~ | new folder inluding `./tool`and `./testing`

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()

etc/interference/requirements.txt

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+flask_cors
+watchdog~=3.0.0
+transformers
+tensorflow
+torch