Update app.py

app.py

@@ -108,7 +108,7 @@ class GradioInterface:
             with gr.Row():
                 prompt_text = gr.Textbox(label="Type the prompt here")
             with gr.Row():
-                meta_prompt_choice = gr.Radio(["star","done","physics","morphosis", "verse", "phor","bolism"], label="Choose Meta Prompt", value="morphosis")
+                meta_prompt_choice = gr.Radio(["star","done","physics","morphosis", "verse", "phor","bolism"], label="Choose Meta Prompt", value="star",info=lambda x: metaprompt_explanations.get(x, "No explanation available."))
                 refine_button = gr.Button("Refine Prompt")
             with gr.Row():
                 gr.Markdown("### Initial prompt analysis")
@@ -192,8 +192,19 @@ class GradioInterface:
     def launch(self, share=False):
         self.interface.launch(share=share)
 
+metaprompt_explanations = {
+    "star": "The ECHO (Enhanced Chain of Harmonized Optimization) method, which provides a comprehensive and structured approach to prompt refinement, including multiple stages of analysis, expansion, and synthesis.",
+    "done": "A detailed, multi-step approach that emphasizes role-playing, structured output, and various advanced prompting techniques like Chain-of-Thought and Tree of Thoughts.",
+    "physics": "A prompt enhancement method that focuses on role-playing, structured output, and incorporating multiple advanced prompting techniques such as Chain-of-Thought and Tree of Thoughts.",
+    "morphosis": "A simplified approach that focuses on clear language, logical flow, and essential elements of prompt engineering without complex techniques.",
+    "verse": "A structured method that emphasizes analyzing the initial prompt, evaluating its strengths and weaknesses, and refining it with a focus on information flow and versatility.",
+    "phor": "An advanced prompt engineering approach that combines multiple techniques, including clarity enhancement, structural improvement, and various specialized prompting methods like Chain-of-Thought and Few-Shot Learning.",
+    "bolism": "A prompt refinement method that emphasizes leveraging the autoregressive nature of language models, encouraging reasoning before conclusions, and providing detailed instructions for output formatting."
+}
+
 # Main code to run the application
 if __name__ == '__main__':
+    meta_info=""
     api_token = os.getenv('HF_API_TOKEN')
     if not api_token:
         raise ValueError("HF_API_TOKEN not found in environment variables")