summaryrefslogtreecommitdiffstats
path: root/interference
diff options
context:
space:
mode:
Diffstat (limited to '')
-rw-r--r--interference/app.py169
1 files changed, 86 insertions, 83 deletions
diff --git a/interference/app.py b/interference/app.py
index f25785f6..5abbcff2 100644
--- a/interference/app.py
+++ b/interference/app.py
@@ -1,104 +1,106 @@
import json
+import time
import random
import string
-import time
-from typing import Any
import requests
-from flask import Flask, request
-from flask_cors import CORS
+
+from typing import Any
+from flask import Flask, request
+from flask_cors import CORS
from transformers import AutoTokenizer
-from g4f import ChatCompletion
+from g4f import ChatCompletion
app = Flask(__name__)
CORS(app)
-
-@app.route("/chat/completions", methods=["POST"])
+@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")
+ 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)
+ response = ChatCompletion.create(model = model,
+ stream = stream, messages = messages)
- completion_id = "".join(random.choices(string.ascii_letters + string.digits, k=28))
+ 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": [
+ 'id': f'chatcmpl-{completion_id}',
+ 'object': 'chat.completion',
+ 'created': completion_timestamp,
+ 'model': model,
+ 'choices': [
{
- "index": 0,
- "message": {
- "role": "assistant",
- "content": response,
+ 'index': 0,
+ 'message': {
+ 'role': 'assistant',
+ 'content': response,
},
- "finish_reason": "stop",
+ 'finish_reason': 'stop',
}
],
- "usage": {
- "prompt_tokens": None,
- "completion_tokens": None,
- "total_tokens": None,
+ '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": [
+ 'id': f'chatcmpl-{completion_id}',
+ 'object': 'chat.completion.chunk',
+ 'created': completion_timestamp,
+ 'model': model,
+ 'choices': [
{
- "index": 0,
- "delta": {
- "content": chunk,
+ 'index': 0,
+ 'delta': {
+ 'content': chunk,
},
- "finish_reason": None,
+ 'finish_reason': None,
}
],
}
- content = json.dumps(completion_data, separators=(",", ":"))
- yield f"data: {content}\n\n"
+ 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": [
+ 'id': f'chatcmpl-{completion_id}',
+ 'object': 'chat.completion.chunk',
+ 'created': completion_timestamp,
+ 'model': model,
+ 'choices': [
{
- "index": 0,
- "delta": {},
- "finish_reason": "stop",
+ 'index': 0,
+ 'delta': {},
+ 'finish_reason': 'stop',
}
],
}
- content = json.dumps(end_completion_data, separators=(",", ":"))
- yield f"data: {content}\n\n"
+ content = json.dumps(end_completion_data, separators=(',', ':'))
+ yield f'data: {content}\n\n'
- return app.response_class(streaming(), mimetype="text/event-stream")
+ return app.response_class(streaming(), mimetype='text/event-stream')
-#Get the embedding from huggingface
+# Get the embedding from huggingface
def get_embedding(input_text, token):
huggingface_token = token
- embedding_model = "sentence-transformers/all-mpnet-base-v2"
+ embedding_model = 'sentence-transformers/all-mpnet-base-v2'
max_token_length = 500
- # Load the tokenizer for the "all-mpnet-base-v2" model
+ # 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)]
+ token_chunks = [tokens[i:i + max_token_length]
+ for i in range(0, len(tokens), max_token_length)]
# Initialize an empty list
embeddings = []
@@ -109,52 +111,53 @@ def get_embedding(input_text, token):
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", " ")
-
+ 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}})
-
+ 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?
+ # 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"])
+@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)
+ 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
- }
- }
+ '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)
-
+ app.run(host='0.0.0.0', port=1337, debug=True)
-if __name__ == "__main__":
+if __name__ == '__main__':
main() \ No newline at end of file