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import os
import json
import google.generativeai as genai
from io import BytesIO
from PIL import Image
import torch
from contextlib import contextmanager
p = os.path.dirname(os.path.realpath(__file__))
def get_config():
try:
config_path = os.path.join(p, 'config.json')
with open(config_path, 'r') as f:
config = json.load(f)
return config
except:
return {}
def save_config(config):
config_path = os.path.join(p, 'config.json')
with open(config_path, 'w') as f:
json.dump(config, f, indent=4)
@contextmanager
def temporary_env_var(key: str, new_value):
old_value = os.environ.get(key)
if new_value is not None:
os.environ[key] = new_value
elif key in os.environ:
del os.environ[key]
try:
yield
finally:
if old_value is not None:
os.environ[key] = old_value
elif key in os.environ:
del os.environ[key]
class Gemini_Flash:
def __init__(self, api_key=None, proxy=None):
config = get_config()
self.api_key = api_key or config.get("GEMINI_API_KEY")
self.proxy = proxy or config.get("PROXY")
if self.api_key is not None:
self.configure_genai()
def configure_genai(self):
genai.configure(api_key=self.api_key, transport='rest')
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"prompt": ("STRING", {"default": "Analyze the image and make a txt2img detailed prompt. no prefix!", "multiline": True}),
"vision": ("BOOLEAN", {"default": True}),
"api_key": ("STRING", {"default": ""}),
"proxy": ("STRING", {"default": ""})
},
"optional": {
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = "generate_content"
CATEGORY = "Gemini flash"
def tensor_to_image(self, tensor):
tensor = tensor.cpu()
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
image = Image.fromarray(image_np, mode='RGB')
return image
def generate_content(self, prompt, vision, api_key, proxy, image=None):
config_updated = False
if api_key and api_key != self.api_key:
self.api_key = api_key
config_updated = True
if proxy != self.proxy:
self.proxy = proxy
config_updated = True
if config_updated:
save_config({"GEMINI_API_KEY": self.api_key, "PROXY": self.proxy})
self.configure_genai()
if not self.api_key:
raise ValueError("API key is required")
model_name = 'gemini-1.5-flash'
model = genai.GenerativeModel(model_name)
with temporary_env_var('HTTP_PROXY', self.proxy), temporary_env_var('HTTPS_PROXY', self.proxy):
try:
if not vision:
# Act like a text LLM
response = model.generate_content(prompt)
textoutput = response.text
else:
# Vision enabled
if image is None:
raise ValueError(f"{model_name} needs image")
else:
pil_image = self.tensor_to_image(image)
response = model.generate_content([prompt, pil_image])
textoutput = response.text
except Exception as e:
textoutput = f"Error: {str(e)}"
return (textoutput,)
NODE_CLASS_MAPPINGS = {
"Gemini_Flash": Gemini_Flash,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Gemini_Flash": "Gemini flash",
} |