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authorCommenter123321 <36051603+Commenter123321@users.noreply.github.com>2023-10-09 18:02:06 +0200
committerCommenter123321 <36051603+Commenter123321@users.noreply.github.com>2023-10-09 18:02:06 +0200
commit119817c96349807efaf87ee432ce46446542b66a (patch)
tree1dbdf4d4dbf4f6c8a8247274ef500a2f1de765d1 /etc/interference/app.py
parentaivvm's no life creator keeps patching it, but I'm just better 😉 (diff)
parentMerge branch 'main' of https://github.com/xtekky/gpt4free (diff)
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Diffstat (limited to 'etc/interference/app.py')
-rw-r--r--etc/interference/app.py163
1 files changed, 163 insertions, 0 deletions
diff --git a/etc/interference/app.py b/etc/interference/app.py
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+++ b/etc/interference/app.py
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+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