortTable(columnIndex) {
+    const table = document.getElementById('leaderboardTable');
+    const tbody = table.querySelector('tbody');
+    const rows = Array.from(tbody.querySelectorAll('tr'));
+    // Update sort direction
+    if (currentSortColumn === columnIndex) {
+        isAscending = !isAscending;
+    } else {
+        currentSortColumn = columnIndex;
+        isAscending = true;
+    }
+    // Sort rows
+    rows.sort((a, b) => {
+        let aValue = a.cells[columnIndex].textContent;
+        let bValue = b.cells[columnIndex].textContent;
+        // Remove HTML tags for comparison
+        aValue = aValue.replace(/<[^>]*>/g, '').trim();
+        bValue = bValue.replace(/<[^>]*>/g, '').trim();
+        // Check if the values are numbers
+        const aNum = parseFloat(aValue);
+        const bNum = parseFloat(bValue);
+        if (!isNaN(aNum) && !isNaN(bNum)) {
+            return isAscending ? aNum - bNum : bNum - aNum;
+        }
+        // Sort as strings
+        return isAscending
+            ? aValue.localeCompare(bValue)
+            : bValue.localeCompare(aValue);
+    });
+    // Update table
+    tbody.innerHTML = '';
+    rows.forEach(row => tbody.appendChild(row));
+    // Update sort indicators
+    const headers = table.querySelectorAll('.sort-icon');
+    headers.forEach((icon, index) => {
+        icon.textContent = index === columnIndex
+            ? (isAscending ? '↑' : '↓')
+            : '↕';
+    });
+}
+// Initialize table sorting and filtering
+document.addEventListener('DOMContentLoaded', () => {
+    // Add event listener for checkboxes
+    const checkboxes = document.querySelectorAll('input[name="embdDim"]');
+    checkboxes.forEach(checkbox => {
+        checkbox.addEventListener('change', updateTable);
+    });
+});

app/templates/index.html ADDED Viewed

	@@ -0,0 +1,175 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>MTEB Leaderboard</title>
+    <!-- Semi Design CSS -->
+    <link rel="stylesheet" href="https://unpkg.com/@douyinfe/semi-ui/dist/css/semi.min.css">
+    <!-- Custom CSS -->
+    <link rel="stylesheet" href="{{ url_for('static', path='css/style.css') }}">
+    <style>
+        .sidebar-nav {
+            padding: 1rem;
+        }
+        .nav-item {
+            margin-bottom: 1rem;
+            border-radius: 8px;
+            overflow: hidden;
+        }
+        .nav-link {
+            display: flex;
+            align-items: center;
+            padding: 0.8rem 1rem;
+            color: #333;
+            text-decoration: none;
+            font-weight: 500;
+            transition: all 0.3s ease;
+        }
+        .nav-icon {
+            margin-right: 0.8rem;
+            font-size: 1.2rem;
+        }
+        .subnav {
+            display: none;
+            background: #f5f5f5;
+            padding: 0.5rem 0;
+        }
+        .nav-item.active .subnav {
+            display: block;
+        }
+        .subnav-link {
+            display: block;
+            padding: 0.5rem 1rem 0.5rem 2.8rem;
+            color: #666;
+            text-decoration: none;
+            font-size: 0.9rem;
+            transition: all 0.2s ease;
+        }
+        .subnav-link:hover {
+            background: #e0e0e0;
+            color: #333;
+        }
+        .subnav-link.active {
+            background: #e0e0e0;
+            color: #1890ff;
+            font-weight: 500;
+        }
+        .nav-item.active .nav-link {
+            background: #1890ff;
+            color: white;
+        }
+    </style>
+</head>
+<body>
+    <div class="app-container">
+        <!-- Sidebar -->
+        <aside class="sidebar">
+            <div class="sidebar-header">
+                <h2>Navigation</h2>
+            </div>
+            <nav class="sidebar-nav">
+                <div class="nav-item {% if active_tab == 'text' %}active{% endif %}">
+                    <a href="/?tab=text" class="nav-link">
+                        <span class="nav-icon">📝</span>
+                        Text Leaderboard
+                    </a>
+                    <div class="subnav">
+                        {% for subtype in ["text", "law", "long-context", "finance", "conversational", "tech", "multilingual", "code", "healthcare"] %}
+                        <a href="/?tab=text&subtype={{ subtype }}" class="subnav-link {% if active_subtype == subtype %}active{% endif %}">
+                            {{ subtype | title }}
+                        </a>
+                        {% endfor %}
+                    </div>
+                </div>
+                <div class="nav-item {% if active_tab == 'multimodal' %}active{% endif %}">
+                    <a href="/?tab=multimodal" class="nav-link">
+                        <span class="nav-icon">🖼️</span>
+                        Multimodal Leaderboard
+                    </a>
+                    <div class="subnav">
+                        {% for subtype in ["text-to-photo", "document-screenshot", "figures-and-tables", "text-to-text"] %}
+                        <a href="/?tab=multimodal&subtype={{ subtype }}" class="subnav-link {% if active_subtype == subtype %}active{% endif %}">
+                            {{ subtype | title }}
+                        </a>
+                        {% endfor %}
+                    </div>
+                </div>
+            </nav>
+        </aside>
+        <!-- Main Content -->
+        <div class="main-wrapper">
+            <header class="header">
+                <h1>Massive Text Embedding Benchmark (MTEB) Leaderboard</h1>
+            </header>
+            <main class="main-content">
+                <div class="table-container">
+                    <table id="leaderboardTable" class="semi-table">
+                        <thead>
+                            <tr>
+                                {% for col in columns %}
+                                <th>
+                                    <div class="column-header">
+                                        <div class="header-content">
+                                            <span>{{ col }}</span>
+                                            <div class="header-actions">
+                                                <button class="sort-btn" onclick="sortTable({{ loop.index0 }})">
+                                                    <span class="sort-icon">↕</span>
+                                                </button>
+                                                <button class="filter-btn" onclick="toggleFilter(event, '{{ col }}')">
+                                                    <span class="filter-icon">⚡</span>
+                                                </button>
+                                            </div>
+                                        </div>
+                                        <div class="filter-dropdown" id="filter-{{ col }}" style="display: none;">
+                                            <div class="filter-content">
+                                                <input type="text"
+                                                       class="filter-search"
+                                                       placeholder="Search..."
+                                                       oninput="filterOptions('{{ col }}')">
+                                                <div class="filter-options" id="options-{{ col }}">
+                                                    <!-- Options will be populated by JavaScript -->
+                                                </div>
+                                                <div class="filter-actions">
+                                                    <button onclick="applyFilter('{{ col }}')">Apply</button>
+                                                    <button onclick="clearFilter('{{ col }}')">Clear</button>
+                                                </div>
+                                            </div>
+                                        </div>
+                                    </div>
+                                </th>
+                                {% endfor %}
+                            </tr>
+                        </thead>
+                        <tbody>
+                            {% for row in data %}
+                            <tr>
+                                {% for cell in row %}
+                                <td>{{ cell | safe }}</td>
+                                {% endfor %}
+                            </tr>
+                            {% endfor %}
+                        </tbody>
+                    </table>
+                </div>
+            </main>
+        </div>
+    </div>
+    <!-- Semi Design JS -->
+    <script src="https://unpkg.com/@douyinfe/semi-ui/dist/js/semi.min.js"></script>
+    <!-- Custom JS -->
+    <script src="{{ url_for('static', path='js/main.js') }}"></script>
+</body>
+</html>

app/ui/component/subtabs_component.py CHANGED Viewed

@@ -31,11 +31,11 @@ class SubtabsComponent:
         df_result = df_result.sort_values(by=sort_col, ascending=False)
         items = []
-        for leaderboard, group_names in LEADERBOARD_MAP.items():
-            with gr.Column(visible=leaderboard.upper() == navigations.upper()) as column:
                 with gr.Tabs():
                     with gr.TabItem("overall"):
-                        df_leaderboard = df_result[df_result["leaderboard"] == leaderboard]
                         gr_df = gr.Dataframe(
                             df_leaderboard,
                             datatype=COLUMNS_TYPES,
@@ -46,9 +46,9 @@ class SubtabsComponent:
                         items.append(gr_df)
-                    for group_name in group_names:
-                        with gr.TabItem(group_name):
-                            df = df_leaderboard[df_leaderboard["group_name"] == group_name]
                             gr_df = gr.Dataframe(
                                 df,
                                 datatype=COLUMNS_TYPES,

         df_result = df_result.sort_values(by=sort_col, ascending=False)
         items = []
+        for group_name,leaderboards in LEADERBOARD_MAP.items():
+            with gr.Column(visible=group_name.upper() == navigations.upper()) as column:
                 with gr.Tabs():
                     with gr.TabItem("overall"):
+                        df_leaderboard = df_result[df_result["group_name"] == group_name]
                         gr_df = gr.Dataframe(
                             df_leaderboard,
                             datatype=COLUMNS_TYPES,
                         items.append(gr_df)
+                    for leaderboard in leaderboards:
+                        with gr.TabItem(leaderboard):
+                            df = df_leaderboard[df_leaderboard["leaderboard"] == leaderboard]
                             gr_df = gr.Dataframe(
                                 df,
                                 datatype=COLUMNS_TYPES,

mock_data/datasets.json DELETED Viewed

@@ -1,17 +0,0 @@
-[
-    {"group_name": "text", "dataset_names": ["LegalQuAD", "MBPP", "PyTorch1024", "TAT-QA"], "leaderboard": "text"},
-    {"group_name": "law", "dataset_names": ["LegalQuAD"], "leaderboard": "text"},
-    {"group_name": "multilingual", "dataset_names": ["LegalQuAD"], "leaderboard": "text"},
-    {"group_name": "german", "dataset_names": ["LegalQuAD"], "leaderboard": "text"},
-    {"group_name": "code", "dataset_names": ["MBPP"], "leaderboard": "text"},
-    {"group_name": "tech", "dataset_names": ["PyTorch1024", "TAT-QA"], "leaderboard": "text"},
-    {"group_name": "long-context", "dataset_names": ["LongContextData1", "LongContextData2"], "leaderboard": "text"},
-    {"group_name": "finance", "dataset_names": ["FinanceData1", "FinanceData2"], "leaderboard": "text"},
-    {"group_name": "conversational", "dataset_names": ["ConversationalData1", "ConversationalData2"], "leaderboard": "text"},
-    {"group_name": "healthcare", "dataset_names": ["HealthcareData1", "HealthcareData2"], "leaderboard": "text"},
-    {"group_name": "multimodal", "dataset_names": ["MultimodalData1", "MultimodalData2"], "leaderboard": "multimodal"},
-    {"group_name": "text-to-photo", "dataset_names": ["TextToPhotoData1", "TextToPhotoData2"], "leaderboard": "multimodal"},
-    {"group_name": "document-screenshot", "dataset_names": ["DocumentScreenshotData1", "DocumentScreenshotData2"], "leaderboard": "multimodal"},
-    {"group_name": "figures-and-tables", "dataset_names": ["FiguresAndTablesData1", "FiguresAndTablesData2"], "leaderboard": "multimodal"},
-    {"group_name": "text-to-text", "dataset_names": ["TextToTextData1", "TextToTextData2"], "leaderboard": "multimodal"}
-]

mock_data/models.json DELETED Viewed

@@ -1,113 +0,0 @@
-[
-    {"model_name":"sentence-transformers/all-MiniLM-L6-v2","embd_dtype":"float32","embd_dim":384,"num_params":22700000,"max_tokens":256,
-    "similarity":"cosine","query_instruct":null,"corpus_instruct":null,
-    "reference":"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"},
-    {"model_name":"jinaai/jina-embeddings-v2-small-en","embd_dtype":"float32","embd_dim":512,"num_params":32700000,"max_tokens":8192,
-    "similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://huggingface.co/jinaai/jina-embeddings-v2-small-en"},
-    {"model_name":"text-embedding-3-small","embd_dtype":"float32","embd_dim":1536,"num_params":null,"max_tokens":8191,
-    "similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://docs.voyageai.com/docs/embeddings"},
-    {"model_name":"voyage-3-lite","embd_dtype":"float32","embd_dim":512,"num_params":null,"max_tokens":32000,"similarity":"cosine",
-    "query_instruct":"Represent the query for retrieving supporting documents: ","corpus_instruct":"Represent the document for retrieval: ",
-    "reference":"https://docs.voyageai.com/docs/embeddings"},
-    {"model_name":"mock-model-1","embd_dtype":"float32","embd_dim":512,"num_params":5000000,"max_tokens":1024,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-1"},
-    {"model_name":"mock-model-2","embd_dtype":"int8","embd_dim":256,"num_params":10000000,"max_tokens":2048,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-2"},
-    {"model_name":"mock-model-3","embd_dtype":"binary","embd_dim":1024,"num_params":2000000,"max_tokens":4096,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-3"},
-    {"model_name":"mock-model-4","embd_dtype":"float32","embd_dim":768,"num_params":3000000,"max_tokens":512,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-4"},
-    {"model_name":"mock-model-5","embd_dtype":"int8","embd_dim":384,"num_params":7000000,"max_tokens":8192,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-5"},
-    {"model_name":"mock-model-6","embd_dtype":"binary","embd_dim":640,"num_params":15000000,"max_tokens":16384,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-6"},
-    {"model_name":"mock-model-7","embd_dtype":"float32","embd_dim":512,"num_params":2500000,"max_tokens":256,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-7"},
-    {"model_name":"mock-model-8","embd_dtype":"int8","embd_dim":1024,"num_params":8000000,"max_tokens":32000,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-8"},
-    {"model_name":"mock-model-9","embd_dtype":"binary","embd_dim":128,"num_params":9000000,"max_tokens":8191,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-9"},
-    {"model_name":"mock-model-10","embd_dtype":"float32","embd_dim":1536,"num_params":6000000,"max_tokens":4096,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-10"},
-    {"model_name":"mock-model-11","embd_dtype":"int8","embd_dim":512,"num_params":11000000,"max_tokens":1024,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-11"},
-    {"model_name":"mock-model-12","embd_dtype":"binary","embd_dim":256,"num_params":12000000,"max_tokens":2048,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-12"},
-    {"model_name":"mock-model-13","embd_dtype":"float32","embd_dim":1024,"num_params":13000000,"max_tokens":512,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-13"},
-    {"model_name":"mock-model-14","embd_dtype":"int8","embd_dim":768,"num_params":14000000,"max_tokens":16384,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-14"},
-    {"model_name":"mock-model-15","embd_dtype":"binary","embd_dim":384,"num_params":15000000,"max_tokens":32000,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-15"},
-    {"model_name":"mock-model-16","embd_dtype":"float32","embd_dim":640,"num_params":16000000,"max_tokens":256,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-16"},
-    {"model_name":"mock-model-17","embd_dtype":"int8","embd_dim":512,"num_params":17000000,"max_tokens":8192,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-17"},
-    {"model_name":"mock-model-18","embd_dtype":"binary","embd_dim":1024,"num_params":18000000,"max_tokens":4096,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-18"},
-    {"model_name":"mock-model-19","embd_dtype":"float32","embd_dim":128,"num_params":19000000,"max_tokens":1024,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-19"},
-    {"model_name":"mock-model-20","embd_dtype":"int8","embd_dim":1536,"num_params":20000000,"max_tokens":2048,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-20"},
-    {"model_name":"mock-model-21","embd_dtype":"binary","embd_dim":512,"num_params":21000000,"max_tokens":512,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-21"},
-    {"model_name":"mock-model-22","embd_dtype":"float32","embd_dim":256,"num_params":22000000,"max_tokens":16384,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-22"},
-    {"model_name":"mock-model-23","embd_dtype":"int8","embd_dim":1024,"num_params":23000000,"max_tokens":32000,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-23"},
-    {"model_name":"mock-model-24","embd_dtype":"binary","embd_dim":768,"num_params":24000000,"max_tokens":8191,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-24"},
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-    {"model_name":"mock-model-95","embd_dtype":"int8","embd_dim":384,"num_params":95000000,"max_tokens":2048,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-95"},
-    {"model_name":"mock-model-96","embd_dtype":"binary","embd_dim":640,"num_params":96000000,"max_tokens":512,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-96"},
-    {"model_name":"mock-model-97","embd_dtype":"float32","embd_dim":512,"num_params":97000000,"max_tokens":16384,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-97"},
-    {"model_name":"mock-model-98","embd_dtype":"int8","embd_dim":1024,"num_params":98000000,"max_tokens":32000,"similarity":"dot","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-98"},
-    {"model_name":"mock-model-99","embd_dtype":"binary","embd_dim":128,"num_params":99000000,"max_tokens":8191,"similarity":"euclidean","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-99"},
-    {"model_name":"mock-model-100","embd_dtype":"float32","embd_dim":1536,"num_params":100000000,"max_tokens":4096,"similarity":"cosine","query_instruct":null,"corpus_instruct":null,"reference":"https://example.com/mock-model-100"}
-]

mock_data/results.json DELETED Viewed

The diff for this file is too large to render. See raw diff

requirements.txt CHANGED Viewed

@@ -1,3 +1,7 @@
 gradio==4.20.0
 uvicorn>=0.14.0
 fastapi<0.113.0

 gradio==4.20.0
 uvicorn>=0.14.0
 fastapi<0.113.0
+jinja2>=3.0.0
+python-multipart>=0.0.5
+aiofiles>=0.8.0
+pandas>=1.3.0

utils/cache_decorator.py CHANGED Viewed

@@ -32,6 +32,27 @@ def cache_df_with_custom_key(cache_key: str):
     return decorator
 if __name__ == '__main__':
     a = time.time()
     time.sleep(5)

     return decorator
+def cache_dict_with_custom_key(cache_key: str):
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            if cache_key in CACHE and CACHE[cache_key].get("expiry") - time.time() < TTL:
+                return CACHE[cache_key]["data"]
+            result: dict = func(*args, **kwargs)
+            if result:
+                d = {"expiry": time.time(), "data": result}
+                CACHE[cache_key] = d
+                return result
+            CACHE[cache_key]["expiry"] += TTL
+            return CACHE[cache_key]["data"]
+        return wrapper
+    return decorator
 if __name__ == '__main__':
     a = time.time()
     time.sleep(5)

utils/http_utils.py ADDED Viewed

	@@ -0,0 +1,7 @@

+import requests
+def get(url: str, params: str = None, verify: bool = False):
+    return requests.get(url, params, verify=verify)