diff options
Diffstat (limited to '')
| -rw-r--r-- | g4f/interference/__init__.py (renamed from interference/app.py) | 81 |
1 files changed, 6 insertions, 75 deletions
diff --git a/interference/app.py b/g4f/interference/__init__.py index 5abbcff2..d756faa7 100644 --- a/interference/app.py +++ b/g4f/interference/__init__.py @@ -2,17 +2,19 @@ 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('/') +def index(): + return 'interference api, url: http://127.0.0.1:1337' + @app.route('/chat/completions', methods=['POST']) def chat_completions(): model = request.get_json().get('model', 'gpt-3.5-turbo') @@ -88,76 +90,5 @@ def chat_completions(): 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 +def run_interference(): + app.run(host='0.0.0.0', port=1337, debug=True)
\ No newline at end of file |