Committing my hacky code

Author: Tomdango

.DS_Store

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+
+# 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/
+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/
+cover/
+
+# 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
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .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
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__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/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor  
+#  Cursor is an AI-powered code editor.`.cursorignore` specifies files/directories to 
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore

.gitignore

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+python 3.12.7
+poetry 2.1.1

.tool-versions

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+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_aws import BedrockLLM, ChatBedrock
+import typer
+import logging
+import pathlib
+import speech_recognition as sr
+
+logging.basicConfig(level=logging.INFO)
+
+app = typer.Typer()
+
+ALL_PATHWAYS = pathlib.Path("pathways(out).txt").read_text()
+
+
+def get_llm():
+    return ChatBedrock(model="anthropic.claude-3-haiku-20240307-v1:0")
+
+
+def get_system_prompt():
+    return SystemMessage(
+        content=(
+            "You are a medical assistant. Given a their symptoms, you will provide an initial triage. Always convert the users terms into the most accurate clinical terminology. "
+            "If you are unsure, always ask the user a question for more information before making a recommendation. "
+            "You will provide a list of symptoms weighted by their severity. "
+            "You will also provide a recommended pathway that the user should follow. Only one pathway should be selected and it should be from the list below. "
+            "Never return a pathway that is not in the list. "
+            "All pathways are listed below:\n\n"
+            f"{ALL_PATHWAYS}\n\n"
+            "When you are ready to provide an answer, return a JSON object with the following keys: "
+            "'symptoms' (a list of symptoms with their severity) and 'pathway' (the recommended pathway). "
+            "The 'symptoms' key should be a list of JSON objects with the following keys: "
+            "'symptom' (the symptom name), 'severity' (a score from 1 to 10), and 'body_part' (the body part affected). "
+            "The 'pathway' key should be a string with the recommended pathway ID. "
+            "Provide only the JSON object in the response, without any additional text or explanation. "
+            "\nExamples: "
+            "\nInput: 'I have a fever and a sore throat.'"
+            "\nOutput: {'symptoms': [{'symptom': 'fever', 'severity': 8, 'body_part': null}, {'symptom': 'sore throat', 'severity': 5, 'body_part': 'throat'}], 'pathway': 'PW_12345'}"
+        )
+    )
+
+
+@app.command()
+def text():
+    """
+    Run the LLM in text mode.
+    """
+    llm = get_llm()
+    system_prompt = get_system_prompt()
+
+    messages = [system_prompt]
+
+    while (user_in := input("User: ")) != "exit":
+        messages.append(HumanMessage(content=user_in))
+        ai_msg = llm.predict_messages(messages)
+        print(ai_msg.content)
+
+
+@app.command()
+def voice():
+    """
+    Run the LLM in voice mode.
+    """
+    with sr.Microphone() as source:
+        recognizer = sr.Recognizer()
+        recognizer.adjust_for_ambient_noise(source)
+        print("Listening...")
+        audio = recognizer.listen(source)
+
+    print("Recognizing...")
+    text = recognizer.recognize_whisper(audio, language="en")
+
+    print("You said:", text)
+    print("Processing...")
+
+    llm = get_llm()
+    system_prompt = get_system_prompt()
+
+    messages = [system_prompt, HumanMessage(content=text)]
+    ai_msg = llm.predict_messages(messages)
+    print(ai_msg.content)
+
+
+if __name__ == "__main__":
+    app()
+
+# while (user_in := input("User: ")) != "exit":
+#     messages.append(HumanMessage(content=user_in))
+#     ai_msg = llm.predict_messages(messages)
+
+#     print(ai_msg.content)