Update app.py

app.py

@@ -150,134 +150,6 @@ class PromptRefiner:
             
         except Exception as e:
             return f"Error: {str(e)}"
-
-class GradioInterface:
-    def __init__(self, prompt_refiner: PromptRefiner):
-        self.prompt_refiner = prompt_refiner
-        custom_css = custom_css
-        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)",
-                   # elem_classes="no-background",
-                    #elem_classes="container2",
-                    lines=5
-                )
-                meta_prompt_choice = gr.Radio(
-                    ["star","done","physics","morphosis", "verse", "phor","bolism","math","arpe"],
-                    label="Choose Meta Prompt",
-                    value="star",
-                    elem_classes=["no-background", "radio-group"]
-                   # elem_classes=[ "radio-group"]
-                )
-                refine_button = gr.Button("Refine Prompt")  
-                
-                # Option 1: Put Examples here (before Meta Prompt explanation)
-                with gr.Row(elem_classes=["container2"]):
-                    with gr.Accordion("Examples", open=False):
-                        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 ?", "arpe"],                    
-                            ],
-                            inputs=[prompt_text, meta_prompt_choice]
-                        )
-                       
-                    with gr.Accordion("Meta Prompt explanation", open=False):
-                        gr.Markdown(explanation_markdown)
-                
-
-                
-                # Option 2: Or put Examples here (after the button)
-                # with gr.Accordion("Examples", open=False):
-                #     gr.Examples(...)
-                
-            with gr.Column(elem_classes=["container", "analysis-container"]):
-                gr.Markdown(' ')
-                gr.Markdown("### Initial prompt analysis")
-                analysis_evaluation = gr.Markdown()
-                gr.Markdown("### Refined Prompt")
-                refined_prompt = gr.Textbox(
-                                    label="Refined Prompt",
-                                    interactive=True,
-                                    show_label=True,  # Must be True for copy button to show
-                                    show_copy_button=True,  # Adds the copy button
-                             #       elem_classes="no-background"
-                                )
-                gr.Markdown("### Explanation of Refinements")
-                explanation_of_refinements = gr.Markdown()
-            
-
-            with gr.Column(elem_classes=["container", "model-container"]):
-              #  gr.Markdown("## See MetaPrompt Impact")            
-                with gr.Row():
-                    apply_model = gr.Dropdown(models,
-                                            value="meta-llama/Llama-3.1-8B-Instruct",
-                                            label="Choose the Model",
-                                            container=False,  # This removes the container around the dropdown
-                                            scale=1,         # Controls the width relative to other components
-                                            min_width=300    # Sets minimum width in pixels
-                                         #   elem_classes="no-background"
-                                        )
-                    apply_button = gr.Button("Apply MetaPrompt")
-
-          #  with gr.Column(elem_classes=["container", "results-container"]):
-                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()
-                
-            refine_button.click(
-                fn=self.refine_prompt,
-                inputs=[prompt_text, meta_prompt_choice],
-                outputs=[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]
-            )
-
-    def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
-        input_data = PromptInput(text=prompt, meta_prompt_choice=meta_prompt_choice)
-        # Since result is a tuple with 4 elements based on the return value of prompt_refiner.refine_prompt
-        initial_prompt_evaluation, refined_prompt, explanation_refinements, full_response = self.prompt_refiner.refine_prompt(input_data)
-        
-        analysis_evaluation = f"\n\n{initial_prompt_evaluation}"
-        return (
-            analysis_evaluation,
-            refined_prompt,
-            explanation_refinements,
-            full_response
-        )
-
-    def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
-        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
-
-    def launch(self, share=False):
-        self.interface.launch(share=share)
-
-
 custom_css = """
 .container {
     border: 2px solid #2196F3;
@@ -451,6 +323,134 @@ custom_css = """
     border-radius: 8px !important;
 }
 """
+class GradioInterface:
+    def __init__(self, prompt_refiner: PromptRefiner):
+        self.prompt_refiner = prompt_refiner
+        custom_css = custom_css
+        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)",
+                   # elem_classes="no-background",
+                    #elem_classes="container2",
+                    lines=5
+                )
+                meta_prompt_choice = gr.Radio(
+                    ["star","done","physics","morphosis", "verse", "phor","bolism","math","arpe"],
+                    label="Choose Meta Prompt",
+                    value="star",
+                    elem_classes=["no-background", "radio-group"]
+                   # elem_classes=[ "radio-group"]
+                )
+                refine_button = gr.Button("Refine Prompt")  
+                
+                # Option 1: Put Examples here (before Meta Prompt explanation)
+                with gr.Row(elem_classes=["container2"]):
+                    with gr.Accordion("Examples", open=False):
+                        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 ?", "arpe"],                    
+                            ],
+                            inputs=[prompt_text, meta_prompt_choice]
+                        )
+                       
+                    with gr.Accordion("Meta Prompt explanation", open=False):
+                        gr.Markdown(explanation_markdown)
+                
+
+                
+                # Option 2: Or put Examples here (after the button)
+                # with gr.Accordion("Examples", open=False):
+                #     gr.Examples(...)
+                
+            with gr.Column(elem_classes=["container", "analysis-container"]):
+                gr.Markdown(' ')
+                gr.Markdown("### Initial prompt analysis")
+                analysis_evaluation = gr.Markdown()
+                gr.Markdown("### Refined Prompt")
+                refined_prompt = gr.Textbox(
+                                    label="Refined Prompt",
+                                    interactive=True,
+                                    show_label=True,  # Must be True for copy button to show
+                                    show_copy_button=True,  # Adds the copy button
+                             #       elem_classes="no-background"
+                                )
+                gr.Markdown("### Explanation of Refinements")
+                explanation_of_refinements = gr.Markdown()
+            
+
+            with gr.Column(elem_classes=["container", "model-container"]):
+              #  gr.Markdown("## See MetaPrompt Impact")            
+                with gr.Row():
+                    apply_model = gr.Dropdown(models,
+                                            value="meta-llama/Llama-3.1-8B-Instruct",
+                                            label="Choose the Model",
+                                            container=False,  # This removes the container around the dropdown
+                                            scale=1,         # Controls the width relative to other components
+                                            min_width=300    # Sets minimum width in pixels
+                                         #   elem_classes="no-background"
+                                        )
+                    apply_button = gr.Button("Apply MetaPrompt")
+
+          #  with gr.Column(elem_classes=["container", "results-container"]):
+                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()
+                
+            refine_button.click(
+                fn=self.refine_prompt,
+                inputs=[prompt_text, meta_prompt_choice],
+                outputs=[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]
+            )
+
+    def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> tuple:
+        input_data = PromptInput(text=prompt, meta_prompt_choice=meta_prompt_choice)
+        # Since result is a tuple with 4 elements based on the return value of prompt_refiner.refine_prompt
+        initial_prompt_evaluation, refined_prompt, explanation_refinements, full_response = self.prompt_refiner.refine_prompt(input_data)
+        
+        analysis_evaluation = f"\n\n{initial_prompt_evaluation}"
+        return (
+            analysis_evaluation,
+            refined_prompt,
+            explanation_refinements,
+            full_response
+        )
+
+    def apply_prompts(self, original_prompt: str, refined_prompt: str, model: str):
+        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
+
+    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.",