""" title: Nano GPT Image Gen author: Elliott Groves version: 1.0.1 date: 2024-09-27 description: Nano GPT Image Gen action button for openwebui. author_url: https://github.com/Orciotrox/Nano-GPT.com_OpenWebUI funding_url: https://github.com/Orciotrox/Nano-GPT.com_OpenWebUI nano_address: nano_1pkmodta8fg8ti39pr1doe1mjbwo8cu3c9mt5u38d73d5t57d9nmgmnheifk icon_url: 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""" #Nano GPT Pipeline https://openwebui.com/f/elliott/nano_gpt/ #Integrates nano-gpt.com into OpenWebUI for seamless text-based AI tasks, like document retrieval and generation. #Nano GPT Image Generation Button https://openwebui.com/f/elliott/nanoimages/ #Allows users to generate images from text prompts with nano-gpt.com. #Nano GPT Receive Nano Button https://openwebui.com/f/elliott/nanogpt_receive_nano/ #Automates receiving Nano cryptocurrency through OpenWebUI for use with nano-gpt.com. from pydantic import BaseModel, Field from typing import Optional, List import requests import asyncio class ImageModel(BaseModel): id: str name: str description: str cost: dict maxImages: int resolutions: list engine: str class Action: class Valves(BaseModel): NANO_GPT_API_KEY: str = Field( default="Your Nano GPT API Key", description="API key for authenticating requests to the Nano GPT API.", ) NAME_PREFIX: str = Field( default="NanoGPT-Image/", description="Prefix to be added before model names.", ) NANO_GPT_API_BASE_URL: str = Field( default="https://nano-gpt.com/api", description="Base URL for accessing Nano GPT API endpoints.", ) def __init__(self): self.valves = self.Valves() self.models = [] async def fetch_models(self) -> List[ImageModel]: try: headers = { "Authorization": f"Bearer {self.valves.NANO_GPT_API_KEY}", "Content-Type": "application/json", } response = requests.get( f"{self.valves.NANO_GPT_API_BASE_URL}/models", headers=headers ) response.raise_for_status() models = response.json().get("models", {}).get("image", {}) if not models: return [] return [ ImageModel( id=model_info.get("model"), name=model_info.get("name", model_key), description=model_info.get("description", ""), cost=model_info.get("cost", {}), maxImages=model_info.get("maxImages", 1), resolutions=[res["value"] for res in model_info.get("resolutions", [])], engine=model_info.get("engine", ""), ) for model_key, model_info in models.items() ] except requests.RequestException as e: print(f"Request failed: {e}") return [] async def action(self, body: dict, __user__=None, __event_emitter__=None, __event_call__=None) -> Optional[dict]: print(f"action: {__name__}") # Fetch available models models = await self.fetch_models() if not models: print("No models available for selection.") return # Display model selection options selection_text = "\n\n".join([f"{i + 1}. {model.name} - {model.description}" for i, model in enumerate(models)]) selection_data = { "type": "input", "data": { "title": "Nano GPT Image Models", "message": f"Input the number of the model you would like to use:\n\n{selection_text}", "placeholder": selection_text, }, } try: # Get user-selected model selected_index = await __event_call__(selection_data) if not selected_index or not (1 <= int(selected_index) <= len(models)): print("Invalid model selection.") return selected_model = models[int(selected_index) - 1] print(f"Selected Model: {selected_model.name}") # Get image width width_data = { "type": "input", "data": { "title": "Image Width", "message": "Enter the width of the image (default is 1024):", "placeholder": "1024", }, } width = await __event_call__(width_data) width = int(width) if width else 1024 # Get image height height_data = { "type": "input", "data": { "title": "Image Height", "message": "Enter the height of the image (default is 1024):", "placeholder": "1024", }, } height = await __event_call__(height_data) height = int(height) if height else 1024 # Get prompt for image generation prompt_data = { "type": "input", "data": { "title": "Image Prompt", "message": "Enter the prompt for image generation:", "placeholder": "A beautiful landscape", }, } image_prompt = await __event_call__(prompt_data) # Generate the image response_data = self.generate_image(image_prompt, selected_model.id, width, height) if not response_data or "data" not in response_data or not response_data["data"]: raise Exception("No image data in the response") image_data = response_data["data"][0].get("b64_json") if not isinstance(image_data, str): raise Exception("Invalid image data format") content = f"\n\n" await __event_emitter__({"type": "message", "data": {"content": content}}) await __event_emitter__({"type": "image", "data": {"content": image_data}}) if __event_emitter__: await __event_emitter__({"type": "status", "data": {"description": "Image Generated", "done": True}}) except Exception as e: error_message = f"Error processing image: {e}" print(error_message) if __event_emitter__: await __event_emitter__({"type": "status", "data": {"description": "Error Generating Image", "done": True}}) await __event_emitter__({"type": "message", "data": {"content": error_message}}) def generate_image(self, prompt, model_id, width, height): data = { "prompt": prompt, "model": model_id, "width": width, "height": height, } headers = { "x-api-key": self.valves.NANO_GPT_API_KEY, "Content-Type": "application/json", } response = requests.post( f"{self.valves.NANO_GPT_API_BASE_URL}/generate-image", headers=headers, json=data ) if response.status_code == 200: return response.json() else: print(f"Error Content: {response.text}") return None