Nano Banana Pro Text-to-Image

curl -X POST "https://api.yelinai.com/v1/chat/completions" \
     -H "Authorization: Bearer $API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
    "model": "gemini-3-pro-image-preview",
    "stream": false,
    "messages": [
        {
            "role": "user",
            "content": "a beautiful sunset over mountains"
        }
    ]
}'

from openai import OpenAI
import base64
import re

client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://api.yelinai.com/v1"
)

response = client.chat.completions.create(
    model="gemini-3-pro-image-preview",
    messages=[
        {
            "role": "user",
            "content": "a beautiful sunset over mountains"
        }
    ]
)

# Extract base64 image data
content = response.choices[0].message.content
match = re.search(r'!\[.*?\]\((data:image/png;base64,.*?)\)', content)

if match:
    base64_data = match.group(1).split(',')[1]
    image_data = base64.b64decode(base64_data)

    with open('output.png', 'wb') as f:
        f.write(image_data)
    print("✅ Image saved: output.png")

#!/bin/bash

# 1. Set API Key
export API_KEY="sk-YOUR_API_KEY"

# 2. Send request (Generate 4K image with Nano Banana Pro)
curl -s -X POST "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "parts": [
        {"text": "A futuristic city skyline at sunset, high detailed, 4k"}
      ]
    }],
    "generationConfig": {
      "responseModalities": ["IMAGE"],
      "imageConfig": {
        "aspectRatio": "16:9",
        "imageSize": "4K"
      }
    }
  }' \
  | jq -r '.candidates[0].content.parts[0].inlineData.data' \
  | base64 --decode > output_4k.png

echo "✅ Image saved: output_4k.png"

import requests
import base64

# ========== Configuration ==========
API_KEY = "sk-YOUR_API_KEY"
API_URL = "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"
PROMPT = "A cute orange cat"
ASPECT_RATIO = "1:1"
IMAGE_SIZE = "2K"  # Nano Banana Pro supports: 1K, 2K, 4K
# ============================

headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

payload = {
    "contents": [{"parts": [{"text": PROMPT}]}],
    "generationConfig": {
        "responseModalities": ["IMAGE"],
        "imageConfig": {
            "aspectRatio": ASPECT_RATIO,
            "imageSize": IMAGE_SIZE
        }
    }
}

response = requests.post(API_URL, headers=headers, json=payload, timeout=180)
result = response.json()

# Save image
image_data = result["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_data))

print("✅ Image saved: output.png")

import requests
import base64

# ========== Configuration ==========
API_KEY = "sk-YOUR_API_KEY"
API_URL = "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"

INPUT_IMAGE = "output.png"  # Use image from Example 1
PROMPT = "Transform this image into Van Gogh Starry Night style oil painting"
ASPECT_RATIO = "1:1"
IMAGE_SIZE = "2K"
# ============================

# Read and encode image
with open(INPUT_IMAGE, "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode("utf-8")

headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

payload = {
    "contents": [{
        "parts": [
            {"text": PROMPT},
            {"inline_data": {"mime_type": "image/jpeg", "data": image_b64}}
        ]
    }],
    "generationConfig": {
        "responseModalities": ["IMAGE"],
        "imageConfig": {
            "aspectRatio": ASPECT_RATIO,
            "imageSize": IMAGE_SIZE
        }
    }
}

response = requests.post(API_URL, headers=headers, json=payload, timeout=180)
result = response.json()

# Save image
output_data = result["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
with open("output_styled.png", "wb") as f:
    f.write(base64.b64decode(output_data))

print("✅ Image saved: output_styled.png")

import requests
import base64

# ========== Configuration ==========
API_KEY = "sk-YOUR_API_KEY"
API_URL = "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"

# Use the two images from Example 1 and Example 2
IMAGES = ["output.png", "output_styled.png"]
PROMPT = "Fuse these two images into an artwork"
ASPECT_RATIO = "16:9"
IMAGE_SIZE = "2K"
# ============================

# Build parts: text + multiple images
parts = [{"text": PROMPT}]

for img_path in IMAGES:
    with open(img_path, "rb") as f:
        img_b64 = base64.b64encode(f.read()).decode("utf-8")
    parts.append({"inline_data": {"mime_type": "image/png", "data": img_b64}})

headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

payload = {
    "contents": [{"parts": parts}],
    "generationConfig": {
        "responseModalities": ["IMAGE"],
        "imageConfig": {
            "aspectRatio": ASPECT_RATIO,
            "imageSize": IMAGE_SIZE
        }
    }
}

response = requests.post(API_URL, headers=headers, json=payload, timeout=180)
result = response.json()

# Save image
output_data = result["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
with open("output_mixed.png", "wb") as f:
    f.write(base64.b64decode(output_data))

print("✅ Image saved: output_mixed.png")

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Nano Banana Pro Image Generation - Complete Demo Script
Includes three scenarios: text-to-image, single image-to-image, multi-image mix
"""

import requests
import base64
import os
from datetime import datetime

# ========== Configuration ==========
API_KEY = "sk-YOUR_API_KEY"
API_URL = "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"
# ============================

def generate_text_to_image(prompt, aspect_ratio="16:9", image_size="2K"):
    """Scenario 1: Text-to-Image"""
    print(f"\n📸 Text-to-Image: {prompt}")

    payload = {
        "contents": [{"parts": [{"text": prompt}]}],
        "generationConfig": {
            "responseModalities": ["IMAGE"],
            "imageConfig": {
                "aspectRatio": aspect_ratio,
                "imageSize": image_size
            }
        }
    }

    return call_api(payload, f"text_{image_size}")

def generate_image_to_image(input_image, prompt, aspect_ratio="1:1", image_size="2K"):
    """Scenario 2: Single Image-to-Image"""
    print(f"\n🎨 Image-to-Image: {input_image}")

    with open(input_image, "rb") as f:
        img_b64 = base64.b64encode(f.read()).decode("utf-8")

    payload = {
        "contents": [{
            "parts": [
                {"text": prompt},
                {"inline_data": {"mime_type": "image/jpeg", "data": img_b64}}
            ]
        }],
        "generationConfig": {
            "responseModalities": ["IMAGE"],
            "imageConfig": {
                "aspectRatio": aspect_ratio,
                "imageSize": image_size
            }
        }
    }

    return call_api(payload, f"styled_{image_size}")

def generate_multi_image_mix(images, prompt, aspect_ratio="16:9", image_size="2K"):
    """Scenario 3: Multi-Image Mix"""
    print(f"\n🖼️  Multi-Image Mix: {len(images)} images")

    parts = [{"text": prompt}]
    for img in images:
        with open(img, "rb") as f:
            parts.append({"inline_data": {"mime_type": "image/jpeg", "data": base64.b64encode(f.read()).decode()}})

    payload = {
        "contents": [{"parts": parts}],
        "generationConfig": {
            "responseModalities": ["IMAGE"],
            "imageConfig": {
                "aspectRatio": aspect_ratio,
                "imageSize": image_size
            }
        }
    }

    return call_api(payload, f"mixed_{image_size}")

def call_api(payload, prefix):
    """Call API"""
    headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=180)
        result = response.json()

        image_data = result["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
        filename = f"{prefix}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"

        with open(filename, "wb") as f:
            f.write(base64.b64decode(image_data))

        print(f"✅ Saved: {filename}")
        return filename
    except Exception as e:
        print(f"❌ Error: {e}")
        return None

# ========== Main Program ==========
def main():
    print("🎨 Nano Banana Pro Image Generation - Complete Demo")

    # Scenario 1: Text-to-Image
    img1 = generate_text_to_image(
        "A futuristic cyberpunk city at night, 4k",
        aspect_ratio="16:9",
        image_size="4K"
    )

    # Scenario 2: Image-to-Image (requires test_cat.jpg)
    if os.path.exists("test_cat.jpg"):
        img2 = generate_image_to_image(
            "test_cat.jpg",
            "Van Gogh Starry Night style",
            aspect_ratio="1:1",
            image_size="2K"
        )

    # Scenario 3: Multi-Image Mix (requires test_cat.jpg, test_apple.jpg)
    if os.path.exists("test_cat.jpg") and os.path.exists("test_apple.jpg"):
        generate_multi_image_mix(
            ["test_cat.jpg", "test_apple.jpg"],
            "A cat eating an apple",
            aspect_ratio="16:9",
            image_size="2K"
        )

    print("\n✅ Demo complete!")

if __name__ == "__main__":
    main()

#!/bin/bash

# ============================================================
# Nano Banana Pro Image Generation Tool - Bash/Curl Version
# Supports 4K resolution and multiple aspect ratios
# ============================================================

# ========== Configuration ==========
API_KEY="sk-YOUR_API_KEY"
API_URL="https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"
PROMPT="A futuristic cyberpunk city at night, neon lights, flying cars, highly detailed, 4k"
ASPECT_RATIO="16:9"
IMAGE_SIZE="4K"  # 1K, 2K, 4K

OUTPUT_FILE="gemini_\`$\{IMAGE_SIZE}_$(date +%Y%m%d_%H%M%S).png"
# ============================

# Check dependencies
if ! command -v jq &> /dev/null; then
    echo "❌ Error: jq tool required"
    echo ""
    echo "Installation:"
    echo "  macOS:   brew install jq"
    echo "  Ubuntu:  sudo apt-get install jq"
    echo "  CentOS:  sudo yum install jq"
    exit 1
fi

echo "============================================================"
echo "Nano Banana Pro Image Generation Tool"
echo "============================================================"
echo "⏰ Start time: $(date '+%Y-%m-%d %H:%M:%S')"
echo "🚀 Generating image..."
echo "📝 Prompt: \`$\{PROMPT}"
echo "📐 Aspect Ratio: \`$\{ASPECT_RATIO}"
echo "🖼️  Resolution: \`$\{IMAGE_SIZE}"

# Build JSON request
REQUEST_JSON=$(jq -n \
  --arg prompt "$PROMPT" \
  --arg ratio "$ASPECT_RATIO" \
  --arg size "$IMAGE_SIZE" \
  '{
    contents: [{
      parts: [{text: $prompt}]
    }],
    generationConfig: {
      responseModalities: ["IMAGE"],
      imageConfig: {
        aspectRatio: $ratio,
        imageSize: $size
      }
    }
  }')

# Send request
RESPONSE=$(curl -s -X POST "\`$\{API_URL}" \
  -H "Authorization: Bearer \`$\{API_KEY}" \
  -H "Content-Type: application/json" \
  -d "\`$\{REQUEST_JSON}")

# Check for errors
if echo "\`$\{RESPONSE}" | jq -e '.error' &> /dev/null; then
    echo "❌ Generation failed"
    echo "💥 Error message:"
    echo "\`$\{RESPONSE}" | jq -r '.error.message // .error'
    exit 1
fi

# Extract image data
echo "💾 Saving image..."
IMAGE_DATA=$(echo "\`$\{RESPONSE}" | jq -r '.candidates[0].content.parts[0].inlineData.data' 2>/dev/null)

if [ -z "$IMAGE_DATA" ] || [ "$IMAGE_DATA" = "null" ]; then
    echo "❌ No image data found"
    exit 1
fi

# Decode and save image
echo "\`$\{IMAGE_DATA}" | base64 --decode > "\`$\{OUTPUT_FILE}"

# Check result
if [ -f "\`$\{OUTPUT_FILE}" ]; then
    FILE_SIZE=$(du -h "\`$\{OUTPUT_FILE}" | cut -f1)
    echo "✅ Image saved: \`$\{OUTPUT_FILE}"
    echo "📊 File size: \`$\{FILE_SIZE}"
    echo "============================================================"
    echo "🎉 Generation successful!"
    echo "⏰ End time: $(date '+%Y-%m-%d %H:%M:%S')"
else
    echo "❌ Image save failed"
    exit 1
fi

curl -s -X POST "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart."}]}],
    "tools": [{"google_search": {}}],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {"aspectRatio": "16:9"}
    }
  }'

# Multi-Image Reference Example (Python)
import requests
import base64

API_KEY = "sk-YOUR_API_KEY"
API_URL = "https://api.yelinai.com/v1beta/models/gemini-3-pro-image-preview:generateContent"

# Prepare multiple reference images
image_paths = ["cat.jpg", "apple.jpg"]
parts = [{"text": "Combine these images: a cat eating an apple on a table"}]

for path in image_paths:
    with open(path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode("utf-8")
    parts.append({
        "inline_data": {
            "mime_type": "image/jpeg",
            "data": image_data
        }
    })

# Send request
response = requests.post(
    API_URL,
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "contents": [{"parts": parts}],
        "generationConfig": {
            "responseModalities": ["IMAGE"],
            "imageConfig": {
                "aspectRatio": "16:9",
                "imageSize": "2K"
            }
        }
    }
)

curl -X POST "https://api.yelinai.com/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{"model": "gemini-3-pro-image-preview", "messages": [{"role": "user", "content": "a cute cat"}]}'

{
  "generationConfig": {
    "responseModalities": ["IMAGE"],
    "imageConfig": {
      "aspectRatio": "16:9",
      "imageSize": "4K"
    }
  }
}

<img src="data:image/png;base64,iVBORw0KG..." />