Update app.py

app.py

@@ -112,11 +112,14 @@ class GradioInterface:
 
         with gr.Blocks() as self.interface:
             gr.Markdown("# PROMPT++")
-            gr.Markdown("### Refine your Prompts: Learn how to generate an improved version of your prompts")
-            gr.Markdown("Enter a main idea for a prompt, choose a meta prompt, and the model will attempt to generate an improved version.")
+            gr.Markdown("### Automating Prompt Engineering by Refining your Prompts")
+            gr.Markdown("Learn how to generate an improved version of your prompts. Enter a main idea for a prompt, choose a meta prompt, and the model will attempt to generate an improved version.")
 
+            gr.Markdown("## Refine Prompt")
             with gr.Row():
-                prompt_text = gr.Textbox(label="Type the prompt here")
+                prompt_text = gr.Textbox(label="Type the prompt (or let it empty to see metaprompt)")
+            with gr.Accordion("Meta Prompt explanation", open=False):
+                gr.Markdown(explanation_markdown)
             with gr.Row():
                 meta_prompt_choice = gr.Radio(["superstar", "star", "done", "physics", "morphosis", "verse", "phor","bolism"], label="Choose Meta Prompt", value="morphosis")
                 refine_button = gr.Button("Refine Prompt")
@@ -128,15 +131,17 @@ class GradioInterface:
                 refined_prompt = gr.Textbox(label="Refined Prompt")
                 gr.Markdown("### Explanation of Refinements")
                 explanation_of_refinements = gr.Markdown(label="Explanation of Refinements")
-
-            with gr.Accordion("Full Response JSON", open=False):
+            
+            with gr.Accordion("Full Response JSON", open=False,visible=False):
                 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]
             )
+            gr.Markdown("## See MetaPrompt Impact")  
             with gr.Row():
                 apply_model = gr.Dropdown(
                     [
@@ -151,37 +156,37 @@ class GradioInterface:
                     label="Choose the Model to apply prompts"                
                 )
                 #   apply_model=gr.Dropdown(["gpt-4o",'gpt-4-turbo'], value="gpt-4o", label="Model"),
-                apply_button = gr.Button("Apply Prompts")
-
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("### Original Prompt Output")
+                apply_button = gr.Button("Apply MetaPrompt")
+                
+            with gr.Tab("Original Prompt Output"):
+                   # gr.Markdown("### Original Prompt Output")
                     original_output = gr.Markdown(label="Original Prompt Output")
-                with gr.Column():
-                    gr.Markdown("### Refined Prompt Output")
+            with gr.Tab("Refined Prompt Output"):
+                    #gr.Markdown("### Refined Prompt Output")
                     refined_output = gr.Markdown(label="Refined Prompt Output")
 
             apply_button.click(
                 fn=self.apply_prompts,
                 inputs=[prompt_text, refined_prompt, apply_model],
-                outputs=[original_output, refined_output]
+                outputs=[original_output, refined_output]  
             )
 
-            gr.Examples(
-                examples=[
-                    ["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"],
-                    ["Write a story.", "bolism"],
-                    ["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 ?", "superstar"],                    
-                ],
-                inputs=[prompt_text, meta_prompt_choice]
-            )
+            with gr.Accordion("Examples", open=True):
+                gr.Examples(
+                    examples=[
+                        ["Write a story on the end of prompt engineering replaced by an Ai specialized in refining prompts.", "star"],
+                        ["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 ?", "superstar"],                    
+                    ],
+                    inputs=[prompt_text, meta_prompt_choice]
+                )
 
                 
     def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
@@ -202,7 +207,20 @@ class GradioInterface:
 
     def launch(self, share=False):
         self.interface.launch(share=share)
-        
+
+metaprompt_explanations = {
+    "star": "Use ECHO when you need a comprehensive, multi-stage approach for complex prompts. It's ideal for tasks requiring in-depth analysis, exploration of multiple alternatives, and synthesis of ideas. Choose this over others when you have time for a thorough refinement process and need to consider various aspects of the prompt.",
+    "superstar": "Use advanced ECHO when you need a comprehensive, multi-stage approach for complex prompts. It's ideal for tasks requiring in-depth analysis, exploration of multiple alternatives, and synthesis of ideas. Choose this over others when you have time for a thorough refinement process and need to consider various aspects of the prompt.",
+    "done": "Opt for this when you want a structured approach with emphasis on role-playing and advanced techniques. It's particularly useful for tasks that benefit from diverse perspectives and complex reasoning. Prefer this over 'physics' when you need a more detailed, step-by-step refinement process.",
+    "physics": "Select this when you need a balance between structure and advanced techniques, with a focus on role-playing. It's similar to 'done' but may be more suitable for scientific or technical prompts. Choose this over 'done' for a slightly less complex approach.",
+    "morphosis": "Use this simplified approach for straightforward prompts or when time is limited. It focuses on essential improvements without complex techniques. Prefer this over other methods when you need quick, clear refinements without extensive analysis.",
+    "verse": "Choose this method when you need to analyze and improve a prompt's strengths and weaknesses, with a focus on information flow. It's particularly useful for enhancing the logical structure of prompts. Use this over 'morphosis' when you need more depth but less complexity than 'star'.",
+    "phor": "Employ this advanced approach when you need to combine multiple prompt engineering techniques. It's ideal for complex tasks requiring both clarity and sophisticated prompting methods. Select this over 'star' when you want a more flexible, technique-focused approach.",
+    "bolism": "Utilize this method when working with autoregressive language models and when the task requires careful reasoning before conclusions. It's best for prompts that need detailed output formatting. Choose this over others when the prompt's structure and reasoning order are crucial."
+}
+
+explanation_markdown = "".join([f"- **{key}**: {value}\n" for key, value in metaprompt_explanations.items()])
+
 # Main code to run the application
 if __name__ == '__main__':
     api_key = os.getenv('GROQ_API_KEY')