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app.py

@@ -2,11 +2,6 @@ import gradio as gr
 import os
 from groq import Groq
 
-# =============================================================================
-# Prompt Optimizer
-# =============================================================================
-
-# Initialize Groq client
 GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
 if not GROQ_API_KEY:
     raise ValueError("GROQ_API_KEY environment variable not set")
@@ -16,7 +11,6 @@ MODEL = "llama-3.3-70b-versatile"
 
 print(f"✅ Groq client initialized with model: {MODEL}")
 
-# ----- Optimizer System Prompt -----
 OPTIMIZER_SYSTEM_PROMPT = """You are an expert prompt engineer. Your task is to analyze and optimize user prompts to get better results from AI language models.
 
 When given a prompt, you will:
@@ -74,7 +68,6 @@ You MUST respond in this exact format:
 
 
 def optimize_prompt(user_prompt: str, context: str = "") -> dict:
-    """Optimize a user's prompt using LLM analysis."""
     if not user_prompt.strip():
         return {
             "analysis": "No prompt provided.",
@@ -131,32 +124,19 @@ def optimize_prompt(user_prompt: str, context: str = "") -> dict:
 
 
 def process_optimization(prompt: str, context: str) -> tuple:
-    """Process the optimization request and return formatted outputs."""
     if not prompt.strip():
-        return (
-            "⚠️ Please enter a prompt to optimize.",
-            "",
-            ""
-        )
+        return ("⚠️ Please enter a prompt to optimize.", "", "")
     
     try:
         result = optimize_prompt(prompt, context)
-        
         analysis = "### 🔍 Analysis\n\n" + result['analysis']
         optimized = result['optimized_prompt']
         changes = "### 📝 Changes Made\n\n" + result['changes']
-        
         return analysis, optimized, changes
-    
     except Exception as e:
-        return (
-            f"❌ Error: {str(e)}",
-            "",
-            ""
-        )
+        return (f"❌ Error: {str(e)}", "", "")
 
 
-# ----- Example Prompts -----
 EXAMPLES = [
     ["write about dogs", ""],
     ["help me with my code", ""],
@@ -168,7 +148,6 @@ EXAMPLES = [
     ["help me prepare for interview", "Software engineering position at Google"],
 ]
 
-# ----- Gradio Interface -----
 demo = gr.Blocks()
 
 with demo:
@@ -177,6 +156,8 @@ with demo:
     
     Transform basic prompts into powerful, well-structured instructions that get better results from AI.
     
+    [![Powered by Groq](https://console.groq.com/powered-by-groq-dark.svg)](https://groq.com)
+    
     **How it works:** Enter your rough prompt → AI analyzes weaknesses → Returns an optimized version with explanations.
     
     **Optimization Techniques Applied:**
@@ -185,47 +166,34 @@ with demo:
     - 📋 Output Format Instructions
     - 🚧 Constraints & Guardrails
     - 🔢 Task Decomposition
-    
-    [![Powered by Groq](https://console.groq.com/powered-by-groq-dark.svg)](https://groq.com)
     """)
     
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### 📝 Input")
-            
             prompt_input = gr.Textbox(
                 label="Your Prompt",
                 placeholder="Enter the prompt you want to optimize...\n\nExample: 'write about dogs'",
                 lines=4
             )
-            
             context_input = gr.Textbox(
                 label="Additional Context (Optional)",
-                placeholder="Any extra context about how you'll use this prompt...\n\nExample: 'For a children's educational website'",
+                placeholder="Any extra context about how you'll use this prompt...",
                 lines=2
             )
-            
             optimize_btn = gr.Button("✨ Optimize Prompt", variant="primary")
-            
             gr.Markdown("### 💡 Example Prompts")
-            gr.Examples(
-                examples=EXAMPLES,
-                inputs=[prompt_input, context_input],
-                label=""
-            )
+            gr.Examples(examples=EXAMPLES, inputs=[prompt_input, context_input], label="")
         
         with gr.Column(scale=1):
             gr.Markdown("### 🎯 Results")
-            
             analysis_output = gr.Markdown(label="Analysis")
-            
             optimized_output = gr.Textbox(
                 label="Optimized Prompt (Ready to Copy)",
                 lines=10,
                 show_copy_button=True,
                 interactive=False
             )
-            
             changes_output = gr.Markdown(label="Changes Made")
     
     optimize_btn.click(
@@ -238,31 +206,16 @@ with demo:
         gr.Markdown("""
         ### Best Practices for Writing Prompts
         
-        **1. Be Specific**
-        - ❌ "Write about history"
-        - ✅ "Write a 500-word overview of the causes of World War I, focusing on the alliance system and nationalism"
-        
-        **2. Define the Output Format**
-        - ❌ "Give me some ideas"
-        - ✅ "Give me 5 ideas as a numbered list, with a one-sentence explanation for each"
+        **1. Be Specific** - ❌ "Write about history" → ✅ "Write a 500-word overview of the causes of World War I"
         
-        **3. Set the Role/Persona**
-        - ❌ "Explain machine learning"
-        - ✅ "You are a computer science professor. Explain machine learning to a first-year undergraduate student"
+        **2. Define the Output Format** - ❌ "Give me some ideas" → ✅ "Give me 5 ideas as a numbered list"
         
-        **4. Add Constraints**
-        - ❌ "Write a story"
-        - ✅ "Write a 300-word short story set in Tokyo, featuring a detective, with a surprise ending. Avoid clichés."
+        **3. Set the Role/Persona** - ❌ "Explain machine learning" → ✅ "You are a CS professor. Explain ML to a first-year student"
         
-        **5. Break Down Complex Tasks**
-        - ❌ "Analyze this data and give recommendations"
-        - ✅ "1) Summarize the key trends in the data. 2) Identify the top 3 issues. 3) Provide actionable recommendations for each issue."
+        **4. Add Constraints** - ❌ "Write a story" → ✅ "Write a 300-word short story set in Tokyo with a surprise ending"
         
-        ### Resources
-        - [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering)
-        - [Anthropic Prompt Engineering](https://docs.anthropic.com/claude/docs/prompt-engineering)
-        """
-        )
+        **5. Break Down Complex Tasks** - ❌ "Analyze this data" → ✅ "1) Summarize trends. 2) Identify issues. 3) Provide recommendations."
+        """)
     
     with gr.Accordion("🔧 Technical Details", open=False):
         gr.Markdown("""
@@ -271,9 +224,7 @@ with demo:
         | **LLM Backend** | Groq API |
         | **Model** | Llama 3.3 70B Versatile |
         | **Optimization Techniques** | 5 (Clarity, Role, Format, Constraints, Decomposition) |
-        | **Response Parsing** | Structured sections (Analysis, Optimized, Changes) |
-        """
-        )
+        """)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)