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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 *
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[Dict[str, Any]] = Field(None, description="Raw response content")
@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):
self.client = InferenceClient(token=api_token, timeout=120)
self.meta_prompts = meta_prompts
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
}
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 ""
output["response_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 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,
self.meta_prompts["star"]
)
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 (
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}",
"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)}" |