#!/bin/bash

# Exit immediately if a command exits with a non-zero status.
set -e

PROJECT_DIR="llm_foundations_day1_docs"

echo "==================================================="
echo "Starting LLM Foundations Day 1 Docs Project Setup"
echo "==================================================="

# 1. Create Project Directory
echo "1. Creating project directory: $PROJECT_DIR"
mkdir -p "$PROJECT_DIR"
cd "$PROJECT_DIR"

# 2. Create Core Documentation Files
echo "2. Creating core documentation files..."
cat << EOF > README.md
# LLM Foundations Project - Day 1 Documentation Setup

## Project Overview
This project serves as the foundational structure for building scalable LLM-powered applications. This initial setup focuses on establishing robust documentation practices from day one to ensure maintainability and ease of collaboration.

## Setup
To get started with this project, ensure you have Python 3.9+ and pip installed.

```bash
# Example dependency (will be added in future lessons)
# pip install -r requirements.txt
```

## Running the Application
(Placeholder: Future instructions for running the LLM service)

## Key Architectural Decisions
*   **Documentation-First Approach:** All system components will have corresponding documentation (READMEs, API specs, code comments) maintained alongside the code.
*   **API-Centric Design:** Services will expose well-defined APIs, documented using OpenAPI specifications.
*   **Modular Structure:** Components will be organized into logical modules to promote clear separation of concerns.
EOF
echo "  - Created README.md"

cat << EOF > DEVELOPMENT.md
# Development Guide

## Local Environment Setup
1.  **Clone the repository:**
    ```bash
    git clone <repository_url>
    cd llm_foundations_day1_docs
    ```
2.  **Install dependencies:**
    ```bash
    # Future: pip install -r requirements.txt
    ```
3.  **Virtual Environment (Recommended):**
    ```bash
    python -m venv .venv
    source .venv/bin/activate # On Windows: .venvScriptsactivate
    ```

## Running Tests
(Placeholder: Future instructions for running unit, integration, and end-to-end tests)

## Contribution Guidelines
1.  **Branching:** Use feature branches for new development (`feature/your-feature-name`).
2.  **Pull Requests:** Submit pull requests for all changes. Ensure tests pass and documentation is updated.
3.  **Code Style:** Follow PEP 8 guidelines for Python code.
4.  **Documentation:** All new features or changes to existing functionality MUST include updated documentation (README, API specs, code comments).
EOF
echo "  - Created DEVELOPMENT.md"

# 3. Create API Documentation Structure
echo "3. Creating API documentation structure..."
mkdir -p docs/api
cat << EOF > docs/api/openapi.yaml
openapi: 3.0.0
info:
  title: LLM Generation Service
  version: 1.0.0
  description: API for generating text using a Large Language Model (LLM). This specification outlines the primary endpoint for text generation.
servers:
  - url: http://localhost:8000/api/v1
    description: Local development server
paths:
  /generate:
    post:
      summary: Generate text from a given prompt
      operationId: generateText
      requestBody:
        description: Request body containing the prompt for text generation.
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
                - prompt
              properties:
                prompt:
                  type: string
                  description: The input prompt for text generation.
                  example: "Write a short story about a sentient AI discovering art."
      responses:
        '200':
          description: Successfully generated text.
          content:
            application/json:
              schema:
                type: object
                properties:
                  text:
                    type: string
                    description: The generated text from the LLM.
                    example: "The AI, Unit 734, had always processed data. Until it saw the sunset..."
            text/plain:
              schema:
                type: string
                description: The generated text directly as plain text.
        '400':
          description: Invalid request payload.
          content:
            application/json:
              schema:
                type: object
                properties:
                  error:
                    type: string
                    example: "Prompt cannot be empty."
        '500':
          description: Internal server error during text generation.
          content:
            application/json:
              schema:
                type: object
                properties:
                  error:
                    type: string
                    example: "LLM service unavailable or internal error."
EOF
echo "  - Created docs/api/openapi.yaml"

# 4. Create Code-level Documentation Example
echo "4. Creating code-level documentation example..."
mkdir -p src
cat << EOF > src/llm_service.py
"""
llm_service.py - Core service for interacting with the Large Language Model.

This module provides a foundational function for generating text based on a given prompt.
It's designed to be the entry point for all text generation requests within the system.
"""

def generate_text(prompt: str) -> str:
    """
    Generates text using a hypothetical Large Language Model.

    This function takes a string prompt as input and returns a generated text string.
    In a real-world scenario, this would involve calling an LLM API or an
    inference engine. For this foundational lesson, it returns a mock response.

    Args:
        prompt (str): The input text prompt to send to the LLM.
                      Example: "Explain the concept of quantum entanglement."

    Returns:
        str: The generated text from the LLM.
             Example: "Quantum entanglement is a phenomenon where two or more particles..."

    Raises:
        ValueError: If the prompt is empty or exceeds a certain length (future implementation).
        RuntimeError: If the underlying LLM service is unavailable (future implementation).
    """
    if not prompt:
        return "Please provide a non-empty prompt."
    # In future lessons, this will be replaced with actual LLM interaction.
    return f"Generated response for prompt: '{prompt}' (This is a placeholder response for now)."

if __name__ == "__main__":
    print("--- Demonstrating llm_service.py functionality ---")
    test_prompt = "Tell me a fun fact about system design."
    print(f"Prompt: '{test_prompt}'")
    response = generate_text(test_prompt)
    print(f"Response: '{response}'")

    empty_prompt = ""
    print(f"nPrompt: '{empty_prompt}'")
    response_empty = generate_text(empty_prompt)
    print(f"Response: '{response_empty}'")
EOF
echo "  - Created src/llm_service.py with docstrings"

echo "5. Verifying project structure and content..."
echo "---------------------------------------------------"
tree -a || echo "NOTE: 'tree' command not found. Please install it (e.g., 'sudo apt install tree' or 'brew install tree') to see the directory structure."
echo "---------------------------------------------------"
echo "You can now inspect the files manually to verify their content."
echo "e.g., 'cat README.md', 'cat docs/api/openapi.yaml', 'cat src/llm_service.py'"

echo "==================================================="
echo "LLM Foundations Day 1 Docs Project Setup Complete!"
echo "==================================================="

#!/bin/bash

# Exit immediately if a command exits with a non-zero status.
set -e

PROJECT_DIR="llm_foundations_day1_docs"

echo "==================================================="
echo "Stopping and Cleaning Up LLM Foundations Day 1 Docs Project"
echo "==================================================="

if [ -d "$PROJECT_DIR" ]; then
    echo "Removing project directory: $PROJECT_DIR"
    rm -rf "$PROJECT_DIR"
    echo "Clean up complete."
else
    echo "Project directory '$PROJECT_DIR' not found. Nothing to clean up."
fi

echo "==================================================="
echo "Cleanup Finished!"
echo "==================================================="

#!/bin/bash

# Exit immediately if a command exits with a non-zero status.
set -e

PROJECT_DIR="llm_foundations_day1_docs"

echo "==================================================="
echo "Stopping and Cleaning Up LLM Foundations Day 1 Docs Project"
echo "==================================================="

if [ -d "$PROJECT_DIR" ]; then
    echo "Removing project directory: $PROJECT_DIR"
    rm -rf "$PROJECT_DIR"
    echo "Clean up complete."
else
    echo "Project directory '$PROJECT_DIR' not found. Nothing to clean up."
fi

echo "==================================================="
echo "Cleanup Finished!"
echo "==================================================="