Merge pull request #1 from jmorganca/refactor

refactor

ollama.py

-import json
-import os
-import threading
-import click
-from tqdm import tqdm
-from pathlib import Path
-from llama_cpp import Llama
-from flask import Flask, Response, stream_with_context, request
-from flask_cors import CORS
-from template import template
-
-app = Flask(__name__)
-CORS(app)  # enable CORS for all routes
-
-# llms tracks which models are loaded
-llms = {}
-lock = threading.Lock()
-
-
-def models_directory():
-    home_dir = Path.home()
-    models_dir = home_dir / ".ollama/models"
-
-    if not models_dir.exists():
-        models_dir.mkdir(parents=True)
-
-    return models_dir
-
-
-def load(model):
-    """
-    Load a model.
-
-    Args:
-    model (str): The name or path of the model to load.
-
-    Returns:
-    str or None: The name of the model
-    dict or None: If the model cannot be loaded, a dictionary with an 'error' key is returned.
-                  If the model is successfully loaded, None is returned.
-    """
-
-    with lock:
-        load_from = ""
-        if os.path.exists(model) and model.endswith(".bin"):
-            # model is being referenced by path rather than name directly
-            path = os.path.abspath(model)
-            base = os.path.basename(path)
-
-            load_from = path
-            name = os.path.splitext(base)[0]  # Split the filename and extension
-        else:
-            # model is being loaded from the ollama models directory
-            dir = models_directory()
-
-            # TODO: download model from a repository if it does not exist
-            load_from = str(dir / f"{model}.bin")
-            name = model
-
-        if load_from == "":
-            return None, {"error": "Model not found."}
-
-        if not os.path.exists(load_from):
-            return None, {"error": f"The model {load_from} does not exist."}
-
-        if name not in llms:
-            llms[name] = Llama(model_path=load_from)
-
-    return name, None
-
-
-def unload(model):
-    """
-    Unload a model.
-
-    Remove a model from the list of loaded models. If the model is not loaded, this is a no-op.
-
-    Args:
-    model (str): The name of the model to unload.
-    """
-    llms.pop(model, None)
-
-
-def generate(model, prompt):
-    # auto load
-    name, error = load(model)
-    if error is not None:
-        return error
-    generated = llms[name](
-        str(prompt),  # TODO: optimize prompt based on model
-        max_tokens=4096,
-        stop=["Q:", "\n"],
-        stream=True,
-    )
-    for output in generated:
-        yield json.dumps(output)
-
-
-def models():
-    dir = models_directory()
-    all_files = os.listdir(dir)
-    bin_files = [
-        file.replace(".bin", "") for file in all_files if file.endswith(".bin")
-    ]
-    return bin_files
-
-
-@app.route("/load", methods=["POST"])
-def load_route_handler():
-    data = request.get_json()
-    model = data.get("model")
-    if not model:
-        return Response("Model is required", status=400)
-    error = load(model)
-    if error is not None:
-        return error
-    return Response(status=204)
-
-
-@app.route("/unload", methods=["POST"])
-def unload_route_handler():
-    data = request.get_json()
-    model = data.get("model")
-    if not model:
-        return Response("Model is required", status=400)
-    unload(model)
-    return Response(status=204)
-
-
-@app.route("/generate", methods=["POST"])
-def generate_route_handler():
-    data = request.get_json()
-    model = data.get("model")
-    prompt = data.get("prompt")
-    prompt = template(model, prompt)
-    if not model:
-        return Response("Model is required", status=400)
-    if not prompt:
-        return Response("Prompt is required", status=400)
-    if not os.path.exists(f"{model}"):
-        return {"error": "The model does not exist."}, 400
-    return Response(
-        stream_with_context(generate(model, prompt)), mimetype="text/event-stream"
-    )
-
-
-@app.route("/models", methods=["GET"])
-def models_route_handler():
-    bin_files = models()
-    return Response(json.dumps(bin_files), mimetype="application/json")
-
-
-@click.group(invoke_without_command=True)
-@click.pass_context
-def cli(ctx):
-    # allows the script to respond to command line input when executed directly
-    if ctx.invoked_subcommand is None:
-        click.echo(ctx.get_help())
-
-
-@cli.command()
-@click.option("--port", default=7734, help="Port to run the server on")
-@click.option("--debug", default=False, help="Enable debug mode")
-def serve(port, debug):
-    print("Serving on http://localhost:{port}")
-    app.run(host="0.0.0.0", port=port, debug=debug)
-
-
-@cli.command(name="load")
-@click.argument("model")
-@click.option("--file", default=False, help="Indicates that a file path is provided")
-def load_cli(model, file):
-    if file:
-        error = load(path=model)
-    else:
-        error = load(model)
-    if error is not None:
-        print(error)
-        return
-    print("Model loaded")
-
-
-@cli.command(name="generate")
-@click.argument("model")
-@click.option("--prompt", default="", help="The prompt for the model")
-def generate_cli(model, prompt):
-    if prompt == "":
-        prompt = input("Prompt: ")
-    output = ""
-    prompt = template(model, prompt)
-    for generated in generate(model, prompt):
-        generated_json = json.loads(generated)
-        text = generated_json["choices"][0]["text"]
-        output += text
-        print(f"\r{output}", end="", flush=True)
-
-
-@cli.command(name="models")
-def models_cli():
-    print(models())
-
-
-@cli.command(name="pull")
-@click.argument("model")
-def pull_cli(model):
-    print("not implemented")
-
-
-@cli.command(name="import")
-@click.argument("model")
-def import_cli(model):
-    print("not implemented")
-
-
-if __name__ == "__main__":
-    cli()
+from ollama.cmd.cli import main
+if __name__ == '__main__':
+    main()

ollama/__init__.py

+from ollama.model import models
+from ollama.engine import generate, load, unload
+
+__all__ = [
+    'models',
+    'generate',
+    'load',
+    'unload',
+]

ollama/cmd/cli.py

+import json
+from pathlib import Path
+from argparse import ArgumentParser
+
+from ollama import model, engine
+from ollama.cmd import server
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('--models-home', default=Path.home() / '.ollama' / 'models')
+
+    subparsers = parser.add_subparsers()
+
+    server.set_parser(subparsers.add_parser('serve'))
+
+    list_parser = subparsers.add_parser('list')
+    list_parser.set_defaults(fn=list)
+
+    generate_parser = subparsers.add_parser('generate')
+    generate_parser.add_argument('model')
+    generate_parser.add_argument('prompt')
+    generate_parser.set_defaults(fn=generate)
+
+    args = parser.parse_args()
+    args = vars(args)
+
+    fn = args.pop('fn')
+    fn(**args)
+
+
+def list(*args, **kwargs):
+    for m in model.models(*args, **kwargs):
+        print(m)
+
+
+def generate(*args, **kwargs):
+    for output in engine.generate(*args, **kwargs):
+        output = json.loads(output)
+
+        choices = output.get('choices', [])
+        if len(choices) > 0:
+            print(choices[0].get('text', ''), end='')

ollama/cmd/server.py

+from aiohttp import web
+
+from ollama import engine
+
+
+def set_parser(parser):
+    parser.add_argument('--host', default='127.0.0.1')
+    parser.add_argument('--port', default=7734)
+    parser.set_defaults(fn=serve)
+
+
+def serve(models_home='.', *args, **kwargs):
+    app = web.Application()
+    app.add_routes([
+        web.post('/load', load),
+        web.post('/unload', unload),
+        web.post('/generate', generate),
+    ])
+
+    app.update({
+        'llms': {},
+        'models_home': models_home,
+    })
+
+    web.run_app(app, **kwargs)
+
+
+async def load(request):
+    body = await request.json()
+    model = body.get('model')
+    if not model:
+        raise web.HTTPBadRequest()
+
+    kwargs = {
+        'llms': request.app.get('llms'),
+        'models_home': request.app.get('models_home'),
+    }
+
+    engine.load(model, **kwargs)
+    return web.Response()
+
+
+async def unload(request):
+    body = await request.json()
+    model = body.get('model')
+    if not model:
+        raise web.HTTPBadRequest()
+
+    engine.unload(model, llms=request.app.get('llms'))
+    return web.Response()
+
+
+async def generate(request):
+    body = await request.json()
+    model = body.get('model')
+    if not model:
+        raise web.HTTPBadRequest()
+
+    prompt = body.get('prompt')
+    if not prompt:
+        raise web.HTTPBadRequest()
+
+    response = web.StreamResponse()
+    await response.prepare(request)
+
+    kwargs = {
+        'llms': request.app.get('llms'),
+        'models_home': request.app.get('models_home'),
+    }
+
+    for output in engine.generate(model, prompt, **kwargs):
+        await response.write(output.encode('utf-8'))
+        await response.write(b'\n')
+
+    return response

ollama/engine.py

+import os
+import json
+import sys
+from contextlib import contextmanager
+from llama_cpp import Llama as LLM
+
+import ollama.model
+
+
+@contextmanager
+def suppress_stderr():
+    stderr = os.dup(sys.stderr.fileno())
+    with open(os.devnull, 'w') as devnull:
+        os.dup2(devnull.fileno(), sys.stderr.fileno())
+        yield
+
+    os.dup2(stderr, sys.stderr.fileno())
+
+
+def generate(model, prompt, models_home='.', llms={}, *args, **kwargs):
+    llm = load(model, models_home=models_home, llms=llms)
+
+    if 'max_tokens' not in kwargs:
+        kwargs.update({'max_tokens': 16384})
+
+    if 'stop' not in kwargs:
+        kwargs.update({'stop': ['Q:', '\n']})
+
+    if 'stream' not in kwargs:
+        kwargs.update({'stream': True})
+
+    for output in llm(prompt, *args, **kwargs):
+        yield json.dumps(output)
+
+
+def load(model, models_home='.', llms={}):
+    llm = llms.get(model, None)
+    if not llm:
+        model_path = {
+            name: path
+            for name, path in ollama.model.models(models_home)
+        }.get(model, None)
+
+        if model_path is None:
+            raise ValueError('Model not found')
+
+        # suppress LLM's output
+        with suppress_stderr():
+            llm = LLM(model_path, verbose=False)
+            llms.update({model: llm})
+
+    return llm
+
+
+def unload(model, llms={}):
+    if model in llms:
+        llms.pop(model)

ollama/model.py

+from os import walk, path
+
+
+def models(models_home='.', *args, **kwargs):
+    for root, _, files in walk(models_home):
+        for file in files:
+            base, ext = path.splitext(file)
+            if ext == '.bin':
+                yield base, path.join(root, file)