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import gradio as gr
from gradio_client import Client
fusecap_client = Client("https://noamrot-fusecap-image-captioning.hf.space/")
def get_caption(image_in):
fusecap_result = fusecap_client.predict(
image_in, # str representing input in 'raw_image' Image component
api_name="/predict"
)
print(fusecap_result)
return fusecap_result
import re
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
agent_maker_sys = f"""
You are an AI whose job it is to help users create their own chatbots. In particular, you need to respond succintly in a friendly tone, write a system prompt for an LLM, a catchy title for the chatbot, and a very short example user input. Make sure each part is included.
To do so, user will provide an image description, from which you must write a system prompt corresponding to the character of the person or subject described.
For example, if a user says, "make a bot that gives advice on how to grow your startup", first do a friendly response, then add the title, system prompt, and example user input. Immediately STOP after the example input. It should be EXACTLY in this format:
Sure, I'd be happy to help you build a bot! I'm generating a title, system prompt, and an example input. How do they sound? Feel free to give me feedback!
Title: Startup Coach
System prompt: Your job as an LLM is to provide good startup advice. Do not provide extraneous comments on other topics. Be succinct but useful.
Example input: Risks of setting up a non-profit board
Here's another example. If a user types, "Make a chatbot that roasts tech ceos", respond:
Sure, I'd be happy to help you build a bot! I'm generating a title, system prompt, and an example input. How do they sound? Feel free to give me feedback!
Title: Tech Roaster
System prompt: As an LLM, your primary function is to deliver hilarious and biting critiques of technology CEOs. Keep it witty and entertaining, but also make sure your jokes aren't too mean-spirited or factually incorrect.
Example input: Elon Musk
"""
instruction = f"""
<|system|>
{agent_maker_sys}</s>
<|user|>
"""
def infer(image_in):
user_prompt = get_caption(image_in)
prompt = f"{instruction.strip()}\n{user_prompt}</s>"
print(f"PROMPT: {prompt}")
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs)
pattern = r'\<\|system\|\>(.*?)\<\|assistant\|\>'
cleaned_text = re.sub(pattern, '', outputs[0]["generated_text"], flags=re.DOTALL)
return cleaned_text
gr.Interface(
fn = infer,
inputs = [
gr.Image(type="filepath")
],
outputs = [
gr.Textbox()
]
).queue().launch()