1 files changed, 6 insertions, 75 deletions

diff --git a/interference/app.py b/g4f/interference/__init__.py
index 5abbcff2..d756faa7 100644
--- a/interference/app.py
+++ b/g4f/interference/__init__.py
@@ -2,17 +2,19 @@ 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('/')
+def index():
+    return 'interference api, url: http://127.0.0.1:1337'
+
 @app.route('/chat/completions', methods=['POST'])
 def chat_completions():
     model    = request.get_json().get('model', 'gpt-3.5-turbo')
@@ -88,76 +90,5 @@ def chat_completions():
 
     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
+def run_interference():
+    app.run(host='0.0.0.0', port=1337, debug=True)
\ No newline at end of file