load model

.gitignore

@@ -0,0 +1,137 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSC config
+.vscode/
+
+# JetBrains config
+.idea/
+
+venv/

ice_breaking_challenge/__init__.py

@@ -21,8 +21,10 @@ def create_app(test_config=None):
 
     # model loading
     # Kaggle API Key 환경 변수 설정
-    kaggle_username = os.getenv("KAGGLE_USERNAME")
-    kaggle_key = os.getenv("KAGGLE_KEY")
+    # kaggle_username = os.getenv("KAGGLE_USERNAME")
+    kaggle_username = "yunzi7"
+    # kaggle_key = os.getenv("KAGGLE_KEY")
+    kaggle_key = "366492cccaf65572878ce8f77000e97a"
     os.environ['KAGGLE_USERNAME'] = kaggle_username
     os.environ['KAGGLE_KEY'] = kaggle_key
     model = load_model_with_lora()

ice_breaking_challenge/models/gemma2_it_2b_icebreaking.lora.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d054362a9d0ac80b58fb79e0de00ceadea5dd980f01420dc503329c5d3690fd
+size 23576536

ice_breaking_challenge/models/model_loader.py

@@ -1,7 +1,7 @@
 import keras_nlp
 
 MODEL_NAME = "gemma2_instruct_2b_en"
-LORA_WEIGHT_PATH = "weights/gemma2_it_2b_icebreaking.lora.h5"
+LORA_WEIGHT_PATH = "ice_breaking_challenge/models/gemma2_it_2b_icebreaking.lora.h5"
 
 def load_model_with_lora(model_name:str = MODEL_NAME, lora_weight_path: str = LORA_WEIGHT_PATH):
     """
@@ -16,6 +16,42 @@ def load_model_with_lora(model_name:str = MODEL_NAME, lora_weight_path: str = LO
     """
     model = keras_nlp.models.GemmaCausalLM.from_preset(model_name)
 
+
     model.backbone.load_lora_weights(lora_weight_path)
+    question_crawling="나의 이런 점은 일할 때 도움이 돼!?"
+    answer_crawling="나누고 싶어하는 마음? 주변 사람들은 그만 퍼주라고 하기도 하지만, 내가 클라이언트로부터 돈을 벌고자 하는 것이 아니라 조금이라도 더 챙겨주고자 하는 마음을 가졌을 때 결국 나의 브랜드가 훨씬 더 커질 수 있다는 믿음이 있다."
+
+    input_text = f"{question_crawling} {answer_crawling}"
+    
+    print(model.generate(input_text, max_length=512))
 
     return model
+
+
+# def template_setting(df:pd.DataFrame, is_test:bool) -> np.ndarray:
+#     template_input="""
+#     <instruction>
+#     Using the text: {question_crawling} {answer_crawling}, create a new multiple-choice question with 4 answer options.
+#     """
+    
+#     template_output="""
+#     <Response>
+#     {question_generated}
+#     {multiple_choice_generated}
+#     {answer_generated}
+#     """
+#     template=template_input+'\n'+template_output
+    
+#     inputs = np.array(df.apply(lambda row: template.format(
+#                         question_crawling=row['question_crawling'],
+#                         answer_crawling=row['answer_crawling'],
+#                         question_generated=row['question_generated'] if not is_test else "",
+#                         multiple_choice_generated=row['multiple_choice_generated'] if not is_test else "",
+#                         answer_generated=row['answer_generated'] if not is_test else "").strip(), axis=1))
+    
+#     outputs = np.array(df.apply(lambda row: template_output.format(
+#                         question_generated=row['question_generated'],
+#                         multiple_choice_generated=row['multiple_choice_generated'],
+#                         answer_generated=row['answer_generated']).strip(), axis=1))
+#     combined_array = np.column_stack((inputs, outputs))
+#     return combined_array

ice_breaking_challenge/qr.py

@@ -21,5 +21,4 @@ def qr() -> None:
         case "GET":
             return render_template("qr.html")
         case "POST":   # qr.html에서 `다음` 버튼 눌렀을 때
-            print("POST---------------------------------------")
             return render_template("introduction.html")

requirements.txt

@@ -1,3 +1,6 @@
 flask
 Flask-Session
-keras-nlp
+tensorflow
+keras
+keras-nlp
+tensorflow-text