app.py

import os
import json
import re
import gradio as gr
from groq import Groq
from pydantic import BaseModel, Field
from typing import Optional, Literal

from custom_css import custom_css
from variables import *

class PromptInput(BaseModel):
    text: str = Field(..., description="The initial prompt text")
    meta_prompt_choice: Literal["superstar","star","done","physics","morphosis", "verse", "phor","bolism","math", "math_meta"] = Field(..., description="Choice of meta prompt strategy")

class RefinementOutput(BaseModel):
    query_analysis: Optional[str] = None
    initial_prompt_evaluation: Optional[str] = None
    refined_prompt: Optional[str] = None
    explanation_of_refinements: Optional[str] = None
    raw_content: Optional[str] = None

class PromptRefiner:
    def __init__(self, api_token: str):
        self.client = Groq(api_key=api_key)

    def refine_prompt(self, prompt_input: PromptInput) -> RefinementOutput:
        if prompt_input.meta_prompt_choice == "morphosis":
            selected_meta_prompt = original_meta_prompt
        elif prompt_input.meta_prompt_choice == "verse":
            selected_meta_prompt = new_meta_prompt
        elif prompt_input.meta_prompt_choice == "physics":
            selected_meta_prompt = metaprompt1
        elif prompt_input.meta_prompt_choice == "bolism":
            selected_meta_prompt = loic_metaprompt
        elif prompt_input.meta_prompt_choice == "done":
            selected_meta_prompt = metadone
        elif prompt_input.meta_prompt_choice == "star":
            selected_meta_prompt = echo_prompt_refiner
        elif prompt_input.meta_prompt_choice == "superstar":
            selected_meta_prompt = advanced_echo_prompt_refiner
        elif prompt_input.meta_prompt_choice == "math":
            selected_meta_prompt = math_meta_prompt
        elif prompt_input.meta_prompt_choice == "math_meta":
            selected_meta_prompt = math_meta
        else:
            selected_meta_prompt = advanced_meta_prompt
    
        messages = [
            {"role": "system", "content": 'You are an expert at refining and extending prompts. Given a basic prompt, provide a more detailed.'},
            {"role": "user", "content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt_input.text)}
        ]
        response = self.client.chat.completions.create(
            model="llama-3.2-90b-text-preview",
            messages=messages,
            max_tokens=8192,
            temperature=0.7
        )
        response_content = response.choices[0].message.content.strip()
        try:
            # Extract JSON from between <json> tags
            json_match = re.search(r'<json>\s*(.*?)\s*</json>', response_content, re.DOTALL)
            if json_match:
                json_str = json_match.group(1)
                # Remove newlines and escape quotes within the JSON string
                json_str = re.sub(r'\n\s*', ' ', json_str)
                json_str = json_str.replace('"', '\\"')
                # Wrap the entire string in quotes and parse it
                json_output = json.loads(f'"{json_str}"')
                # Ensure json_output is a dictionary
                if isinstance(json_output, str):
                    json_output = json.loads(json_output)
                # Unescape the parsed JSON
                for key, value in json_output.items():
                    if isinstance(value, str):
                        json_output[key] = value.replace('\\"', '"')
                return RefinementOutput(**json_output, raw_content=response_content)
            else:
                raise ValueError("No JSON found in the response")
        except (json.JSONDecodeError, ValueError) as e:
            print(f"Error parsing JSON: {e}")
            print(f"Raw content: {response_content}")
            # If JSON parsing fails, attempt to extract the content manually
            output = {}
            for key in ["initial_prompt_evaluation", "refined_prompt", "explanation_of_refinements"]:
                pattern = rf'"{key}":\s*"(.*?)"(?:,|\}})'
                match = re.search(pattern, response_content, re.DOTALL)
                if match:
                    output[key] = match.group(1).replace('\\n', '\n').replace('\\"', '"')
                else:
                    output[key] = "" # Set empty string if content not found
            return RefinementOutput(**output, raw_content=response_content)
                
    def apply_prompt(self, prompt: str, model: str) -> str:
        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.completions.create(
                model=model,
                messages=messages,
                max_tokens=8000, # Increased token limit
                temperature=0.8,
                stream=True  # Enable streaming in the API call
            )
            # Initialize an empty string to accumulate the response
            full_response = ""
            
            # Process the streaming response
            for chunk in response:
                if chunk.choices[0].delta.content is not None:
                    full_response += chunk.choices[0].delta.content
                    
            # Return the complete response
            return full_response.replace('\n\n', '\n').strip()
           
        except Exception as e:
            return f"Error: {str(e)}"

class GradioInterface:
    def __init__(self, prompt_refiner: PromptRefiner,custom_css):
        self.prompt_refiner = prompt_refiner
        custom_css = custom_css
        with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as self.interface:
            with gr.Column(elem_classes=["container", "title-container"]):
                gr.Markdown("# PROMPT++")
                gr.Markdown("### Automating Prompt Engineering by Refining your Prompts")
                gr.Markdown("Learn how to generate an improved version of your prompts.")
        
            with gr.Column(elem_classes=["container", "input-container"]):
                prompt_text = gr.Textbox(
                    label="Type your prompt (or let it empty to see metaprompt)",
                   # elem_classes="no-background",
                    #elem_classes="container2",
                    lines=5
                )
                meta_prompt_choice = gr.Radio(
                    ["superstar", "star", "done", "physics", "morphosis", "verse", "phor","bolism","math","math_meta"],
                    label="Choose Meta Prompt",
                    value="star",
                    elem_classes=["no-background", "radio-group"]
                   # elem_classes=[ "radio-group"]
                )
                refine_button = gr.Button("Refine Prompt")  
                
                # Option 1: Put Examples here (before Meta Prompt explanation)
                with gr.Row(elem_classes=["container2"]):
                    with gr.Accordion("Examples", open=False):
                        gr.Examples(
                            examples=[
                                ["Write a story on the end of prompt engineering replaced by an Ai specialized in refining prompts.", "superstar"],
                                ["Tell me about that guy who invented the light bulb", "physics"],
                                ["Explain the universe.", "star"],
                                ["What's the population of New York City and how tall is the Empire State Building and who was the first mayor?", "morphosis"],
                                ["List American presidents.", "verse"],                        
                                ["Explain why the experiment failed.", "morphosis"],
                                ["Is nuclear energy good?", "verse"],
                                ["How does a computer work?", "phor"],
                                ["How to make money fast?", "done"],
                                ["how can you prove IT0's lemma in stochastic calculus ?", "math_meta"],                    
                            ],
                            inputs=[prompt_text, meta_prompt_choice]
                        )
                       
                    with gr.Accordion("Meta Prompt explanation", open=False):
                        gr.Markdown(explanation_markdown)
                

                
                # Option 2: Or put Examples here (after the button)
                # with gr.Accordion("Examples", open=False):
                #     gr.Examples(...)
                
            with gr.Column(elem_classes=["container", "analysis-container"]):
                gr.Markdown(' ')
                gr.Markdown("### Initial prompt analysis")
                analysis_evaluation = gr.Markdown()
                gr.Markdown("### Refined Prompt")
                refined_prompt = gr.Textbox(
                                    label="Refined Prompt",
                                    interactive=True,
                                    show_label=True,  # Must be True for copy button to show
                                    show_copy_button=True,  # Adds the copy button
                             #       elem_classes="no-background"
                                )
                gr.Markdown("### Explanation of Refinements")
                explanation_of_refinements = gr.Markdown()
            

            with gr.Column(elem_classes=["container", "model-container"]):
              #  gr.Markdown("## See MetaPrompt Impact")            
                with gr.Row():
                    apply_model = gr.Dropdown(models,
                                            value="llama-3.1-70b-versatile",
                                            label="Choose the Model",
                                            container=False,  # This removes the container around the dropdown
                                            scale=1,         # Controls the width relative to other components
                                            min_width=300    # Sets minimum width in pixels
                                         #   elem_classes="no-background"
                                        )
                    apply_button = gr.Button("Apply MetaPrompt")

          #  with gr.Column(elem_classes=["container", "results-container"]):
                gr.Markdown("### Prompts on choosen model")
                with gr.Tabs():
                    with gr.TabItem("Original Prompt Output"):
                        original_output = gr.Markdown()
                    with gr.TabItem("Refined Prompt Output"):
                        refined_output = gr.Markdown()
            with gr.Accordion("Full Response JSON", open=False, visible=True):
                full_response_json = gr.JSON()
                
            refine_button.click(
                fn=self.refine_prompt,
                inputs=[prompt_text, meta_prompt_choice],
                outputs=[analysis_evaluation, refined_prompt, explanation_of_refinements, full_response_json]
            )

            apply_button.click(
                fn=self.apply_prompts,
                inputs=[prompt_text, refined_prompt, apply_model],
                outputs=[original_output, refined_output]
            )

    def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
        try:
            input_data = PromptInput(text=prompt, meta_prompt_choice=meta_prompt_choice)
            result = self.prompt_refiner.refine_prompt(input_data)
            
            # Ensure all values are strings or None
            analysis_evaluation = str(result.initial_prompt_evaluation) if result.initial_prompt_evaluation else ""
            refined_prompt = str(result.refined_prompt) if result.refined_prompt else ""
            explanation_refinements = str(result.explanation_of_refinements) if result.explanation_of_refinements else ""
            
            # Create a safe JSON-serializable dictionary
            full_response = {
                "initial_prompt_evaluation": analysis_evaluation,
                "refined_prompt": refined_prompt,
                "explanation_of_refinements": explanation_refinements,
                "raw_content": str(result.raw_content) if result.raw_content else ""
            }
            
            return (
                analysis_evaluation,
                refined_prompt,
                explanation_refinements,
                full_response
            )
        except Exception as e:
            # Return safe default values in case of any error
            error_response = {
                "error": str(e),
                "initial_prompt_evaluation": "",
                "refined_prompt": "",
                "explanation_of_refinements": "",
                "raw_content": ""
            }
            return "", "", "", error_response

    def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
        original_output = self.prompt_refiner.apply_prompt(original_prompt, model)
        refined_output = self.prompt_refiner.apply_prompt(refined_prompt, model)
        return original_output, refined_output

    def launch(self, share=False):
        self.interface.launch(share=share)
        
# explanation_markdown = "".join([f"- **{key}**: {value}\n" for key, value in metaprompt_explanations.items()])

```
meta_info=""
api_key = os.getenv('GROQ_API_KEY')
if not api_key:
    raise ValueError("GROQ_API_KEY not found in environment variables")

        
metadone=os.getenv('metadone')
echo_prompt_refiner = os.getenv('echo_prompt_refiner')
advanced_echo_prompt_refiner = os.getenv('advanced_echo_prompt_refiner')
metaprompt1 = os.getenv('metaprompt1')     
loic_metaprompt = os.getenv('loic_metaprompt')    
openai_metaprompt=os.getenv('openai_metaprompt')
original_meta_prompt = os.getenv('original_meta_prompt')    
new_meta_prompt = os.getenv('new_meta_prompt')   
advanced_meta_prompt = os.getenv('advanced_meta_prompt')
math_meta_prompt = os.getenv('math_meta_prompt')
math_meta = os.getenv('math_meta')
```


# Main code to run the application
if __name__ == '__main__':
    prompt_refiner = PromptRefiner(api_key)
    gradio_interface = GradioInterface(prompt_refiner,custom_css)
    gradio_interface.launch(share=True)