scientific-diagram-generation

Scientific Diagram Generation

AI-powered scientific illustration generation using Gemini Image models. Creates publication-quality mechanism diagrams, pathway illustrations, and scientific figures.

API Configuration

API Call Structure

curl -X POST "${GEMINI_BASE_URL:-https://generativelanguage.googleapis.com/v1beta}/models/gemini-3.1-flash-image-preview:generateContent" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LLM_API_KEY" \
  -d '{
    "contents": [{"role": "user", "parts": [{"text": "YOUR_PROMPT_HERE"}]}],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"]
    }
  }'

Python Implementation

import httpx, base64

API_KEY = "your-gemini-key"
MODEL = "gemini-3.1-flash-image-preview"
BASE = os.environ.get("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta")
URL = f"{BASE}/models/{MODEL}:generateContent"

async def generate(prompt: str) -> bytes:
    payload = {
        "contents": [{"role": "user", "parts": [{"text": prompt}]}],
        "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]}
    }
    async with httpx.AsyncClient(timeout=120) as c:
        r = await c.post(URL, json=payload,
            headers={"Content-Type": "application/json",
                     "Authorization": f"Bearer {API_KEY}"})
        r.raise_for_status()
        for cand in r.json().get("candidates", []):
            for part in cand["content"]["parts"]:
                if "inlineData" in part:
                    return base64.b64decode(part["inlineData"]["data"])
    return b""