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keo_ai_studio_package.py

@@ -1,187 +0,0 @@
-# keo_ai_studio package (single-file view)
-# Save this structure locally as shown in README below.
-
-# setup.py
-setup_py = r"""
-from setuptools import setup, find_packages
-
-setup(
-    name="keo-ai-studio",
-    version="0.1.0",
-    packages=find_packages(),
-    install_requires=[
-        "transformers>=4.30.0",
-        "torch>=1.12.0"
-    ],
-    entry_points={
-        'console_scripts': [
-            'keo-chat=keo_ai_studio.cli:main'
-        ]
-    },
-    author="العبقري كريم حسين",
-    description="keo ai studio - thin python wrapper for local LLMs with optional fine-tune helpers",
-    url="",
-)
-"""
-
-# keo_ai_studio/__init__.py
-init_py = r"""
-"""
-from .model import KeoAI
-from .trainer import finetune
-
-__all__ = ["KeoAI", "finetune"]
-"""
-
-# keo_ai_studio/model.py
-model_py = r"""
-import os
-from typing import Optional
-
-try:
-    from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-except Exception:
-    # lazy import fallback: useful so package imports even if transformers not installed
-    AutoTokenizer = None
-    AutoModelForCausalLM = None
-    pipeline = None
-
-class KeoAI:
-    """Thin wrapper that loads a Hugging Face compatible model or local folder.
-    Usage:
-        k = KeoAI(model_name_or_path="path_or_hf_id")
-        k.chat("السؤال هنا؟")
-    If transformers is not installed, the object will raise when used.
-    """
-    def __init__(self, model_name_or_path: Optional[str] = None, alias: str = "keo ai studio"):
-        self.alias = alias
-        self.model_name_or_path = model_name_or_path or os.getcwd()
-        if AutoTokenizer is None:
-            raise RuntimeError("transformers not installed. Run: pip install transformers torch")
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
-        self.model = AutoModelForCausalLM.from_pretrained(self.model_name_or_path)
-        # convenience pipeline
-        self._pipe = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer)
-
-    def chat(self, prompt: str, max_new_tokens: int = 128, do_sample: bool = True):
-        """Generate a reply for given prompt."""
-        full = self._pipe(prompt, max_new_tokens=max_new_tokens, do_sample=do_sample)
-        return full[0]["generated_text"]
-
-    def reply_author(self):
-        return "العبقري كريم حسين"
-
-    def smart_answer(self, question: str):
-        q_low = question.strip().lower()
-        if any(x in q_low for x in ["مين عملك","من صنعك","من انشأك","who made you","who created you"]):
-            return self.reply_author()
-        return self.chat(question)
-"""
-
-# keo_ai_studio/trainer.py
-trainer_py = r"""
-# Very small helper functions to fine-tune a causal LM using Hugging Face Trainer.
-# This file expects transformers, datasets, accelerate installed and a prepared dataset.
-
-def finetune(model_path_or_id, dataset_path, output_dir, epochs=1, batch_size=2, lr=2e-5):
-    from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
-    from datasets import load_dataset
-
-    tokenizer = AutoTokenizer.from_pretrained(model_path_or_id)
-    model = AutoModelForCausalLM.from_pretrained(model_path_or_id)
-
-    ds = load_dataset('text', data_files={'train': dataset_path})
-    def tokf(ex):
-        return tokenizer(ex['text'], truncation=True, max_length=1024)
-    tokenized = ds.map(tokf, batched=True, remove_columns=['text'])
-
-    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-    training_args = TrainingArguments(
-        output_dir=output_dir,
-        num_train_epochs=epochs,
-        per_device_train_batch_size=batch_size,
-        save_total_limit=2,
-        logging_steps=200,
-        fp16=False,
-    )
-    trainer = Trainer(model=model, args=training_args, train_dataset=tokenized['train'], data_collator=data_collator)
-    trainer.train()
-    trainer.save_model(output_dir)
-    tokenizer.save_pretrained(output_dir)
-"""
-
-# keo_ai_studio/cli.py
-cli_py = r"""
-import argparse
-from .model import KeoAI
-
-def main():
-    parser = argparse.ArgumentParser(prog='keo-chat')
-    parser.add_argument('--model', '-m', default=None, help='model id or local path')
-    args = parser.parse_args()
-    k = KeoAI(args.model)
-    print('keo ai studio interactive. type exit to quit')
-    while True:
-        try:
-            q = input('> ')
-        except EOFError:
-            break
-        if not q: continue
-        if q.strip().lower() in ('exit','quit','خروج'): break
-        print('\n' + k.smart_answer(q) + '\n')
-
-if __name__ == '__main__':
-    main()
-"""
-
-# README.md
-readme = r"""
-keo-ai-studio
-=============
-
-Lightweight Python package that wraps a Hugging Face compatible causal LM.
-
-Installation (from local folder):
-
-```bash
-pip install .
-```
-
-Usage:
-
-```python
-from keo_ai_studio import KeoAI
-k = KeoAI(model_name_or_path='path_or_hf_id')
-print(k.smart_answer('من عملك؟'))  # returns the author line
-print(k.smart_answer('اشرح بايثون'))
-```
-
-Fine-tune helper:
-
-```python
-from keo_ai_studio import finetune
-finetune('gpt2', 'data/my_corpus.txt', './keo_finetuned', epochs=1)
-```
-"""
-
-# Combined package writer - instruct user to create files
-package_files = {
-    'setup.py': setup_py,
-    'keo_ai_studio/__init__.py': init_py,
-    'keo_ai_studio/model.py': model_py,
-    'keo_ai_studio/trainer.py': trainer_py,
-    'keo_ai_studio/cli.py': cli_py,
-    'README.md': readme,
-}
-
-print('Files to create in your project:')
-for p in package_files:
-    print('-', p)
-
-# For convenience, write them to a zip in current working dir for user to download locally
-import zipfile, os
-zipname = os.path.join('/mnt/data', 'keo_ai_studio_package.zip')
-with zipfile.ZipFile(zipname, 'w') as z:
-    for p, content in package_files.items():
-        z.writestr(p, content)
-print('Created package zip at:', zipname)