Update app.py

app.py

@@ -6,6 +6,16 @@ import gradio as gr
 from pydantic import BaseModel, Field
 from typing import Optional, Literal
 
+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."
+}
+
 class PromptInput(BaseModel):
     text: str = Field(..., description="The initial prompt text")
     meta_prompt_choice: Literal["star","done","physics","morphosis", "verse", "phor","bolism"] = Field(..., description="Choice of meta prompt strategy")
@@ -16,6 +26,7 @@ class RefinementOutput(BaseModel):
     refined_prompt: Optional[str] = None
     explanation_of_refinements: Optional[str] = None
     raw_content: Optional[str] = None
+    metaprompt_explanation: Optional[str] = None  # New field
 
 class PromptRefiner:
     def __init__(self, api_token: str):
@@ -42,10 +53,10 @@ class PromptRefiner:
             {"role": "user", "content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt_input.text)}
         ]
         response = self.client.chat_completion(
-            model=prompt_refiner_model,
+            model="meta-llama/Meta-Llama-3-70B-Instruct",
             messages=messages,
-            max_tokens=2000,
-            temperature=0.8
+            max_tokens=4000,
+            temperature=0.3
         )
         response_content = response.choices[0].message.content.strip()
         try:
@@ -60,6 +71,10 @@ class PromptRefiner:
                 for key, value in json_output.items():
                     if isinstance(value, str):
                         json_output[key] = value.replace('\\"', '"')
+                
+                # Add the metaprompt explanation to the output
+                json_output['metaprompt_explanation'] = metaprompt_explanations.get(prompt_input.meta_prompt_choice, "")
+                
                 return RefinementOutput(**json_output, raw_content=response_content)
             else:
                 raise ValueError("No JSON found in the response")
@@ -74,6 +89,10 @@ class PromptRefiner:
                     output[key] = match.group(1).replace('\\n', '\n').replace('\\"', '"')
                 else:
                     output[key] = ""
+            
+            # Add the metaprompt explanation to the output
+            output['metaprompt_explanation'] = metaprompt_explanations.get(prompt_input.meta_prompt_choice, "")
+            
             return RefinementOutput(**output, raw_content=response_content)
                 
     def apply_prompt(self, prompt: str, model: str) -> str:
@@ -119,14 +138,17 @@ class GradioInterface:
                 gr.Markdown("### Explanation of Refinements")
                 explanation_of_refinements = gr.Markdown(label="Explanation of Refinements")
             
+            with gr.Row():
+                gr.Markdown("### Metaprompt Explanation")
+                metaprompt_explanation = gr.Markdown(label="Metaprompt Explanation")
+
             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]
+                outputs=[analysis_evaluation, refined_prompt, explanation_of_refinements, full_response_json, metaprompt_explanation]
             )
             with gr.Row():
                 apply_model = gr.Dropdown(
@@ -181,7 +203,8 @@ class GradioInterface:
             analysis_evaluation,
             result.refined_prompt,
             result.explanation_of_refinements,
-            result.dict()
+            result.dict(),
+            result.metaprompt_explanation
         )
 
     def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):