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import gradio as gr
from tools import *
from format_dexcom import process_csv
import tempfile
import os
from huggingface_hub import list_models
from typing import List, Tuple
from pathlib import Path
import plotly.graph_objects as go
from huggingface_hub import HfApi
def get_available_models() -> List[str]:
"""Get list of available gluformer models from HuggingFace."""
api = HfApi()
files = api.list_repo_files("Livia-Zaharia/gluformer_models")
# Filter for .pth files
gluformer_models = [
file for file in files
if file.endswith('.pth') and "weights" in file.lower() and 'gluformer' in file.lower()
]
return gluformer_models
AVAILABLE_MODELS = get_available_models()
print(AVAILABLE_MODELS)
def process_and_prepare(file: tempfile._TemporaryFileWrapper, model_name: str) -> Tuple[gr.Slider, gr.Markdown]:
"""Process the raw CSV and prepare it for prediction.
Args:
file: Uploaded temporary file object
model_name: Name of the selected model
Returns:
Tuple containing:
- Updated slider component
- Sample count markdown component
"""
with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp_file:
processed_path = tmp_file.name
process_csv(
input_dir=file.name,
output_file=processed_path
)
return prep_predict_glucose_tool(processed_path, model_name)
with gr.Blocks() as demo:
gr.Markdown("# Glucose Prediction Tool")
gr.Markdown("Upload a Dexcom CSV file to get predictions")
model_selector = gr.Dropdown(
choices=AVAILABLE_MODELS,
value="gluformer_1samples_500epochs_10heads_32batch_geluactivation_livia_large_weights.pth",
label="Select Model",
interactive=True
)
file_input = gr.File(label="Upload Raw Dexcom CSV File")
with gr.Row():
index_slider = gr.Slider(
minimum=0,
maximum=100, # This will be updated dynamically
value=10,
step=1,
label="Select Sample Index",
visible=False
)
sample_count = gr.Markdown(visible=False)
plot_output = gr.Plot()
# Update slider and show total samples when file is uploaded
file_input.change(
fn=process_and_prepare,
inputs=[file_input, model_selector],
outputs=[index_slider, sample_count],
queue=True
)
# Only update plot after processing is complete
index_slider.change(
fn=predict_glucose_tool,
inputs=[index_slider],
outputs=plot_output,
queue=True
)
demo.launch(share=True) |