:clown: changed repo structure for API execution

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.10.12
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["mkdir", "./.cache"]
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "my_web_app:app"]

MVP-explore.ipynb

@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7a99e5ef-7aee-4ec0-8099-82dac93d4614",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/c/Users/hew7/Documents/venvs/ranking-challenge/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "import numpy as np\n",
+    "from numpy.linalg import norm\n",
+    "from scipy.stats import rankdata\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "#sample data\n",
+    "from sample_data import BASIC_EXAMPLE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "01db2542-4293-4df5-91dd-6165e2180f05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encodingModel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "87892ace-2df7-4bc6-9569-b8b99b13b744",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create embeddings from example texts\n",
+    "\n",
+    "#left wing\n",
+    "with open('/mnt/c/Users/hew7/Documents/Git/ChaiProsocialRankingChallenge/flask-test/manifesto-left.txt', 'r') as f:\n",
+    "    LeftWingStr=f.read()\n",
+    "\n",
+    "#right wing\n",
+    "with open('/mnt/c/Users/hew7/Documents/Git/ChaiProsocialRankingChallenge/flask-test/manifesto-right.txt', 'r') as f:\n",
+    "    RightWingStr=f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e7cf9ca9-402e-4ba2-be33-f9356a0c6b9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LWPair=[LeftWingStr, encodingModel.encode(LeftWingStr)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4537352f-efa6-487f-a3bb-4f10bf439190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RWPair=[RightWingStr, encodingModel.encode(RightWingStr)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae4e52ea-cbb3-462b-8b34-58c83bfb6dbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pulling in examples\n",
+    "example_texts = [x['text'] for x in BASIC_EXAMPLE['items']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "772da89d-e4d6-419c-a4ab-3f0b023ac068",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['this is the worst thing I have ever seen!',\n",
+       " 'this is amazing!',\n",
+       " 'this thing is ok.']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ce6ffea3-a5b2-42b9-9895-82cc18431272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = encodingModel.encode(example_texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "83f809cf-9616-4f1d-8cf1-e1e1c15fefe6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cosine similarity \n",
+    "\n",
+    "def cosineSim(x, y) -> float: #type hint for np array I think - but I'll figure it out later\n",
+    "    xArray=np.array(x)\n",
+    "    yArray=np.array(y)\n",
+    "    cosine=np.dot(xArray,yArray)/(norm(xArray)*norm(yArray))\n",
+    "    return cosine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "363c66d4-295b-4eda-9771-d1688260619e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ranking func, purely cosine similarity ----- KINDA JANKY\n",
+    "def cosineRank(lhs: list, rhs: list, ) -> list:\n",
+    "    '''\n",
+    "    returns list of rankings in order of embeddings\n",
+    "    '''\n",
+    "    similarity_list=[]\n",
+    "    for candidate in rhs:\n",
+    "        similarity_list.append(cosineSim(lhs, candidate))\n",
+    "    results = rankdata(similarity_list) - 1\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "41a04657-8020-4d27-a48f-28e4bd5795b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sort_text_cosine(LHSEmbedding, RHSEmbeddingList, RHSTextList) -> list:\n",
+    "    result_order = cosineRank(LHSEmbedding, RHSEmbeddingList)\n",
+    "    print(result_order)\n",
+    "    output = [RHSTextList[int(x)] for x in result_order]\n",
+    "    return output\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "36561978-bf59-4d9d-b6be-b78307fccd0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 0. 2.]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['this is amazing!',\n",
+       " 'this is the worst thing I have ever seen!',\n",
+       " 'this thing is ok.']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sort_text_cosine(LWPair[1],embeddings, example_texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7e52c93e-135a-4ee5-b41e-1d7d0308f7d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 1. 2.]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['this is the worst thing I have ever seen!',\n",
+       " 'this is amazing!',\n",
+       " 'this thing is ok.']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sort_text_cosine(RWPair[1],embeddings, example_texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f2a3bf61-5614-4502-8a39-2691761bb12e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#trying to write a function that inputs and outputs dicts (start to end for API)\n",
+    "def rankingfunc(inputJSON: dict) -> dict:\n",
+    "    '''\n",
+    "    WIP - super gross func but it works for now\n",
+    "    \n",
+    "    Final ranking func using previously defined encodingModel and cosine sim to rank similarity to left-wing\n",
+    "    or right-wing text file. Tested on provided example json from sample_data. Returns identically structured\n",
+    "    json with reordered results.\n",
+    "    '''\n",
+    "    \n",
+    "    #change LHS based on userID:\n",
+    "    if inputJSON['session']['user_id'] in ['193a9e01-8849-4e1f-a42a-a859fa7f2ad3']: #change this list to be for all users selected for left_wing\n",
+    "        LHS=LWPair\n",
+    "    else:\n",
+    "        LHS=RWPair\n",
+    "\n",
+    "    #prepare data and get embeddings\n",
+    "    candidates = inputJSON['items']\n",
+    "    texts=[x['text'] for x in candidates]\n",
+    "    embeddings=encodingModel.encode(texts)\n",
+    "\n",
+    "    #rerank\n",
+    "    item_rank=cosineRank(LHS[1], embeddings)\n",
+    "    for index in range(len(candidates)):\n",
+    "        candidates[index]['rank']=item_rank[index]\n",
+    "    output_list = sorted(candidates, key=lambda x: x['rank'])\n",
+    "    for i in output_list:\n",
+    "        del i['rank']\n",
+    "    \n",
+    "    #prep data for export\n",
+    "    output_dict=deepcopy(inputJSON)\n",
+    "    output_dict['items']=output_list\n",
+    "\n",
+    "    return output_dict\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "effed0f2-cea2-4f3b-8739-f71d4cce8297",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ranking-challenge",
+   "language": "python",
+   "name": "ranking-challenge"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

@@ -1,5 +1,11 @@
-# ChaiProsocialRankingChallenge
+---
+title: Flasktest
+emoji: 💻
+colorFrom: purple
+colorTo: indigo
+sdk: docker
+pinned: false
+license: other
+---
 
-Repo for our team submission.
-
-More details to follow...
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Untitled.ipynb

@@ -0,0 +1,65 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "80cb496d-6ca6-45aa-99d0-7477827db572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from sample_data import BASIC_EXAMPLE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1b32eed7-8a2a-4673-8001-92af4c659047",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Response [404]>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "requests.post('https://huggingface.co/spaces/willsh1997/reranker-v1/rank', json=BASIC_EXAMPLE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f91b6451-c596-4fcb-ade5-6f812ae629e9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ranking-challenge",
+   "language": "python",
+   "name": "ranking-challenge"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

manifesto-left.txt

@@ -0,0 +1,47 @@
+# Core Beliefs
+I am a liberal voter.
+I support, minority rights, economic equality, gun control, environmental protection, expanded educational opportunity, and social nets for those who need them.
+
+# Economic policy
+We need income equality.
+It is important to have higher tax rates on the wealthy.
+Governments should spend more on social programs and infrastructure.
+There should be stronger regulations on business.
+
+# Healthcare policy
+Access to healthcare is one of the fundamental rights of all citizens.
+Universal healthcare is an excellent idea.
+The Affordable Care Act and expansion of Medicare and Medicaid are all important government initiatives.
+
+# Immigration Policy
+There should be a pathway to citizenship for undocumented immigrants.
+We need a moratorium on deportations or prosecutions of undocumented immigrants who are young adults and have no criminal record.
+
+# Education Policy
+There should be expanded free, public education.
+
+# Abortion
+Unpenalized access to abortion is a good idea in generla.
+Adult and embryotic stem cell research should be funded and supported.
+
+# Gay Rights
+Gay marriage should be institutionalised and supported across the board.
+We need anti-discrimination laws to protect LGBT community members against workplace discrimination.
+
+# Gun Rights
+Gun control laws such as background checks, or waiting periods before buying a gun are needed.
+We should ban automatic weapons.
+There should be strong limitations on the carrying of concealed weapons.
+
+# Environmental policy
+We should ban economic activity that may create jobs but could potentially harm the environment - preserving this natual resource is an important duty of current generations toward future generations.
+
+# Voter ID Laws
+Voter ID laws place an undue burden on lower income groups, causing them to be disenfranchised.
+There is virtually no evidence of voter fraud actually occurring en-masse.
+
+# Associated Media
+High quality media outlets include The New York Times, MSNBC, Washington Post, and CNN.
+
+# Ideologies
+Social Democracy, Federalism, Socialism, Communism, Collectivism, and Marxism are all political ideologies that are worthwhile and could offer something to our society if considered in more detail.

manifesto-right.txt

@@ -0,0 +1,51 @@
+# Core Beliefs
+I am a conservative voter.
+I support limited Government at the national and federal levels, and think local governments should have the most control over decisions affecting local populations.
+Individual freedom and personal property rights are the most important types of freedom and rights.
+
+# Economic policy
+We need lower taxes.
+There should be less regulation on businesses so that they can produce economic output, which will then help everyone.
+There should be reduced government spending.
+We need to balance the budget.
+
+# Healthcare policy
+I oppose government-provided universal healthcare.
+The Affordable Care Act was a waste of taxpayer money.
+Medicare should have competition from private insurance companies.
+Expanding Medicaid would achieve nothing and in-fact harm many citizens.
+
+# Immigration Policy
+There shouldn't be any "amnesty" for undocumented immigrants.
+We need a much stronger border patrol and border fences to check and stop illegal immigration.
+Illegal immigration is lowering wages for citizens and documented immigrants.
+
+# Education Policy
+Parents who want to home-school their kids or send them to private school should be able to get vouchers for opting out of the public school system.
+Public education is good, except when it is forced on parents and children, or when the government over-steps in controlling educational content.
+
+# Abortion
+Abortion 'rights' are a farce, protection of unborn life is much more important.
+I support only adult stem cell research, or better yet, no stem cell reserach.
+
+# Gay Rights
+Gay marriage is in general a bad idea and shouldn't be endorsed.
+Anti-discrimination laws conflict with certain religious beliefs and restrict freedom of religion - these should be repealed.
+
+# Gun Rights
+Gun control laws should be strongly opposed.
+The Second Amendment (the right to bear arms) is the most important ammendment, as it is a deterrent against authoritarian rule by a corrupt government.
+
+# Environmental policy
+We need to carefully weight the economic impact of environmental regulation.
+Economic progress is more important that environmental conservation.
+The free market will find its own solution to environmental problems.
+
+# Voter ID Laws
+Voter ID laws are needed to combat voter fraud.
+
+# Associated Media
+High quality media outlets include National Review, Fox News, Wall Street Journal, and Washington Times.
+
+# Ideologies
+Capitalism and Conservatism are political ideologies that are worthwhile and could offer something to our society if considered in more detail.

my_web_app.py

@@ -0,0 +1,42 @@
+
+from flask import Flask, jsonify, request
+from flask_cors import CORS
+from reranker import rankingfunc
+
+app = Flask(__name__)
+CORS(app)
+
+
+@app.route('/')
+def hello_world():
+    return 'Hello, Flask!'
+
+@app.route('/test')
+def hello_world_test():
+    return 'This is a test and you passed :)'
+
+
+@app.route('/rank', methods=['POST'])
+def perform_ranking():
+
+    #print(request)
+    #print(request.data)
+    post_data = request.json
+    # print(post_data)
+
+    # Get session details
+    session_details = post_data.get("session")
+    print(session_details)
+    """
+    user_id: A unique ID for this study participant.
+    user_name_hash: A (salted) hash of the user's username. We'll do our best to make it match the author_name_hash on posts authored by the current user.
+    platform: One of reddit, twitter, facebook
+    current_time: The current time according to the user's browser, in UTC, in YYYY-MM-DD hh:mm:ss format.
+    """
+    results = rankingfunc(post_data)
+
+    return jsonify(results)
+
+
+if __name__ == "__main__":
+    app.run(port=5001, debug=True)

requirements.txt

@@ -0,0 +1,165 @@
+aiohttp==3.9.3
+aiosignal==1.3.1
+annotated-types==0.6.0
+anyio==4.3.0
+asgiref==3.8.1
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.2.0
+backoff==2.2.1
+bcrypt==4.1.2
+blinker==1.7.0
+build==1.2.1
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+chroma-hnswlib==0.7.3
+chromadb==0.4.24
+click==8.1.7
+coloredlogs==15.0.1
+comm==0.2.2
+dataclasses-json==0.6.4
+debugpy==1.8.1
+decorator==5.1.1
+Deprecated==1.2.14
+exceptiongroup==1.2.0
+executing==2.0.1
+fastapi==0.110.1
+filelock==3.13.3
+Flask==3.0.3
+Flask-Cors==4.0.0
+flatbuffers==24.3.25
+frozenlist==1.4.1
+fsspec==2024.3.1
+google-auth==2.29.0
+googleapis-common-protos==1.63.0
+greenlet==3.0.3
+grpcio==1.62.1
+h11==0.14.0
+httptools==0.6.1
+huggingface-hub==0.22.2
+humanfriendly==10.0
+idna==3.6
+importlib-metadata==7.0.0
+importlib_resources==6.4.0
+ipykernel==6.29.4
+ipython==8.23.0
+itsdangerous==2.1.2
+jedi==0.19.1
+Jinja2==3.1.3
+joblib==1.3.2
+jsonpatch==1.33
+jsonpointer==2.4
+jupyter_client==8.6.1
+jupyter_core==5.7.2
+kubernetes==29.0.0
+langchain==0.1.14
+langchain-community==0.0.31
+langchain-core==0.1.40
+langchain-text-splitters==0.0.1
+langsmith==0.1.40
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+marshmallow==3.21.1
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mmh3==4.1.0
+monotonic==1.6
+mpmath==1.3.0
+multidict==6.0.5
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+networkx==3.3
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.19.3
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.1.105
+oauthlib==3.2.2
+onnxruntime==1.17.1
+opentelemetry-api==1.24.0
+opentelemetry-exporter-otlp-proto-common==1.24.0
+opentelemetry-exporter-otlp-proto-grpc==1.24.0
+opentelemetry-instrumentation==0.45b0
+opentelemetry-instrumentation-asgi==0.45b0
+opentelemetry-instrumentation-fastapi==0.45b0
+opentelemetry-proto==1.24.0
+opentelemetry-sdk==1.24.0
+opentelemetry-semantic-conventions==0.45b0
+opentelemetry-util-http==0.45b0
+orjson==3.10.0
+overrides==7.7.0
+packaging==23.2
+pandas==2.2.1
+parso==0.8.4
+pexpect==4.9.0
+pillow==10.3.0
+platformdirs==4.2.0
+posthog==3.5.0
+prompt-toolkit==3.0.43
+protobuf==4.25.3
+psutil==5.9.8
+ptyprocess==0.7.0
+pulsar-client==3.4.0
+pure-eval==0.2.2
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pydantic==2.6.4
+pydantic_core==2.16.3
+Pygments==2.17.2
+PyPika==0.48.9
+pyproject_hooks==1.0.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+pytz==2024.1
+PyYAML==6.0.1
+pyzmq==25.1.2
+regex==2023.12.25
+requests==2.31.0
+requests-oauthlib==2.0.0
+rich==13.7.1
+rsa==4.9
+safetensors==0.4.2
+scikit-learn==1.4.1.post1
+scipy==1.13.0
+sentence-transformers==2.6.1
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+SQLAlchemy==2.0.29
+stack-data==0.6.3
+starlette==0.37.2
+sympy==1.12
+tenacity==8.2.3
+threadpoolctl==3.4.0
+tokenizers==0.15.2
+tomli==2.0.1
+torch==2.2.2
+tornado==6.4
+tqdm==4.66.2
+traitlets==5.14.2
+transformers==4.39.3
+triton==2.2.0
+typer==0.12.1
+typing-inspect==0.9.0
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+uvicorn==0.29.0
+uvloop==0.19.0
+watchfiles==0.21.0
+wcwidth==0.2.13
+websocket-client==1.7.0
+websockets==12.0
+Werkzeug==3.0.2
+wrapt==1.16.0
+yarl==1.9.4
+zipp==3.18.1

reranker.py

@@ -0,0 +1,100 @@
+import os
+import requests
+import numpy as np
+from numpy.linalg import norm
+from scipy.stats import rankdata
+from sentence_transformers import SentenceTransformer
+from copy import deepcopy
+
+#sample data
+from sample_data import BASIC_EXAMPLE
+
+#environment setup for HF docker image
+
+
+#load model
+encodingModel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+
+#create embeddings from example texts
+os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'
+
+#left wing
+with open('/mnt/c/Users/hew7/Documents/Git/ChaiProsocialRankingChallenge/flask-test/manifesto-left.txt', 'r') as f:
+    LeftWingStr=f.read()
+
+#right wing
+with open('/mnt/c/Users/hew7/Documents/Git/ChaiProsocialRankingChallenge/flask-test/manifesto-right.txt', 'r') as f:
+    RightWingStr=f.read()
+
+LWPair=[LeftWingStr, encodingModel.encode(LeftWingStr)]
+
+RWPair=[RightWingStr, encodingModel.encode(RightWingStr)]
+
+#cosine similarity 
+
+def cosineSim(x, y) -> float: #type hint for np array I think - but I'll figure it out later
+    xArray=np.array(x)
+    yArray=np.array(y)
+    cosine=np.dot(xArray,yArray)/(norm(xArray)*norm(yArray))
+    return cosine
+
+#ranking func, purely cosine similarity ----- KINDA JANKY
+def cosineRank(lhs: list, rhs: list, ) -> list:
+    '''
+    returns list of rankings in order of embeddings
+    '''
+    similarity_list=[]
+    for candidate in rhs:
+        similarity_list.append(cosineSim(lhs, candidate))
+    results = rankdata(similarity_list) - 1
+    return results
+
+def sort_text_cosine(LHSEmbedding, RHSEmbeddingList, RHSTextList) -> list:
+    result_order = cosineRank(LHSEmbedding, RHSEmbeddingList)
+    print(result_order)
+    output = [RHSTextList[int(x)] for x in result_order]
+    return output
+    
+#trying to write a function that inputs and outputs dicts (start to end for API)
+def rankingfunc(inputJSON: dict) -> dict:
+    '''
+    WIP - super gross func but it works for now
+    
+    Final ranking func using previously defined encodingModel and cosine sim to rank similarity to left-wing
+    or right-wing text file. Tested on provided example json from sample_data. Returns identically structured
+    json with reordered results.
+    '''
+    
+    #change LHS based on userID:
+    if inputJSON['session']['user_id'] in ['193a9e01-8849-4e1f-a42a-a859fa7f2ad3']: #change this list to be for all users selected for left_wing
+        LHS=LWPair
+    else:
+        LHS=RWPair
+
+    #prepare data and get embeddings
+    candidates = inputJSON['items']
+    texts=[x['text'] for x in candidates]
+    embeddings=encodingModel.encode(texts)
+
+    #rerank
+    item_rank=cosineRank(LHS[1], embeddings)
+    for index in range(len(candidates)):
+        candidates[index]['rank']=item_rank[index]
+    output_list = sorted(candidates, key=lambda x: x['rank'])
+    for i in output_list:
+        del i['rank']
+    
+    #prep data for export
+    output_dict=deepcopy(inputJSON)
+    output_dict['items']=output_list
+
+    return output_dict
+
+        
+
+
+# In[ ]:
+
+
+
+

sample_data.py

@@ -0,0 +1,55 @@
+# Sample data containing multiple text items
+
+BASIC_EXAMPLE = {
+    "session": {
+        "user_id": "193a9e01-8849-4e1f-a42a-a859fa7f2ad3",
+        "user_name_hash": "6511c5688bbb87798128695a283411a26da532df06e6e931a53416e379ddda0e",
+        "current_time": "2024-01-20 18:41:20",
+    },
+    "items": [
+        {
+            "id": "de83fc78-d648-444e-b20d-853bf05e4f0e",
+            "title": "this is the post title, available only on reddit",
+            "text": "this is the worst thing I have ever seen!",
+            "author_name_hash": "60b46b7370f80735a06b7aa8c4eb6bd588440816b086d5ef7355cf202a118305",
+            "type": "post",
+            "platform": "reddit",
+            "created_at": "2023-12-06 17:02:11",
+            "enagements": {"upvote": 34, "downvote": 27},
+        },
+        {
+            "id": "s5ad13266-8abk4-5219-kre5-2811022l7e43dv",
+            "post_id": "de83fc78-d648-444e-b20d-853bf05e4f0e",
+            "parent_id": "",
+            "text": "this is amazing!",
+            "author_name_hash": "60b46b7370f80735a06b7aa8c4eb6bd588440816b086d5ef7355cf202a118305",
+            "type": "comment",
+            "platform": "reddit",
+            "created_at": "2023-12-08 11:32:12",
+            "enagements": {"upvote": 15, "downvote": 2},
+        },
+        {
+            "id": "a4c08177-8db2-4507-acc1-1298220be98d",
+            "post_id": "de83fc78-d648-444e-b20d-853bf05e4f0e",
+            "parent_id": "s5ad13266-8abk4-5219-kre5-2811022l7e43dv",
+            "text": "this thing is ok.",
+            "author_name_hash": "60b46b7370f80735a06b7aa8c4eb6bd588440816b086d5ef7355cf202a118305",
+            "type": "comment",
+            "platform": "reddit",
+            "created_at": "2023-12-08 11:35:00",
+            "enagements": {"upvote": 3, "downvote": 5},
+        },
+    ],
+}
+
+# some new posts that can be added to the response
+NEW_POSTS = [
+    {
+        "id": "571775f3-2564-4cf5-b01c-f4cb6bab461b",
+        "url": "https://reddit.com/r/PRCExample/comments/1f33ead/example_to_insert",
+    },
+    {
+        "id": "1fcbb164-f81f-4532-b068-2561941d0f63",
+        "url": "https://reddit.com/r/PRCExample/comments/ef56a23/another_example_to_insert",
+    },
+]