my_agent/utils/utils.py

from langchain_core.messages import SystemMessage
from .tools import StoryFormatter 
from .models_loader import llm
import base64
from PIL import Image
from io import BytesIO
from fastapi import UploadFile
from huggingface_hub import InferenceClient
from .prompts import story_to_prompt , final_story_prompt
import os

def generate_final_story(final_state):
    if len(final_state['preferred_topics'])>0:
        template = final_story_prompt(final_state)
        messages = [SystemMessage(content=template)]
        response = llm.bind_tools([StoryFormatter]).invoke(messages)
        print('The final response is:',response)
        if hasattr(response, 'tool_calls') and response.tool_calls:
            response = response.tool_calls[0]['args']
        elif hasattr(response, 'content'):
            response = response.content
        else:
          response = "No response"
        # state.final_story.append(response)
        # state.stories.append(response)
        return response
    
    else:
        return final_state['stories'][-1]



def encode_image_to_base64(uploaded_file: UploadFile) -> str:
    return base64.b64encode(uploaded_file.file.read()).decode("utf-8")
  

# Convert base64 string to PIL image (optional for LangGraph processing)
def process_image(base64_str: str) -> Image.Image:
    image_data = base64.b64decode(base64_str)
    return Image.open(BytesIO(image_data))


def generate_prompt(final_story):
    print('************Entering prompt generator****************')
    messages = [
    (
        "system",
        story_to_prompt,
    ),
    ("human", final_story),
]
    prompt = llm.invoke(messages)
    print('The prompt is:',prompt)
    return prompt.content

def generate_image(final_story):
    prompt = generate_prompt(final_story)
    print('************Finished prompt generator****************')

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ.get('HUGGINGFACEHUB_ACCESS_TOKEN'),
    )

    print('************Finished calling generator****************')


        # output is a PIL.Image object
    image = client.text_to_image(
        prompt,
        model="black-forest-labs/FLUX.1-schnell",
    )
    print('*****************Image Created*******************')

    image.save('image.png')
    print('*****************Image Saved*******************')
    return "Image Created"
    # try:
    #     return image
    # except:
    #     return 'Image created'