Add environment files and cleanup

.gitignore

@@ -0,0 +1,21 @@
+# Python artifacts
+__pycache__/
+*.py[cod]
+*.egg-info/
+
+# Environments
+venv/
+.venv/
+ENV/
+env/
+
+# dotenv files
+*.env
+
+# Output files
+*.docx
+*.html
+
+# Node
+node_modules/
+

README.md

@@ -3,6 +3,15 @@
 This repository contains small Python utilities for interacting with AI models.
 All scripts are located in the `scripts/` directory.
 
+## Setup
+
+Create the conda environment and install the required Python packages:
+
+```bash
+conda env create -f environment.yml
+conda activate myscripts
+```
+
 ## Available Scripts
 
 - **claude_call.py** – Query Anthropic's Claude model and save results to a DOCX
@@ -14,3 +23,7 @@ All scripts are located in the `scripts/` directory.
   outputs.
 
 See [scripts/README.md](scripts/README.md) for more details on each script.
+
+The repository also includes a simple monitoring demo located in
+`monitoring-app/`. Consult its README for instructions on running the FastAPI
+backend and React frontend.

environment.yml

@@ -0,0 +1,22 @@
+name: myscripts
+channels:
+  - conda-forge
+dependencies:
+  - python=3.10
+  - pip
+  - pip:
+      - python-docx
+      - markdown2
+      - tqdm
+      - anthropic
+      - groq
+      - faster-whisper
+      - ollama
+      - langchain-community
+      - psutil
+      - pyjwt
+      - requests
+      - fastapi
+      - uvicorn
+      - websockets
+      - python-dotenv

monitoring-app/README.md

@@ -12,13 +12,12 @@ This project provides a minimal FastAPI backend and React-based frontend for rea
 
 ### Running
 
-Install dependencies and start:
+Create the conda environment from the repository root and start the server:
 
 ```bash
+conda env create -f ../../environment.yml  # run once
+conda activate myscripts
 cd backend
-python3 -m venv venv
-. venv/bin/activate
-pip install -r requirements.txt
 uvicorn main:app --reload
 ```
 
@@ -28,7 +27,8 @@ uvicorn main:app --reload
 
 ## Security Notes
 
-- Change `SECRET_KEY`, `ADMIN_USER`, and `ADMIN_PASS` in `.env`.
+- Copy `backend/.env.example` to `backend/.env` and change `SECRET_KEY`,
+  `ADMIN_USER`, and `ADMIN_PASS`.
 - Use HTTPS termination in production.
 
 This is a starting point for a full-featured monitoring solution.

scripts/README.md

@@ -4,6 +4,12 @@ This directory contains small utility scripts for working with AI models.
 Each script is saved with a descriptive name and `.py` extension so they can be
 run using Python.
 
+The API-based scripts expect the relevant keys to be provided as environment
+variables:
+
+- `ANTHROPIC_API_KEY` for `claude_call.py`
+- `GROQ_API_KEY` for the Groq examples
+
 ## Scripts
 
 ### `claude_call.py`

scripts/claude_call.py

@@ -18,7 +18,7 @@ def generate_messages(section: str) -> List[dict]:
         }
     ]
 
-client: anthropic.Anthropic = anthropic.Anthropic(api_key=)
+client: anthropic.Anthropic = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY", ""))
 
 print("Generating policy outline...")
 
@@ -60,10 +60,10 @@ def generate_messages(section: str) -> List[dict]:
     word_document.add_paragraph("\n")
     html_sections.append(markdown(detailed_info))
 
-docx_file_path = "e.docx"
+docx_file_path = "output.docx"
 with open(docx_file_path, "wb") as doc_file:
     word_document.save(doc_file)
 
-html_file_path = ".html"
+html_file_path = "output.html"
 with open(html_file_path, "w") as html_file:
     html_file.write("\n".join(html_sections))

scripts/groq_simple_chatbot.py

@@ -1,15 +1,15 @@
 from groq import Groq
+import os
 
-
-# Initialize the Groq API
-client= Groq(api_key="<key-here>")
+# Initialize the Groq API using an environment variable
+client = Groq(api_key=os.getenv("GROQ_API_KEY", ""))
 
 # Initialize the conversation list
 conversation = [
     {"role": "system", "content": "You are a helpful assistant."},
 ]
 
-# Function to interact with GPT-3
+# Function to interact with the Groq model
 def chat_with_groq(prompt, conversation):
     model_engine = "mixtral-8x7b-32768"
 

scripts/read_mp3_summary.py

@@ -1,19 +1,15 @@
 from faster_whisper import WhisperModel
 from groq import Groq
+import os
 
-def open_file(filepath):
-    with open(filepath, 'r', encoding='utf-8') as infile:
-        return infile.read()
-
-API_KEY = open_file('groqapikey.txt')
-client = Groq(api_key=API_KEY)
+client = Groq(api_key=os.getenv("GROQ_API_KEY", ""))
 
 def transcribe_audio(audio_path, model_size="medium"):
     model = WhisperModel(model_size, device="cpu")
     segments, _ = model.transcribe(audio_path)
     return " ".join([segment.text for segment in segments])
 
-audio_file_path = "/path/to/your/mp3"  # Replace with your audio file path
+audio_file_path = os.getenv("AUDIO_FILE", "/path/to/your/mp3")
 transcribed_text = transcribe_audio(audio_file_path)
 with open("transcription.txt", "w") as file:
     file.write(transcribed_text)