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from fastapi import FastAPI, Request
from requests import Response
from langchain_community.embeddings.ollama import OllamaEmbeddings
import numpy as np
import json
app = FastAPI(
title="FetishTest",
description="Game of matching fetish of users",
)
# set up
embed_model = OllamaEmbeddings(model="bge-m3")
with open("standard_character.json", "r") as f:
standard_character = json.load(f)
with open("Q&A.json", "r") as f:
answer2label = json.load(f)
# cosine similarity
def cosine_similarity(vec1, vec2):
vec1, vec2 = np.array(vec1), np.array(vec2)
return vec1.dot(vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
@app.post("/fetish")
async def matching(request: Request):
request = await request.json()
answer = request["answer"] # ['A', 'B' ...]
print(f"user_input: {answer}")
user_labels = []
for idx, ans in enumerate(answer):
curr_label = answer2label[idx][ans]
if curr_label not in user_labels:
user_labels.append(answer2label[idx][ans])
user_labels = sorted(user_labels)
print(f"user_labels: {user_labels}")
user_embedding = embed_model.embed_query(" ".join(user_labels))
matching_dict = {}
for character in standard_character:
sim = cosine_similarity(user_embedding, character["embedding"])
matching_dict[character["key"]] = sim
# sort by sim
matching_tuple = sorted(matching_dict.items(), key=lambda x: x[1], reverse=True)
# return the top 1
matched = matching_tuple[0][0]
sim = matching_tuple[0][1]
matched_name = standard_character[matched]["name"]
matched_label = standard_character[matched]["label"]
result = {
"result": matched
}
print(f"matched: {matched}")
print(f"{matched_name}: {matched_label} -- score: {sim}")
return result
if __name__ == "__main__":
import uvicorn
uvicorn.run(
"api:app",
host="0.0.0.0",
port=8002,
loop="asyncio",
workers=8,
limit_concurrency=10,
timeout_keep_alive=60,
access_log=True
) |