app.py

import gradio as gr
import torch
import transformers

# https://github.com/huggingface/peft
# Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs)
# to various downstream applications without fine-tuning all the model's parameters.
from peft import PeftModel

from scrape_website import process_webpage

assert (
    "LlamaTokenizer" in transformers._import_structure["models.llama"]
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")

BASE_MODEL = "decapoda-research/llama-7b-hf"
LORA_WEIGHTS = "tloen/alpaca-lora-7b"

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

try:
    # mps device enables high-performance training on GPU for MacOS devices with Metal programming framework.
    if torch.backends.mps.is_available():
        device = "mps"
except:
    pass

if device == "cuda":
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL,
        load_in_8bit=False,
        torch_dtype=torch.float16,
        device_map="auto",
    )
    model = PeftModel.from_pretrained(
        model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
    )
elif device == "mps":
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL,
        device_map={"": device},
        torch_dtype=torch.float16,
    )
    model = PeftModel.from_pretrained(
        model,
        LORA_WEIGHTS,
        device_map={"": device},
        torch_dtype=torch.float16,
    )
else:
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
    )
    model = PeftModel.from_pretrained(
        model,
        LORA_WEIGHTS,
        device_map={"": device},
    )


def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an url that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
    else:
        return f"""Below is an url that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""


if device != "cpu":
    model.half()
model.eval()
if torch.__version__ >= "2":
    model = torch.compile(model)


def evaluate(
    instruction,
    url,
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=128,
    **kwargs,
):
    content = process_webpage(url=url)
    # avoid GPU memory overflow
    with torch.no_grad():
        torch.cuda.empty_cache()
        prompt = generate_prompt(instruction, content)
        inputs = tokenizer(prompt, return_tensors="pt")
        input_ids = inputs["input_ids"].to(device)
        generation_config = GenerationConfig(
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_beams=num_beams,
            **kwargs,
        )
        generation_output = model.generate(
            input_ids=input_ids,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
        )
        s = generation_output.sequences[0]
        output = tokenizer.decode(s)
    # avoid GPU memory overflow
    torch.cuda.empty_cache()
    return output.split("### Response:")[1].strip()


g = gr.Interface(
    fn=evaluate,
    inputs=[
        gr.components.Textbox(
            lines=2, label="FAQ", placeholder="Ask me anything about this website?"
        ),
        gr.components.Textbox(
            lines=1, label="Website URL", placeholder="https://www.meet-drift.ai/"
        ),
        # gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
        # gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
        # gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
        # gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
        # gr.components.Slider(
        #     minimum=1, maximum=512, step=1, value=128, label="Max tokens"
        # ),
    ],
    outputs=[
        gr.inputs.Textbox(
            lines=5,
            label="Output",
        )
    ],
    title="FAQ A Website",
    examples=[
        [
            "Can you list the capabilities this company has in bullet points?",
            "https://www.meet-drift.ai/",
        ],
        ["What's the name of the founder?", "https://www.meet-drift.ai/about"],
        [
            "in 1 word what's the service the company is providing?",
            "https://www.meet-drift.ai/",
        ],
        [
            "in 1 word what's the service the company is providing?",
            "https://www.tribe.ai/about",
        ],
        ["Who is Noah Gale?", "https://www.tribe.ai/team"],
        ["What sector is Tribe active in?", "https://www.tribe.ai"],
    ]
    # description="Alpaca-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).",
)
g.queue(concurrency_count=1)
g.launch()