prompt_refiner.py

import json
import re
from typing import Optional, Dict, Any, Union, List, Tuple
from pydantic import BaseModel, Field, validator
from huggingface_hub import InferenceClient
from huggingface_hub.errors import HfHubHTTPError
from variables import *
from metaprompt_router import metaprompt_router

class LLMResponse(BaseModel):
  initial_prompt_evaluation: str = Field(..., description="Evaluation of the initial prompt")
  refined_prompt: str = Field(..., description="The refined version of the prompt")
  explanation_of_refinements: Union[str, List[str]] = Field(..., description="Explanation of the refinements made")
  response_content: Optional[Union[Dict[str, Any], str]] = Field(None, description="Raw response content")

  @validator('response_content', pre=True)
  def validate_response_content(cls, v):
      if isinstance(v, str):
          try:
              return json.loads(v)
          except json.JSONDecodeError:
              return {"raw_content": v}
      return v

  @validator('initial_prompt_evaluation', 'refined_prompt')
  def clean_text_fields(cls, v):
      if isinstance(v, str):
          return v.strip().replace('\\n', '\n').replace('\\"', '"')
      return v

  @validator('explanation_of_refinements')
  def clean_refinements(cls, v):
      if isinstance(v, str):
          return v.strip().replace('\\n', '\n').replace('\\"', '"')
      elif isinstance(v, list):
          return [item.strip().replace('\\n', '\n').replace('\\"', '"').replace('•', '-') 
                 for item in v if isinstance(item, str)]
      return v

class PromptRefiner:
  def __init__(self, api_token: str, meta_prompts: dict,metaprompt_explanations: dict):
      self.client = InferenceClient(token=api_token, timeout=120)
      self.meta_prompts = meta_prompts
      self.metaprompt_explanations=metaprompt_explanations

  def _clean_json_string(self, content: str) -> str:
      """Clean and prepare JSON string for parsing."""
      content = content.replace('•', '-')  # Replace bullet points
      content = re.sub(r'\s+', ' ', content)  # Normalize whitespace
      content = content.replace('\\"', '"')  # Fix escaped quotes
      return content.strip()

  def _parse_response(self, response_content: str) -> dict:
      """Parse the LLM response with enhanced error handling."""
      try:
          # Extract content between <json> tags
          json_match = re.search(r'<json>\s*(.*?)\s*</json>', response_content, re.DOTALL)
          if json_match:
              json_str = self._clean_json_string(json_match.group(1))
              try:
                  # Try parsing the cleaned JSON
                  parsed_json = json.loads(json_str)
                  if isinstance(parsed_json, str):
                      parsed_json = json.loads(parsed_json)
                  
                  return {
                      "initial_prompt_evaluation": parsed_json.get("initial_prompt_evaluation", ""),
                      "refined_prompt": parsed_json.get("refined_prompt", ""),
                      "explanation_of_refinements": parsed_json.get("explanation_of_refinements", ""),
                      "response_content": parsed_json if isinstance(parsed_json, dict) else {"raw_content": parsed_json}
                  }
              except json.JSONDecodeError:
                  # If JSON parsing fails, try regex parsing
                  return self._parse_with_regex(json_str)
          
          # If no JSON tags found, try regex parsing
          return self._parse_with_regex(response_content)

      except Exception as e:
          print(f"Error parsing response: {str(e)}")
          print(f"Raw content: {response_content}")
          return self._create_error_dict(str(e))

  def _parse_with_regex(self, content: str) -> dict:
      """Parse content using regex when JSON parsing fails."""
      output = {}
      
      # Handle explanation_of_refinements list format
      refinements_match = re.search(r'"explanation_of_refinements":\s*$(.*?)$', content, re.DOTALL)
      if refinements_match:
          refinements_str = refinements_match.group(1)
          refinements = [
              item.strip().strip('"').strip("'").replace('•', '-')
              for item in re.findall(r'[•"]([^"•]+)[•"]', refinements_str)
          ]
          output["explanation_of_refinements"] = refinements
      else:
          # Try single string format
          pattern = r'"explanation_of_refinements":\s*"(.*?)"(?:,|\})'
          match = re.search(pattern, content, re.DOTALL)
          output["explanation_of_refinements"] = match.group(1).strip() if match else ""

      # Extract other fields
      for key in ["initial_prompt_evaluation", "refined_prompt"]:
          pattern = rf'"{key}":\s*"(.*?)"(?:,|\}})'
          match = re.search(pattern, content, re.DOTALL)
          output[key] = match.group(1).strip() if match else ""
      
      # Store the original content in a structured way
      output["response_content"] = {"raw_content": content}
      return output

  def _create_error_dict(self, error_message: str) -> dict:
      """Create a standardized error response dictionary."""
      return {
          "initial_prompt_evaluation": f"Error parsing response: {error_message}",
          "refined_prompt": "",
          "explanation_of_refinements": "",
          "response_content": {"error": error_message}
      }
      
  def automatic_metaprompt(self, prompt: str, meta_prompt_choice: str) -> Tuple[str, str, str, dict]:
      """Automatically select and apply the most appropriate metaprompt for the given prompt."""
      try:
          # First, use the router to determine the best metaprompt
          router_messages = [
              {
                  "role": "system",
                  "content": "You are an AI Prompt Selection Assistant that helps choose the most appropriate metaprompt based on the user's query."
              },
              {
                  "role": "user",
                  "content": metaprompt_router.replace("[Insert initial prompt here]", prompt)
              }
          ]
    
          # Get router response
          router_response = self.client.chat_completion(
              model=prompt_refiner_model,
              messages=router_messages,
              max_tokens=3000,
              temperature=0.2
          )
    
          router_content = router_response.choices[0].message.content.strip()
          
          # Extract JSON from router response
          json_match = re.search(r'<json>(.*?)</json>', router_content, re.DOTALL)
          if not json_match:
              raise ValueError("No JSON found in router response")
              
          router_result = json.loads(json_match.group(1))
          
          # Get the recommended metaprompt key
          recommended_key = router_result["recommended_metaprompt"]["key"]
          
          # Use the recommended metaprompt to refine the prompt
          selected_meta_prompt = self.meta_prompts.get(recommended_key)
          selected_meta_prompt_explanations = self.metaprompt_explanations.get(recommended_key)
          
          # Now use the selected metaprompt to refine the original prompt
          refine_messages = [
              {
                  "role": "system",
                  "content": 'You are an expert at refining and extending prompts. Given a basic prompt, provide a more relevant and detailed prompt.'
              },
              {
                  "role": "user",
                  "content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt)
              }
          ]
    
          response = self.client.chat_completion(
              model=prompt_refiner_model,
              messages=refine_messages,
              max_tokens=3000,
              temperature=0.8
          )
    
          response_content = response.choices[0].message.content.strip()
          result = self._parse_response(response_content)
    
          try:
              llm_response = LLMResponse(**result)
              metaprompt_analysis = f"""
                    #### Selected MetaPrompt Analysis
                    - <span style="color: grey; font-style: italic;">**Primary Choice**</span>: *{router_result["recommended_metaprompt"]["name"]}*
                    - *Description*: *{router_result["recommended_metaprompt"]["description"]}*
                    - *Why This Choice*: *{router_result["recommended_metaprompt"]["explanation"]}*
                    
                    #### Alternative Option
                    - <span style="color: grey; font-style: italic;">**Secondary Choice**</span>: *{router_result["alternative_recommendation"]["name"]}*
                    - *Why Consider This*: *{router_result["alternative_recommendation"]["explanation"]}*
                    """
              return (
                  metaprompt_analysis,
                  llm_response.initial_prompt_evaluation,
                  llm_response.refined_prompt,
                  llm_response.explanation_of_refinements,
                  llm_response.dict()
              )
          except Exception as e:
              print(f"Error creating LLMResponse: {e}")
              return self._create_error_response(f"Error validating response: {str(e)}")
    
      except HfHubHTTPError as e:
          return self._create_error_response("Model timeout. Please try again later.")
      except Exception as e:
          return self._create_error_response(f"Unexpected error: {str(e)}")



  def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> Tuple[str, str, str, dict]:
      """Refine the given prompt using the selected meta prompt."""
      try:
          selected_meta_prompt = self.meta_prompts.get(meta_prompt_choice)
          selected_meta_prompt_explanations = self.metaprompt_explanations.get(meta_prompt_choice)
          
          messages = [
              {
                  "role": "system", 
                  "content": 'You are an expert at refining and extending prompts. Given a basic prompt, provide a more relevant and detailed prompt.'
              },
              {
                  "role": "user", 
                  "content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt)
              }
          ]
          
          response = self.client.chat_completion(
              model=prompt_refiner_model,
              messages=messages,
              max_tokens=3000,
              temperature=0.8
          )
          
          response_content = response.choices[0].message.content.strip()
          result = self._parse_response(response_content)
          
          try:
              llm_response = LLMResponse(**result)
              return (
                  f"- **{meta_prompt_choice}**: {selected_meta_prompt_explanations}",
                  llm_response.initial_prompt_evaluation,
                  llm_response.refined_prompt,
                  llm_response.explanation_of_refinements,
                  llm_response.dict()
              )
          except Exception as e:
              print(f"Error creating LLMResponse: {e}")
              return self._create_error_response(f"Error validating response: {str(e)}")

      except HfHubHTTPError as e:
          return self._create_error_response("Model timeout. Please try again later.")
      except Exception as e:
          return self._create_error_response(f"Unexpected error: {str(e)}")

  def _create_error_response(self, error_message: str) -> Tuple[str, str, str, dict]:
      """Create a standardized error response tuple."""
      return (
          f"Error: {error_message}",
          f"Error: {error_message}",
          "The selected model is currently unavailable.",
          "An error occurred during processing.",
          {"error": error_message}
      )

  def apply_prompt(self, prompt: str, model: str) -> str:
      """Apply formatting to the prompt using the specified model."""
      try:
          messages = [
              {
                  "role": "system",
                  "content": """You are a markdown formatting expert. Format your responses with proper spacing and structure following these rules:
                      1. Paragraph Spacing:
                      - Add TWO blank lines between major sections (##)
                      - Add ONE blank line between subsections (###)
                      - Add ONE blank line between paragraphs within sections
                      - Add ONE blank line before and after lists
                      - Add ONE blank line before and after code blocks
                      - Add ONE blank line before and after blockquotes
                      
                      2. Section Formatting:
                      # Title
                      
                      ## Major Section
                      
                      [blank line]
                      Content paragraph 1
                      [blank line]
                      Content paragraph 2
                      [blank line]"""
              },
              {
                  "role": "user",
                  "content": prompt
              }
          ]
  
          response = self.client.chat_completion(
              model=model,
              messages=messages,
              max_tokens=3000,
              temperature=0.8,
              stream=True
          )
          
          full_response = ""
          for chunk in response:
              if chunk.choices[0].delta.content is not None:
                  full_response += chunk.choices[0].delta.content
                  
          return full_response.replace('\n\n', '\n').strip()
              
      except Exception as e:
          return f"Error: {str(e)}"