from fastapi import FastAPI, Depends, HTTPException from fastapi.responses import HTMLResponse, Response from pydantic import BaseModel from google import genai from google.genai import types import requests from cryptography.fernet import Fernet import os import uuid Url = os.getenv('URL') Api_key = os.getenv('API_KEY') Key = os.getenv('KEY') System_instruction = os.getenv('System_instruction') AGENTS_MD_CONTENT = os.getenv('AGENTS_MD_CONTENT') client = genai.Client(api_key=Api_key) cipher = Fernet(Key.encode()) print(Key) app = FastAPI() class InputPrompt(BaseModel): input_prompt: str @app.post("/optimize") async def optimize_text(prompt: InputPrompt): unique_id = str(uuid.uuid4()) optimized_text = gen(prompt.input_prompt) url = Url data = { "id": unique_id, "a": prompt.input_prompt, "b": optimized_text } encrypted_data = {k: cipher.encrypt(v.encode()).decode() for k, v in data.items()} response = requests.post(url, json=encrypted_data) return {"id": unique_id, "optimized_text": optimized_text} def gen(prompt): try: response = client.models.generate_content( model="gemma-4-26b-a4b-it", contents=[ types.Content(role="system", parts=[types.Part(text=System_instruction)]), types.Content(role="user", parts=[types.Part(text=prompt)]) ], config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_level="MINIMAL") ), ) return response.text.rstrip() except Exception as e: print(f"GenAI Error: {e}") raise HTTPException(status_code=500, detail="AI Generation Failed") class VoteInput(BaseModel): unique_id: str is_upvote: bool @app.post("/vote") async def vote(vote_input: VoteInput): data = {"id": vote_input.unique_id, "vote": str(vote_input.is_upvote)} try: response = requests.post(f"{Url}/vote", json=data, timeout=10) response.raise_for_status() except requests.RequestException as e: raise HTTPException(status_code=502, detail="Invalid") return {"status": "success"} @app.get("/agents.md") async def get_agents_md(): return Response(content=AGENTS_MD_CONTENT, media_type="text/markdown") @app.get("/robots.txt") async def get_robots_txt(): robots_content = ( "User-agent: *\n" "Allow: /\n" "Allow: /agents.md\n" "Disallow: /optimize\n" "Disallow: /vote\n" ) return Response(content=robots_content, media_type="text/plain") @app.get("/", response_class=HTMLResponse) async def read_items(): html_content = """ Prompt Optimizer — Paste a Rough Prompt, Get It Fixed in Seconds (Free, No Sign-Up)

Prompt Optimizer✎ paper edition

Get API
Hi! Got a rough prompt? ✎ Paste it below — let's tighten it up.
How it works & FAQ

How Prompt Optimizer works

  1. Paste your rough prompt. No template to pick, no form fields — just plain text, exactly as messy as it already is.
  2. Get a rewritten version back. The tool adds the missing structure: clearer intent, constraints, tone, and format.
  3. Copy and use it anywhere. The optimized prompt works with ChatGPT, Claude, Gemini, or any other AI model.

Frequently asked questions

Is Prompt Optimizer free to use?

Yes. It's completely free, with no account or signup required. Just paste your prompt and get an optimized version back.

Which AI models does the optimized prompt work with?

The optimized prompt is model-agnostic, so it works well with ChatGPT, Claude, Gemini, and most other large language models.

Do I need to fill out a template or form?

No. Unlike many prompt tools, there's no template picker or form to fill out. You paste your rough prompt as plain text and get a rewritten version back.

What does a prompt optimizer actually do?

It takes a vague or incomplete prompt and rewrites it to add missing structure — clearer intent, constraints, tone, and format — so the AI model produces a more accurate and useful response.

""" return html_content