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