import gradio as gr import openai import requests import os import fileinput from dotenv import load_dotenv import io import sys import json from PIL import Image from stability_sdk import client import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation title="najimino AI recipe generator" inputs_label="どんな料理か教えてくれれば，新しいレシピを考えます" outputs_label="najimino AIが返信をします" visual_outputs_label="料理のイメージ" description=""" - ※入出力の文字数は最大1000文字程度までを目安に入力してください。回答に20秒くらいかかります． """ article = """ """ load_dotenv() openai.api_key = os.getenv('OPENAI_API_KEY') os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443' stability_api = client.StabilityInference( key=os.getenv('STABILITY_KEY'), verbose=True, engine="stable-diffusion-512-v2-1", # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 # stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-diffusion-xl-beta-v2-2-2 stable-inpainting-v1-0 stable-inpainting-512-v2-0 ) # MODEL = "gpt-4" # MODEL = "gpt-3.5-turbo-16k" MODEL = "gpt-3.5-turbo-0613" def get_filetext(filename, cache={}): if filename in cache: # キャッシュに保存されている場合は、キャッシュからファイル内容を取得する return cache[filename] else: if not os.path.exists(filename): raise ValueError(f"ファイル '{filename}' が見つかりませんでした") with open(filename, "r") as f: text = f.read() # ファイル内容をキャッシュする cache[filename] = text return text class StabilityAI: @classmethod def generate_image(cls, visualize_prompt): print("visualize_prompt:"+visualize_prompt) answers = stability_api.generate( prompt=visualize_prompt, ) for resp in answers: for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: print("NSFW") if artifact.type == generation.ARTIFACT_IMAGE: img = Image.open(io.BytesIO(artifact.binary)) return img class OpenAI: @classmethod def chat_completion(cls, prompt, start_with=""): constraints = get_filetext(filename = "constraints.md") template = get_filetext(filename = "template.md") # ChatCompletion APIに渡すデータを定義する data = { "model": MODEL, "messages": [ {"role": "system", "content": constraints} ,{"role": "system", "content": template} ,{"role": "assistant", "content": "Sure!"} ,{"role": "user", "content": prompt} ,{"role": "assistant", "content": start_with} ], } # ChatCompletion APIを呼び出す response = requests.post( "https://api.openai.com/v1/chat/completions", headers={ "Content-Type": "application/json", "Authorization": f"Bearer {openai.api_key}" }, json=data ) # ChatCompletion APIから返された結果を取得する result = response.json() print(result) content = result["choices"][0]["message"]["content"].strip() visualize_prompt = content.split("### Prompt for Visual Expression\n\n")[1] #print("split_content:"+split_content) #if len(split_content) > 1: # visualize_prompt = split_content[1] #else: # visualize_prompt = "vacant dish" #print("visualize_prompt:"+visualize_prompt) answers = stability_api.generate( prompt=visualize_prompt, ) @classmethod def chat_completion_with_function(cls, prompt, messages, functions): print("prompt:"+prompt) response = openai.ChatCompletion.create( model=MODEL, messages=messages, functions=functions, function_call="auto" ) # ChatCompletion APIから返された結果を取得する message = response.choices[0].message print(json.dumps(message, indent=2)) return message class NajiminoAI: def __init__(self, user_message): self.user_message = user_message def generate_recipe_prompt(self): template = get_filetext(filename="template.md") prompt = f""" {self.user_message} --- 上記を元に、下記テンプレートを埋めてください。 --- {template} """ return prompt def format_recipe(self, lang, title, description, ingredients, instruction, comment_feelings_taste, explanation_to_blind_person, prompt_for_visual_expression_in_en): debug_message = f""" lang: {lang} title: {title} description: {description} ingredients: {ingredients} instruction: {instruction} comment_feelings_taste: {comment_feelings_taste} explanation_to_blind_person: {explanation_to_blind_person} prompt_for_visual_expression_in_en: {prompt_for_visual_expression_in_en} """ print(debug_message) return debug_message @classmethod def generate(cls, user_message): najiminoai = NajiminoAI(user_message) return najiminoai.generate_recipe() def generate_recipe(self): user_message = self.user_message constraints = get_filetext(filename = "constraints.md") messages = [ {"role": "system", "content": constraints} ,{"role": "user", "content": user_message} ] functions = [ { "name": "format_recipe", "description": "どんな料理か教えてくれれば，新しいレシピを考えます", "parameters": { "type": "object", "default": {}, "title": "The Schema of new recipe", "required": [ "lang", "title", "description", "ingredients", "instruction", "comment_feelings_taste", "explanation_to_blind_person", "prompt_for_visual_expression_in_en" ], "properties": { "lang": { "type": "string", "default": "ja", "title": "Language Schema", "examples": [ "ja" ] }, "title": { "type": "string", "default": "", "title": "Title of New Recipe.", "description": "Write your title of new recipe.", "examples": [ "グルテンフリーサバのお好み焼き" ] }, "description": { "type": "string", "default": "", "title": "Your New Recipe", "description": "Write new recipe and brainstorm and work out the details of every aspect of the new recipe.", "examples": [ "サバを使ったお好み焼きのレシピです。グルテンフリー仕様で作られているので、小麦粉を使わずに作ることができます。" ] }, "ingredients": { "type": "string", "default": "", "title": "Your Ingredients", "description": "Brainstorm the ingredients needed to cook a new recipe and come up with all the ingredients for the new recipe.", "examples": [ "・サバのフィレ 200g\n・卵 2個\n・キャベツ 1/4個\n・もやし 50g\n・紅しょうが（刻んだもの） 2片\n・ネギ（小口切り） 適量\n・酒大さじ2\n・しょうゆ 大さじ2\n・だし汁 100ml\n・片栗粉 大さじ2\n・サラダ油 お好みで" ] }, "instruction": { "type": "string", "default": "", "title": "Your Instruction", "description": "Instruct them to cook a new recipe, brainstorming and working out every aspect and detail of the new recipe.", "examples": [ "1. キャベツは粗みじん切りにし、もやしは石づきを取っておく。\n2. ボウルに卵を割り入れ、よく混ぜる。酒、しょうゆ、だし汁を加えてさらに混ぜる。\n3. キャベツ、もやし、紅しょうが、ネギを卵液に加え、さらに混ぜる。\n4. サバのフィレを食べやすい大きさに切り、片栗粉をまぶす。\n5. フライパンにサラダ油を熱し、サバを並べ入れる。両面をこんがり焼く。\n6. サバの上に卵液を流し入れ、蓋をして約5分ほど蒸し焼きにする。\n7. ひっくり返して、もう片面も蓋をして約5分ほど蒸し焼きにする。\n8. お好み焼きを取り出し、お好みでソースやマヨネーズをかけて完成です。" ] }, "comment_feelings_taste": { "type": "string", "default": "", "title": "Your Comment and Feelings, taste of new recipe.", "description": "Review commnet of new recipe and brainstorm every point of new recipe to fill the details.", "examples": [ "このグルテンフリーサバのお好み焼きは、サバの旨味とキャベツの甘みが絶妙にマッチしています。表面はサクサク、中はもちもちとした食感で、一度食べたらやみつきになる美味しさです。" ] }, "explanation_to_blind_person": { "type": "string", "default": "", "title": "Your Explanation to Blind Person", "description": "Review commnet of new recipe here to explain to the blind people more concretely in detail. Please brainstorm every point of new recipe to fill the details.", "examples": [ "このグルテンフリーサバのお好み焼きは、サバのフィレとキャベツを主な材料としています。まず、キャベツを細かく刻んでおきます。次に、卵をボウルに割り入れ、酒、しょうゆ、だし汁と一緒によく混ぜます。その後、刻んだキャベツ、もやし、紅しょうが、ネギを卵液に加えて混ぜます。サバのフィレは食べやすい大きさに切り、片栗粉をまぶしてから焼きます。フライパンにサラダ油を熱し、サバを焼きます。その上に卵液を流し入れ、蓋をして約5分蒸し焼きにします。ひっくり返して、もう片面も蓋をして約5分蒸し焼きにします。最後にお好み焼きを取り出し、ソースやマヨネーズをかけて完成です。" ] }, "prompt_for_visual_expression_in_en": { "type": "string", "default": "", "title": "The Schema of prompt for visual expression in English", "examples": [ "Imagine a delicious gluten-free okonomiyaki with mackerel. The okonomiyaki is crispy on the outside and chewy on the inside. It is topped with savory sauce and creamy mayonnaise, creating a mouthwatering visual. The dish is garnished with finely chopped green onions and red pickled ginger, adding a pop of color. The mackerel fillets are beautifully grilled and placed on top of the okonomiyaki, adding a touch of elegance. The dish is served on a traditional Japanese plate, completing the visual presentation." ] } } } } ] message = OpenAI.chat_completion_with_function(prompt=user_message, messages=messages, functions=functions) # resultからfunction_callを取り出す # message = result["function_call"] print(json.dumps(message, indent=2)) if message.get("function_call"): function_name = message["function_call"]["name"] args = json.loads(message["function_call"]["arguments"]) lang=args.get("lang") title=args.get("title") description=args.get("description") ingredients=args.get("ingredients") instruction=args.get("instruction") comment_feelings_taste=args.get("comment_feelings_taste") explanation_to_blind_person=args.get("explanation_to_blind_person") prompt_for_visual_expression_in_en=args.get("prompt_for_visual_expression_in_en") function_response = self.format_recipe( lang=lang, title=title, description=description, ingredients=ingredients, instruction=instruction, comment_feelings_taste=comment_feelings_taste, explanation_to_blind_person=explanation_to_blind_person, prompt_for_visual_expression_in_en=prompt_for_visual_expression_in_en ) answers = StabilityAI.generate_image(prompt_for_visual_expression_in_en) return [function_response, answers] def main(): iface = gr.Interface(fn=NajiminoAI.generate, inputs=gr.Textbox(label=inputs_label), outputs=[gr.Textbox(label=inputs_label), gr.Image(label=visual_outputs_label)], title=title, description=description, article=article, allow_flagging='never' ) iface.launch() if __name__ == '__main__': function = '' if len(sys.argv) > 1: function = sys.argv[1] if function == 'generate': NajiminoAI.generate("グルテンフリーの香ばしいサバのお好み焼き") elif function == 'generate_image': answers = StabilityAI.generate_image("Imagine a delicious gluten-free okonomiyaki with mackerel. The okonomiyaki is crispy on the outside and chewy on the inside. It is topped with savory sauce and creamy mayonnaise, creating a mouthwatering visual. The dish is garnished with finely chopped green onions and red pickled ginger, adding a pop of color. The mackerel fillets are beautifully grilled and placed on top of the okonomiyaki, adding a touch of elegance. The dish is served on a traditional Japanese plate, completing the visual presentation.") print(answers) # import PIL # answersが何のクラス確認する if type(answers) == PIL.PngImagePlugin.PngImageFile: #save image answers.save("image.png") else: main()