Spaces:
Sleeping
Sleeping
File size: 3,080 Bytes
946d35b 708437f 946d35b 708437f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
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
def generate_final_story(query):
if len(query['preferred_topics'])>0:
template = f'''I want to create a detailed storyline for a video in the given topic. You have to provide me that storyline what to include in the video.
Now, i am giving you the topic of the video. But the need is to generate the story focusing on the format that i'll provide to you.
You can use this format for the reference purpose, not for the exact similar generation. The format is:\n{query['retrievals'][-1]}.
\n\n Now let's start creating the storyline for my topic. The topic of the video is: \n\n{query['topic']}\n\n
**Final Reminder** You have to strongly focus on these topics while creating the storyline: {[item for sublist in query['preferred_topics'] for item in sublist]}'''
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 query['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="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",
)
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'
|