app.py

import gradio as gr
import openai
import requests
import os
import fileinput
from dotenv import load_dotenv
import io
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation

title="ochyAI 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="ochyAI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。解答に120秒くらいかかります．エラーが出た場合はログを開いてエラーメッセージを送ってくれるとochyAIが喜びます
"""

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,
)
MODEL = "gpt-4"

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 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": "gpt-4",
            "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()
        
        split_content = content.split("### Prompt for Visual Expression\n\n")
        if len(split_content) > 1:
            visualize_prompt = split_content[1]
        else:
            visualize_prompt = "vacant dish"  # or any default value

        #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,
        )

        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 [content, img]
        
class NajiminoAI:
    
    @classmethod
    def generate_emo_prompt(cls, user_message):
        template = get_filetext(filename="template.md")
        prompt = f"""
        {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

    @classmethod
    def generate_emo(cls, user_message):
        prompt = NajiminoAI.generate_emo_prompt(user_message);
        start_with = ""
        result = OpenAI.chat_completion(prompt=prompt, start_with=start_with)
        return result

def main():
    iface = gr.Interface(fn=NajiminoAI.generate_emo,
                        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__':
    main()