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Diffstat (limited to 'etc/interference/app.py')
| -rw-r--r-- | etc/interference/app.py | 163 |
1 files changed, 163 insertions, 0 deletions
diff --git a/etc/interference/app.py b/etc/interference/app.py new file mode 100644 index 00000000..5abbcff2 --- /dev/null +++ b/etc/interference/app.py @@ -0,0 +1,163 @@ +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()
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