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authorTekky <98614666+xtekky@users.noreply.github.com>2023-10-12 15:32:50 +0200
committerGitHub <noreply@github.com>2023-10-12 15:32:50 +0200
commit86248b44bcb4261c627335e24f5713e242793ece (patch)
tree59671d1777b76bade80b4a0c5ce8a42121878a9e /etc/interference/app.py
parent~ | Merge pull request #1053 from Lin-jun-xiang/fix_GptGo (diff)
parentchange "Models" to "Providers" (diff)
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Diffstat (limited to 'etc/interference/app.py')
-rw-r--r--etc/interference/app.py163
1 files changed, 0 insertions, 163 deletions
diff --git a/etc/interference/app.py b/etc/interference/app.py
deleted file mode 100644
index 5abbcff2..00000000
--- a/etc/interference/app.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import json
-import time
-import random
-import string
-import requests
-
-from typing import Any
-from flask import Flask, request
-from flask_cors import CORS
-from transformers import AutoTokenizer
-from g4f import ChatCompletion
-
-app = Flask(__name__)
-CORS(app)
-
-@app.route('/chat/completions', methods=['POST'])
-def chat_completions():
- model = request.get_json().get('model', 'gpt-3.5-turbo')
- stream = request.get_json().get('stream', False)
- messages = request.get_json().get('messages')
-
- response = ChatCompletion.create(model = model,
- stream = stream, messages = messages)
-
- completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
- completion_timestamp = int(time.time())
-
- if not stream:
- return {
- 'id': f'chatcmpl-{completion_id}',
- 'object': 'chat.completion',
- 'created': completion_timestamp,
- 'model': model,
- 'choices': [
- {
- 'index': 0,
- 'message': {
- 'role': 'assistant',
- 'content': response,
- },
- 'finish_reason': 'stop',
- }
- ],
- 'usage': {
- 'prompt_tokens': None,
- 'completion_tokens': None,
- 'total_tokens': None,
- },
- }
-
- def streaming():
- for chunk in response:
- completion_data = {
- 'id': f'chatcmpl-{completion_id}',
- 'object': 'chat.completion.chunk',
- 'created': completion_timestamp,
- 'model': model,
- 'choices': [
- {
- 'index': 0,
- 'delta': {
- 'content': chunk,
- },
- 'finish_reason': None,
- }
- ],
- }
-
- content = json.dumps(completion_data, separators=(',', ':'))
- yield f'data: {content}\n\n'
- time.sleep(0.1)
-
- end_completion_data: dict[str, Any] = {
- 'id': f'chatcmpl-{completion_id}',
- 'object': 'chat.completion.chunk',
- 'created': completion_timestamp,
- 'model': model,
- 'choices': [
- {
- 'index': 0,
- 'delta': {},
- 'finish_reason': 'stop',
- }
- ],
- }
- content = json.dumps(end_completion_data, separators=(',', ':'))
- yield f'data: {content}\n\n'
-
- return app.response_class(streaming(), mimetype='text/event-stream')
-
-
-# Get the embedding from huggingface
-def get_embedding(input_text, token):
- huggingface_token = token
- embedding_model = 'sentence-transformers/all-mpnet-base-v2'
- max_token_length = 500
-
- # Load the tokenizer for the 'all-mpnet-base-v2' model
- tokenizer = AutoTokenizer.from_pretrained(embedding_model)
- # Tokenize the text and split the tokens into chunks of 500 tokens each
- tokens = tokenizer.tokenize(input_text)
- token_chunks = [tokens[i:i + max_token_length]
- for i in range(0, len(tokens), max_token_length)]
-
- # Initialize an empty list
- embeddings = []
-
- # Create embeddings for each chunk
- for chunk in token_chunks:
- # Convert the chunk tokens back to text
- chunk_text = tokenizer.convert_tokens_to_string(chunk)
-
- # Use the Hugging Face API to get embeddings for the chunk
- api_url = f'https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}'
- headers = {'Authorization': f'Bearer {huggingface_token}'}
- chunk_text = chunk_text.replace('\n', ' ')
-
- # Make a POST request to get the chunk's embedding
- response = requests.post(api_url, headers=headers, json={
- 'inputs': chunk_text, 'options': {'wait_for_model': True}})
-
- # Parse the response and extract the embedding
- chunk_embedding = response.json()
- # Append the embedding to the list
- embeddings.append(chunk_embedding)
-
- # averaging all the embeddings
- # this isn't very effective
- # someone a better idea?
- num_embeddings = len(embeddings)
- average_embedding = [sum(x) / num_embeddings for x in zip(*embeddings)]
- embedding = average_embedding
- return embedding
-
-
-@app.route('/embeddings', methods=['POST'])
-def embeddings():
- input_text_list = request.get_json().get('input')
- input_text = ' '.join(map(str, input_text_list))
- token = request.headers.get('Authorization').replace('Bearer ', '')
- embedding = get_embedding(input_text, token)
-
- return {
- 'data': [
- {
- 'embedding': embedding,
- 'index': 0,
- 'object': 'embedding'
- }
- ],
- 'model': 'text-embedding-ada-002',
- 'object': 'list',
- 'usage': {
- 'prompt_tokens': None,
- 'total_tokens': None
- }
- }
-
-def main():
- app.run(host='0.0.0.0', port=1337, debug=True)
-
-if __name__ == '__main__':
- main() \ No newline at end of file