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