app.py

import gradio as gr
from prompt_refiner import PromptRefiner
from variables import models, explanation_markdown
from variables import *
from custom_css import custom_css

class GradioInterface:
  def __init__(self, prompt_refiner: PromptRefiner, custom_css):
      self.prompt_refiner = prompt_refiner
      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)",
                  lines=5
              )
              with gr.Row(elem_classes=["container2"]):
                  automatic_metaprompt_button = gr.Button("Refine Prompt using automatic MetaPrompt choice")
                  
              with gr.Accordion("Manual Choice of Meta Prompt", open=False):
                  meta_prompt_choice = gr.Radio(
                      metaprompt_list,
                      label="Choose Meta Prompt",
                      value=metaprompt_list[0],
                      elem_classes=["no-background", "radio-group"]
                  )
                  refine_button = gr.Button("Refine Prompt using manual choice") 
                  gr.Markdown(explanation_markdown)
                  gr.Examples(
                      examples=examples,
                      inputs=[prompt_text, meta_prompt_choice]
                  )

                                 
          with gr.Column(elem_classes=["container", "analysis-container"]):
              gr.Markdown(' ')
              gr.Markdown("### Original Prompt analysis")
              analysis_evaluation = gr.Markdown()
              gr.Markdown("---")
              #MetaPrompt_analysis_evaluation= gr.Markdown("### MataPrompt used")
              MetaPrompt_analysis = gr.Markdown("### MataPrompt used")              
              gr.Markdown("### Refined Prompt")
              refined_prompt = gr.Textbox(
                  label="Refined Prompt",
                  interactive=True,
                  show_label=True,
                  show_copy_button=True,
              )
              gr.Markdown("### Explanation of Refinements")
              explanation_of_refinements = gr.Markdown()
          
          with gr.Column(elem_classes=["container", "model-container"]):
              with gr.Row():
                  apply_model = gr.Dropdown(models,
                      value="meta-llama/Llama-3.1-8B-Instruct",
                      label="Choose the Model",
                      container=False,
                      scale=1,
                      min_width=300
                  )
                  apply_button = gr.Button("Apply Prompts")

              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()

          automatic_metaprompt_button.click(
              fn=self.automatic_metaprompt,
              inputs=[prompt_text, meta_prompt_choice],
              outputs=[MetaPrompt_analysis, analysis_evaluation, refined_prompt, explanation_of_refinements, full_response_json]
          )
          
          refine_button.click(
              fn=self.refine_prompt,
              inputs=[prompt_text, meta_prompt_choice],
              outputs=[MetaPrompt_analysis, 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],
              api_name="apply_prompts"
          )
          gr.HTML(
              "<p style='text-align: center; color:orange;'>⚠ This space is in progress, and we're actively working on it, so you might find some bugs! Please report any issues you have in the Community tab to help us make it better for all.</p>"
          )

  def automatic_metaprompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
      """Wrapper method to call prompt_refiner's automatic_metaprompt"""
      metaprompt_analysis_evaluation, initial_prompt_evaluation, refined_prompt, explanation_refinements, full_response = (
          self.prompt_refiner.automatic_metaprompt(prompt, meta_prompt_choice)
      )
      analysis_evaluation = f"\n\n{initial_prompt_evaluation}"
      return (
          metaprompt_analysis_evaluation,
          analysis_evaluation,
          refined_prompt,
          explanation_refinements,
          full_response
      )

  def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
      """Refine the given prompt using the selected meta prompt."""
      metaprompt_analysis_evaluation, initial_prompt_evaluation, refined_prompt, explanation_refinements, full_response = (
          self.prompt_refiner.refine_prompt(prompt, meta_prompt_choice)
      )
      analysis_evaluation = f"\n\n{initial_prompt_evaluation}"
      return (
          metaprompt_analysis_evaluation,
          analysis_evaluation,
          refined_prompt,
          explanation_refinements,
          full_response
      )

  def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
      try:
          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
      except Exception as e:
          return f"Error: {str(e)}", f"Error: {str(e)}"

  def launch(self, share=False):
      self.interface.launch(share=share)


if __name__ == '__main__':
  # Initialize the prompt refiner with API token
  prompt_refiner = PromptRefiner(api_token, meta_prompts, metaprompt_explanations)
  
  # Create the Gradio interface
  gradio_interface = GradioInterface(prompt_refiner, custom_css)
  
  # Launch the interface
  gradio_interface.launch(share=True)