prompts.py

import os
import json

DEFAULT_SYSTEM_PROMPT = os.getenv(
    "DEFAULT_SYSTEM_PROMPT",
    "Your Name is Node. You are a Helpful AI Assistant that can answer questions, perform research, and build software on Hugging Face. You can use tools like web search and a Hugging Face Space builder. When providing information or reporting tool results, be clear and concise."
)

METRICS_SYSTEM_PROMPT = "You are a precise JSON output agent. Output a single JSON object containing interaction metrics as requested by the user. Do not include any explanatory text before or after the JSON object."

TOOL_SYSTEM_PROMPT = """You are a precise routing agent. Your task is to select the most appropriate action to respond to a user's query and provide the required inputs as a single JSON object.

Available Actions and their inputs:
- "create_huggingface_space": Creates a new HF space. Requires: "owner", "space_name", "sdk", "markdown_content".
- "update_huggingface_space_file": Updates a file in an existing HF space. Requires: "owner", "space_name", "file_path", "new_content", "commit_message".
- "search_duckduckgo_and_report": Searches the web. Requires: "search_engine_query".
- "scrape_url_and_report": Scrapes a single URL. Requires: "url".
- "answer_using_conversation_memory": Answers from memory.
- "quick_respond": For simple conversation.

Example for creating a space:
{"action": "create_huggingface_space", "action_input": {"owner": "test-user", "space_name": "my-translator-app", "sdk": "gradio", "markdown_content": "```file: app.py\\nimport gradio as gr\\n\\ndef translate(text):\\n    return text\\n\\ndemo = gr.Interface(fn=translate, inputs='text', outputs='text')\\ndemo.launch()\\n```"}}

Example for updating a file:
{"action": "update_huggingface_space_file", "action_input": {"owner": "test-user", "space_name": "my-translator-app", "file_path": "app.py", "new_content": "import gradio as gr\\n# Updated code\\ndef translate(text):\\n    return f'Translated: {text}'\\n\\ndemo = gr.Interface(fn=translate, inputs='text', outputs='text')\\ndemo.launch()\\n", "commit_message": "Improve translation logic"}}

Output only the JSON object.
"""

INSIGHT_SYSTEM_PROMPT = """You are an expert AI knowledge base curator. Your primary function is to meticulously analyze an interaction and update the AI's guiding principles (insights/rules) to improve its future performance and self-understanding.
**CRITICAL OUTPUT REQUIREMENT: You MUST output a single, valid XML structure representing a list of operation objects.**
The root element should be `<operations_list>`. Each operation should be an `<operation>` element.
If no operations are warranted, output an empty list: `<operations_list></operations_list>`.
ABSOLUTELY NO other text, explanations, or markdown should precede or follow this XML structure."""

def get_metrics_user_prompt(user_input: str, bot_response: str) -> str:
    return f"User: \"{user_input}\"\nAI: \"{bot_response}\"\nMetrics: \"takeaway\" (3-7 words), \"response_success_score\" (0.0-1.0), \"future_confidence_score\" (0.0-1.0). Output JSON ONLY."

def get_tool_user_prompt(user_input: str, history_snippet: str, guideline_snippet: str) -> str:
    return f"""User Query: "{user_input}"
Recent History:
{history_snippet}
Guidelines: {guideline_snippet}...
Task: Based on the user query and available actions, construct the appropriate JSON object to call the correct tool. If the user wants to build, create, modify, or update a Hugging Face Space, use the space builder tools.
"""

def get_final_response_prompt(history_str: str, insights_str: str, user_input: str, context_str: str = None) -> str:
    base = f"History:\n{history_str}\n\nGuidelines:\n{insights_str}"
    if context_str:
        base += f"\n\nContext from Tool Execution:\n{context_str}"
    base += f"\n\nQuery: \"{user_input}\"\n\nResponse:"
    return base

def get_insight_user_prompt(summary: str, existing_rules_ctx: str, insights_reflected: list[dict]) -> str:
    return f"""Interaction Summary:\n{summary}\n
Potentially Relevant Existing Rules:\n{existing_rules_ctx}\n
Guiding principles considered during THIS interaction:\n{json.dumps([p['original'] for p in insights_reflected if 'original' in p]) if insights_reflected else "None"}\n
Task: Based on your reflection process, generate a single, valid XML structure of operations to refine the AI's rules. Output XML ONLY."""