1 files changed, 162 insertions, 0 deletions

diff --git a/g4f/api/__init__.py b/g4f/api/__init__.py
new file mode 100644
index 00000000..c52085dc
--- /dev/null
+++ b/g4f/api/__init__.py
@@ -0,0 +1,162 @@
+import json
+import random
+import string
+import time
+
+import requests
+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")
+    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 = {
+            "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 run_api():
+    app.run(host="0.0.0.0", port=1337)