Update app.py

app.py

@@ -9,9 +9,44 @@ from typing import Optional, Literal
 from custom_css import custom_css
 from variables import *
 
+logger = logging.getLogger(__name__)
+
+# Add system prompt generation meta prompt
+SYSTEM_META_PROMPT = = """
+Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.
+# Guidelines
+- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
+- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
+- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
+    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
+    - Conclusion, classifications, or results should ALWAYS appear last.
+- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
+   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
+- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
+- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
+- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
+- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
+- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)
+    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.
+    - JSON should never be wrapped in code blocks (```) unless explicitly requested.
+The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")
+[Concise instruction describing the task - this should be the first line in the prompt, no section header]
+[Additional details as needed.]
+[Optional sections with headings or bullet points for detailed steps.]
+# Steps [optional]
+[optional: a detailed breakdown of the steps necessary to accomplish the task]
+# Output Format
+[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc]
+# Examples [optional]
+[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]
+[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]
+# Notes [optional]
+[optional: edge cases, details, and an area to call or repeat out specific important considerations]
+""".strip()
+
 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")
+    meta_prompt_choice: Literal["superstar","star","done","physics","morphosis", "verse", "phor","bolism","math", "math_meta", "system"] = Field(..., description="Choice of meta prompt strategy")
 
 class RefinementOutput(BaseModel):
     query_analysis: Optional[str] = None
@@ -19,12 +54,52 @@ class RefinementOutput(BaseModel):
     refined_prompt: Optional[str] = None
     explanation_of_refinements: Optional[str] = None
     raw_content: Optional[str] = None
+    system_prompt: Optional[str] = None  # New field for system prompt
 
 class PromptRefiner:
     def __init__(self, api_token: str):
         self.client = Groq(api_key=api_key)
 
+        def generate_system_prompt(self, task_or_prompt: str, model: str = "llama-3.2-90b-text-preview") -> str:
+        """Generate a system prompt for the given task or prompt."""
+        messages = [
+            {
+                "role": "system",
+                "content": SYSTEM_META_PROMPT,
+            },
+            {
+                "role": "user",
+                "content": f"Task, Goal, or Current Prompt:\n{task_or_prompt}",
+            },
+        ]
+        
+        response = self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            max_tokens=3000,
+            temperature=0.7,
+            stream=True
+        )
+        
+        prompt = ''
+        for chunk in response:
+            if chunk.choices[0].delta.content is not None:
+                prompt += chunk.choices[0].delta.content
+                
+        logger.info("Generated system prompt: %s", prompt)
+        return prompt
+    
     def refine_prompt(self, prompt_input: PromptInput) -> RefinementOutput:
+        # Handle system prompt generation separately
+        if prompt_input.meta_prompt_choice == "system":
+            system_prompt = self.generate_system_prompt(prompt_input.text)
+            return RefinementOutput(
+                refined_prompt=system_prompt,
+                explanation_of_refinements="Generated system prompt based on the task/prompt.",
+                system_prompt=system_prompt
+            )
+            
+        # Existing meta prompt selection logic      
         if prompt_input.meta_prompt_choice == "morphosis":
             selected_meta_prompt = original_meta_prompt
         elif prompt_input.meta_prompt_choice == "verse":
@@ -164,9 +239,9 @@ class GradioInterface:
                     lines=5
                 )
                 meta_prompt_choice = gr.Radio(
-                    ["superstar", "star", "done", "physics", "morphosis", "verse", "phor","bolism","math","math_meta"],
+                    ["superstar", "star", "done", "physics", "morphosis", "verse", "phor","bolism","math","math_meta", "system"],
                     label="Choose Meta Prompt",
-                    value="star",
+                    value="superstar",
                     elem_classes=["no-background", "radio-group"]
                    # elem_classes=[ "radio-group"]
                 )
@@ -238,13 +313,25 @@ class GradioInterface:
                         refined_output = gr.Markdown()
             with gr.Accordion("Full Response JSON", open=False, visible=True):
                 full_response_json = gr.JSON()
-                
+
+            # Add new tab for system prompt output
+            with gr.Column(elem_classes=["container", "system-prompt-container"]):
+                with gr.Tabs():
+                    with gr.TabItem("System Prompt"):
+                        system_prompt_output = gr.Textbox(
+                            label="Generated System Prompt",
+                            interactive=True,
+                            show_label=True,
+                            show_copy_button=True
+                        )
+
+            # Modified click handler to include system prompt output
             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, system_prompt_output]
             )
-
+                
             apply_button.click(
                 fn=self.apply_prompts,
                 inputs=[prompt_text, refined_prompt, apply_model],
@@ -255,36 +342,41 @@ class GradioInterface:
         try:
             input_data = PromptInput(text=prompt, meta_prompt_choice=meta_prompt_choice)
             result = self.prompt_refiner.refine_prompt(input_data)
+
+             # Include system prompt in output
+            system_prompt = str(result.system_prompt) if result.system_prompt else ""
             
             # 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
+            # Create response 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 ""
+                "initial_prompt_evaluation": str(result.initial_prompt_evaluation) if result.initial_prompt_evaluation else "",
+                "refined_prompt": str(result.refined_prompt) if result.refined_prompt else "",
+                "explanation_of_refinements": str(result.explanation_of_refinements) if result.explanation_of_refinements else "",
+                "raw_content": str(result.raw_content) if result.raw_content else "",
+                "system_prompt": system_prompt
             }
             
             return (
                 analysis_evaluation,
                 refined_prompt,
                 explanation_refinements,
-                full_response
+                full_response,
+                system_prompt
             )
         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": ""
+                "raw_content": "",
+                "system_prompt": ""
             }
-            return "", "", "", error_response
+            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)