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import gradio as gr
import os, requests
import torch, torchvision, einops
import spaces
import subprocess
from transformers import AutoModelForCausalLM, AutoModel, AutoModelForVision2Seq, PaliGemmaForConditionalGeneration, LlavaForConditionalGeneration, LlavaNextForConditionalGeneration
from huggingface_hub import login
# Install required package
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
HF_TOKEN = os.getenv("HF_TOKEN")
login(token=HF_TOKEN, add_to_git_credential=True)
# Cache for storing loaded models and their summaries
model_cache = {}
# Function to get the model summary
@spaces.GPU
def get_model_summary(model_name):
if model_name in model_cache:
return model_cache[model_name], ""
try:
# Fetch the config.json file
config_url = f"https://huggingface.co/{model_name}/raw/main/config.json"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
response = requests.get(config_url, headers=headers)
response.raise_for_status()
config = response.json()
architecture = config["architectures"][0]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Select the correct model class based on the architecture
if architecture == "LlavaNextForConditionalGeneration":
from transformers import LlavaNextForConditionalGeneration
model = LlavaNextForConditionalGeneration.from_pretrained(model_name, trust_remote_code=True).to(device)
elif architecture == "LlavaForConditionalGeneration":
from transformers import LlavaForConditionalGeneration
model = LlavaForConditionalGeneration.from_pretrained(model_name, trust_remote_code=True).to(device)
elif architecture == "PaliGemmaForConditionalGeneration":
from transformers import PaliGemmaForConditionalGeneration
model = PaliGemmaForConditionalGeneration.from_pretrained(model_name, trust_remote_code=True).to(device)
elif architecture == "Idefics2ForConditionalGeneration":
from transformers import Idefics2ForConditionalGeneration
model = Idefics2ForConditionalGeneration.from_pretrained(model_name, trust_remote_code=True).to(device)
elif architecture == "MiniCPMV":
from transformers import MiniCPMV
model = MiniCPMV.from_pretrained(model_name, trust_remote_code=True).to(device)
else:
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
model_summary = str(model)
model_cache[model_name] = model_summary
return model_summary, ""
except Exception as e:
return "", str(e)
# Create the Gradio Blocks interface
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
textbox = gr.Textbox(label="Model Name", placeholder="Enter the model name here OR select example below...", lines=1)
gr.Markdown("### Vision Models")
vision_examples = gr.Examples(
examples=[
["llava-hf/llava-v1.6-mistral-7b-hf"],
["xtuner/llava-phi-3-mini-hf"],
["xtuner/llava-llama-3-8b-v1_1-transformers"],
["vikhyatk/moondream2"],
["openbmb/MiniCPM-Llama3-V-2_5"],
["microsoft/Phi-3-vision-128k-instruct"],
["google/paligemma-3b-mix-224"],
["HuggingFaceM4/idefics2-8b-chatty"],
["microsoft/llava-med-v1.5-mistral-7b"]
],
inputs=textbox
)
gr.Markdown("### Other Models")
other_examples = gr.Examples(
examples=[
["google/gemma-7b"],
["microsoft/Phi-3-mini-4k-instruct"],
["meta-llama/Meta-Llama-3-8B"],
["mistralai/Mistral-7B-Instruct-v0.3"]
],
inputs=textbox
)
submit_button = gr.Button("Submit")
with gr.Column():
output = gr.Textbox(label="Model Architecture", lines=20, placeholder="Model architecture will appear here...", show_copy_button=True)
error_output = gr.Textbox(label="Error", lines=10, placeholder="Exceptions will appear here...", show_copy_button=True)
def handle_click(model_name):
model_summary, error_message = get_model_summary(model_name)
return model_summary, error_message
submit_button.click(fn=handle_click, inputs=textbox, outputs=[output, error_output])
# Launch the interface
demo.launch()
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