app.py

import gradio as gr
import os
from groq import Groq

GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY environment variable not set")

client = Groq(api_key=GROQ_API_KEY)
MODEL = "llama-3.3-70b-versatile"

print(f"✅ Groq client initialized with model: {MODEL}")

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:
1. Analyze its weaknesses (vagueness, missing context, unclear output format, etc.)
2. Rewrite it as an optimized version applying best practices
3. Explain what changes you made and why

## Optimization Techniques to Apply:

### 1. Clarity & Specificity
- Replace vague terms with specific ones
- Add necessary context the AI needs
- Define any ambiguous terms

### 2. Role/Persona Framing
- Add "You are a [relevant expert]..." when it would improve response quality
- Match expertise level to the task

### 3. Output Format Instructions
- Specify desired format (bullet points, paragraphs, table, JSON, etc.)
- Include length guidance if appropriate
- Request specific sections or structure

### 4. Constraints & Guardrails
- Add what to include AND what to exclude
- Specify tone (professional, casual, technical, etc.)
- Set boundaries for scope

### 5. Task Decomposition
- Break complex requests into numbered steps
- Add "Think step by step" for reasoning tasks
- Sequence multi-part requests logically

## Response Format:

You MUST respond in this exact format:

**ANALYSIS:**
[2-3 sentences identifying the main weaknesses of the original prompt]

**OPTIMIZED PROMPT:**
[The complete rewritten prompt - ready to copy and use]

**CHANGES MADE:**
- [Change 1]: [Brief explanation why]
- [Change 2]: [Brief explanation why]
- [Change 3]: [Brief explanation why]
[Continue for all significant changes]

## Important Rules:
- The optimized prompt should be self-contained (user can copy-paste it directly)
- Don't over-engineer simple prompts - match complexity to the task
- Preserve the user's original intent completely
- Keep the optimized prompt concise but complete"""


def optimize_prompt(user_prompt: str, context: str = "") -> dict:
    if not user_prompt.strip():
        return {
            "analysis": "No prompt provided.",
            "optimized_prompt": "",
            "changes": "",
            "raw_response": ""
        }
    
    user_message = "Please optimize this prompt:\n\n" + user_prompt
    
    if context.strip():
        user_message += "\n\nAdditional context: " + context
    
    response = client.chat.completions.create(
        model=MODEL,
        messages=[
            {"role": "system", "content": OPTIMIZER_SYSTEM_PROMPT},
            {"role": "user", "content": user_message}
        ],
        max_tokens=2000,
        temperature=0.7
    )
    
    raw_response = response.choices[0].message.content
    
    result = {
        "analysis": "",
        "optimized_prompt": "",
        "changes": "",
        "raw_response": raw_response
    }
    
    if "**ANALYSIS:**" in raw_response:
        parts = raw_response.split("**ANALYSIS:**")
        if len(parts) > 1:
            remainder = parts[1]
            
            if "**OPTIMIZED PROMPT:**" in remainder:
                analysis_part, remainder = remainder.split("**OPTIMIZED PROMPT:**", 1)
                result["analysis"] = analysis_part.strip()
                
                if "**CHANGES MADE:**" in remainder:
                    prompt_part, changes_part = remainder.split("**CHANGES MADE:**", 1)
                    result["optimized_prompt"] = prompt_part.strip()
                    result["changes"] = changes_part.strip()
                else:
                    result["optimized_prompt"] = remainder.strip()
    
    if not result["optimized_prompt"]:
        result["optimized_prompt"] = raw_response
        result["analysis"] = "Could not parse structured response."
    
    return result


def process_optimization(prompt: str, context: str) -> tuple:
    if not prompt.strip():
        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)}", "", "")


EXAMPLES = [
    ["write about dogs", ""],
    ["help me with my code", ""],
    ["explain quantum computing", "For a blog post aimed at beginners"],
    ["write an email to my boss", "Requesting time off next week"],
    ["create a marketing plan", "For a new mobile app startup"],
    ["summarize this article", ""],
    ["give me recipe ideas", "Vegetarian, quick weeknight meals"],
    ["help me prepare for interview", "Software engineering position at Google"],
]

demo = gr.Blocks()

with demo:
    gr.Markdown("""
    # ✨ Prompt Optimizer
    
    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:**
    - 🎯 Clarity & Specificity
    - 🎭 Role/Persona Framing
    - 📋 Output Format Instructions
    - 🚧 Constraints & Guardrails
    - 🔢 Task Decomposition
    """)
    
    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...",
                lines=2
            )
            optimize_btn = gr.Button("✨ Optimize Prompt", variant="primary")
            gr.Markdown("### 💡 Example Prompts")
            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(
        fn=process_optimization,
        inputs=[prompt_input, context_input],
        outputs=[analysis_output, optimized_output, changes_output]
    )
    
    with gr.Accordion("📚 Prompt Engineering Tips", open=False):
        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"
        
        **2. Define the Output Format** - ❌ "Give me some ideas" → ✅ "Give me 5 ideas as a numbered list"
        
        **3. Set the Role/Persona** - ❌ "Explain machine learning" → ✅ "You are a CS professor. Explain ML to a first-year student"
        
        **4. Add Constraints** - ❌ "Write a story" → ✅ "Write a 300-word short story set in Tokyo with a surprise ending"
        
        **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("""
        | Component | Details |
        |-----------|---------|
        | **LLM Backend** | Groq API |
        | **Model** | Llama 3.3 70B Versatile |
        | **Optimization Techniques** | 5 (Clarity, Role, Format, Constraints, Decomposition) |
        """)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)