Commit
·
9978e32
1
Parent(s):
34225b1
Implement simple WDI MCP Server
Browse filesSigned-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
- mcp_client.py +1 -1
- pyproject.toml +8 -0
- uv.lock +697 -0
- wdi_mcp_server.py +78 -5
mcp_client.py
CHANGED
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@@ -240,7 +240,7 @@ def gradio_interface():
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server_path = gr.Textbox(
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label="Server Script Path",
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placeholder="Enter path to server script (e.g., weather.py)",
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-
value="
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)
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with gr.Column(scale=1):
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connect_btn = gr.Button("Connect")
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server_path = gr.Textbox(
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label="Server Script Path",
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placeholder="Enter path to server script (e.g., weather.py)",
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+
value="wdi_mcp_server.py",
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)
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with gr.Column(scale=1):
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connect_btn = gr.Button("Connect")
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pyproject.toml
CHANGED
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@@ -7,4 +7,12 @@ requires-python = ">=3.10"
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dependencies = [
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"anthropic>=0.51.0",
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"gradio[mcp]>=5.29.1",
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]
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dependencies = [
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"anthropic>=0.51.0",
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"gradio[mcp]>=5.29.1",
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+
"numpy>=2.2.6",
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+
"scikit-learn>=1.6.1",
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+
"sentence-transformers>=4.1.0",
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+
]
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+
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+
[dependency-groups]
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+
dev = [
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+
"ipython>=8.36.0",
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]
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uv.lock
CHANGED
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@@ -58,6 +58,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
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]
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[[package]]
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name = "audioop-lts"
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version = "0.2.1"
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@@ -189,6 +198,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
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[[package]]
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name = "distro"
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version = "1.9.0"
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@@ -210,6 +228,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674 },
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[[package]]
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name = "fastapi"
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version = "0.115.12"
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@@ -396,6 +423,40 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
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[[package]]
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name = "jinja2"
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version = "3.1.6"
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@@ -480,6 +541,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/b3/4a/4175a563579e884192ba6e81725fc0448b042024419be8d83aa8a80a3f44/jiter-0.10.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3aa96f2abba33dc77f79b4cf791840230375f9534e5fac927ccceb58c5e604a5", size = 354213 },
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]
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[[package]]
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name = "markdown-it-py"
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version = "3.0.0"
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@@ -550,6 +620,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/4f/65/6079a46068dfceaeabb5dcad6d674f5f5c61a6fa5673746f42a9f4c233b3/MarkupSafe-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:e444a31f8db13eb18ada366ab3cf45fd4b31e4db1236a4448f68778c1d1a5a2f", size = 15739 },
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]
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[[package]]
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name = "mcp"
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version = "1.9.0"
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@@ -579,6 +661,24 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 },
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]
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[[package]]
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name = "numpy"
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version = "2.2.6"
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@@ -641,6 +741,160 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/37/48/ac2a9584402fb6c0cd5b5d1a91dcf176b15760130dd386bbafdbfe3640bf/numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00", size = 12812666 },
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]
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[[package]]
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name = "orjson"
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version = "3.10.18"
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@@ -764,6 +1018,27 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436 },
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]
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[[package]]
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name = "pillow"
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version = "11.2.1"
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@@ -841,6 +1116,36 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/21/2c/5e05f58658cf49b6667762cca03d6e7d85cededde2caf2ab37b81f80e574/pillow-11.2.1-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:208653868d5c9ecc2b327f9b9ef34e0e42a4cdd172c2988fd81d62d2bc9bc044", size = 2674751 },
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]
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[[package]]
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name = "pydantic"
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version = "2.11.4"
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@@ -1058,6 +1363,75 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
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]
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[[package]]
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name = "requests"
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version = "2.32.3"
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@@ -1124,6 +1498,122 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/4d/c0/1108ad9f01567f66b3154063605b350b69c3c9366732e09e45f9fd0d1deb/safehttpx-0.1.6-py3-none-any.whl", hash = "sha256:407cff0b410b071623087c63dd2080c3b44dc076888d8c5823c00d1e58cb381c", size = 8692 },
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| 1127 |
[[package]]
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| 1128 |
name = "semantic-version"
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| 1129 |
version = "2.10.0"
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@@ -1133,6 +1623,34 @@ wheels = [
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| 1133 |
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[[package]]
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| 1878 |
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| 1879 |
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|
| 1880 |
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|
| 1881 |
+
{ name = "filelock" },
|
| 1882 |
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{ name = "huggingface-hub" },
|
| 1883 |
+
{ name = "numpy" },
|
| 1884 |
+
{ name = "packaging" },
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| 1885 |
+
{ name = "pyyaml" },
|
| 1886 |
+
{ name = "regex" },
|
| 1887 |
+
{ name = "requests" },
|
| 1888 |
+
{ name = "safetensors" },
|
| 1889 |
+
{ name = "tokenizers" },
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| 1890 |
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{ name = "tqdm" },
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| 1891 |
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| 1892 |
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| 1895 |
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| 1896 |
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| 1897 |
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| 1898 |
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| 1899 |
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| 1900 |
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| 1902 |
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| 1911 |
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| 1912 |
[[package]]
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| 1913 |
name = "typer"
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| 1914 |
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| 1977 |
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| 1987 |
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|
| 1988 |
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|
| 1989 |
[[package]]
|
| 1990 |
name = "websockets"
|
| 1991 |
version = "15.0.1"
|
wdi_mcp_server.py
CHANGED
|
@@ -3,15 +3,67 @@ import json
|
|
| 3 |
import sys
|
| 4 |
import io
|
| 5 |
import time
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6 |
from gradio_client import Client
|
| 7 |
from pydantic import BaseModel, Field
|
| 8 |
|
|
|
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|
|
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|
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|
|
|
| 9 |
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
|
| 10 |
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace")
|
| 11 |
|
|
|
|
| 12 |
mcp = FastMCP("huggingface_spaces_wdi_data")
|
| 13 |
|
| 14 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 15 |
@mcp.tool()
|
| 16 |
async def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:
|
| 17 |
"""Generate an image using SanaSprint model.
|
|
@@ -50,6 +102,9 @@ async def generate_image(prompt: str, width: int = 512, height: int = 512) -> st
|
|
| 50 |
class SearchOutput(BaseModel):
|
| 51 |
idno: str = Field(..., description="The unique identifier of the indicator.")
|
| 52 |
name: str = Field(..., description="The name of the indicator.")
|
|
|
|
|
|
|
|
|
|
| 53 |
definition: str | None = Field(None, description="The indicator definition.")
|
| 54 |
|
| 55 |
|
|
@@ -62,14 +117,32 @@ async def search_relevant_indicators(query: str, top_k: int = 1) -> list[SearchO
|
|
| 62 |
top_k: The number of shortlisted indicators that will be returned that are semantically related to the query.
|
| 63 |
|
| 64 |
Returns:
|
| 65 |
-
List of objects with keys indicator name
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 66 |
"""
|
|
|
|
|
|
|
| 67 |
|
| 68 |
return [
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
definition="GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.",
|
| 73 |
)
|
| 74 |
]
|
| 75 |
|
|
|
|
| 3 |
import sys
|
| 4 |
import io
|
| 5 |
import time
|
| 6 |
+
import numpy as np
|
| 7 |
+
import pandas as pd
|
| 8 |
+
import torch
|
| 9 |
+
from sklearn.metrics.pairwise import cosine_similarity
|
| 10 |
+
from sentence_transformers import SentenceTransformer
|
| 11 |
from gradio_client import Client
|
| 12 |
from pydantic import BaseModel, Field
|
| 13 |
|
| 14 |
+
|
| 15 |
+
def get_best_torch_device():
|
| 16 |
+
if torch.cuda.is_available():
|
| 17 |
+
return torch.device("cuda")
|
| 18 |
+
elif getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
|
| 19 |
+
return torch.device("mps")
|
| 20 |
+
else:
|
| 21 |
+
return torch.device("cpu")
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
device = get_best_torch_device()
|
| 25 |
+
|
| 26 |
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
|
| 27 |
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace")
|
| 28 |
|
| 29 |
+
|
| 30 |
mcp = FastMCP("huggingface_spaces_wdi_data")
|
| 31 |
|
| 32 |
|
| 33 |
+
# Load the basic WDI metadata and vectors.
|
| 34 |
+
wdi_data_vec_fpath = (
|
| 35 |
+
"./data/avsolatorio__GIST-small-Embedding-v0__005__indicator_embeddings.json"
|
| 36 |
+
)
|
| 37 |
+
df = pd.read_json(wdi_data_vec_fpath)
|
| 38 |
+
|
| 39 |
+
# Make it easy to index based on the idno
|
| 40 |
+
df.index = df["idno"]
|
| 41 |
+
|
| 42 |
+
# Change the IDS naming to metadata standard
|
| 43 |
+
df.rename(columns={"title": "name", "text": "definition"}, inplace=True)
|
| 44 |
+
|
| 45 |
+
# Extract the vectors into a torch.tensor
|
| 46 |
+
vectors = torch.Tensor(df["embedding"]).to(device)
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
# Load the embedding model
|
| 50 |
+
model_name = "/".join(wdi_data_vec_fpath.split("/")[-1].split("__")[:2])
|
| 51 |
+
embedding_model = SentenceTransformer(model_name, device=device)
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def get_top_k(query: str, top_k: int = 10, fields: list[str] | None = None):
|
| 55 |
+
if fields is None:
|
| 56 |
+
fields = ["idno"]
|
| 57 |
+
|
| 58 |
+
# Convert the query to a search vector
|
| 59 |
+
search_vec = embedding_model.encode([query], convert_to_tensor=True) @ vectors.T
|
| 60 |
+
|
| 61 |
+
# Sort by descending similarity score
|
| 62 |
+
idx = search_vec.argsort(descending=True)[0][:top_k].tolist()
|
| 63 |
+
|
| 64 |
+
return df.iloc[idx][fields].to_dict("records")
|
| 65 |
+
|
| 66 |
+
|
| 67 |
@mcp.tool()
|
| 68 |
async def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:
|
| 69 |
"""Generate an image using SanaSprint model.
|
|
|
|
| 102 |
class SearchOutput(BaseModel):
|
| 103 |
idno: str = Field(..., description="The unique identifier of the indicator.")
|
| 104 |
name: str = Field(..., description="The name of the indicator.")
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
class DetailedOutput(SearchOutput):
|
| 108 |
definition: str | None = Field(None, description="The indicator definition.")
|
| 109 |
|
| 110 |
|
|
|
|
| 117 |
top_k: The number of shortlisted indicators that will be returned that are semantically related to the query.
|
| 118 |
|
| 119 |
Returns:
|
| 120 |
+
List of objects with keys indicator code/idno and name.
|
| 121 |
+
"""
|
| 122 |
+
|
| 123 |
+
return [
|
| 124 |
+
SearchOutput(**out)
|
| 125 |
+
for out in get_top_k(query=query, top_k=top_k, fields=["idno", "name"])
|
| 126 |
+
]
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
@mcp.tool()
|
| 130 |
+
async def indicator_info(indicator_ids: list[str]) -> list[DetailedOutput]:
|
| 131 |
+
"""Provides definition information for the given indicator id (idno).
|
| 132 |
+
|
| 133 |
+
Args:
|
| 134 |
+
indicator_ids: A list of indicator ids (idno) that additional information is being requested.
|
| 135 |
+
|
| 136 |
+
Returns:
|
| 137 |
+
List of objects with keys indicator code/idno, name, and definition.
|
| 138 |
"""
|
| 139 |
+
if isinstance(indicator_ids, str):
|
| 140 |
+
indicator_ids = [indicator_ids]
|
| 141 |
|
| 142 |
return [
|
| 143 |
+
DetailedOutput(**out)
|
| 144 |
+
for out in df.loc[indicator_ids][["idno", "name", "definition"]].to_dict(
|
| 145 |
+
"records"
|
|
|
|
| 146 |
)
|
| 147 |
]
|
| 148 |
|