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308b9ce 8d1382d 308b9ce | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | from pathlib import Path
import gradio as gr
import pandas as pd
import os
# ---------- Paths ----------
from .config import build_paths, UI_EXAMPLES
HERE = Path(__file__).resolve()
SRC_DIR = HERE.parents[1]
p = build_paths(SRC_DIR)
MODEL_DIR = p["MODEL_DIR"]
MODEL_PATH = p["MODEL_PATH"]
FEATURE_SCALER_PATH = p.get("FEATURE_SCALER_PATH")
TARGET_SCALER_PATH = p.get("TARGET_SCALER_PATH")
ENCODER_PATH = p["ENCODER_PATH"]
SCHEMA_PATH = p["SCHEMA_PATH"]
LOGS_DIR = p["LOGS_DIR"]; LOGS_DIR.mkdir(parents=True, exist_ok=True)
DB_PATH = p["DB_PATH"]
REPORT_PATH = p["REPORT_PATH"]
# ---------- Load model & schema ----------
from .model_loader import load_model_and_schema, load_optional_joblib
model, schema, TARGET_NAME, FEATURES, INTERNAL_EXPECTED = load_model_and_schema(
MODEL_PATH, SCHEMA_PATH
)
fx_scaler = load_optional_joblib(FEATURE_SCALER_PATH)
y_scaler = load_optional_joblib(TARGET_SCALER_PATH)
encoder = load_optional_joblib(ENCODER_PATH)
UI_FEATURE_NAMES = [f["name"] for f in FEATURES]
# ---------- Helpers ----------
from .helpers.log_utils import log_prediction
from .helpers.predict_utils import predict_single
from .helpers.schema_utils import get_bounds
from .helpers.report_utils import read_model_report, report_summary_df, report_metrics_df
from .helpers.sqlite_utils import load_val_subset
# ---------- UI ----------
def build_app():
app_title = f"TrAIn.me — (v5.0-minimal)"
app_desc_ml = "Personalize your experience"
app_desc_ex = "Choose your training program"
# app_desc_dl = "Generate your personalized exercise"
app_desc_dl_exec = "Execution generator"
from .pages.ml_tab import render_ml_tab
# from .pages.exercices_tab import render_list_of_exercices
# from .pages.dl_tab import render_dl_tab
from .pages.dl_execution_tab import render_dl_execution_tab
from .config import UI_EXAMPLES
with gr.Blocks(title=app_title) as demo:
gr.Markdown(f"# {app_title}\n{app_desc_ml} / {app_desc_dl_exec}")
with gr.Tabs():
# Onglet 1 : ML
level_out, wf_out, wt_out = render_ml_tab(
app_desc_ml=app_desc_ml,
feature_specs=FEATURES,
ui_feature_names=UI_FEATURE_NAMES,
internal_expected=INTERNAL_EXPECTED,
target_name=TARGET_NAME,
schema=schema,
ui_examples=UI_EXAMPLES,
db_path=DB_PATH,
model=model,
logs_dir=LOGS_DIR,
model_path=MODEL_PATH,
feature_scaler=fx_scaler,
target_scaler=y_scaler,
encoder=encoder,
report_path=REPORT_PATH,
on_load=demo.load,
)
# # Onglet 2 : liste des programmes
# selected_program_state, goal_state = render_list_of_exercices(
# app_desc_ex=app_desc_ex,
# level_out=level_out,
# )
# # Onglet 3 : DL – programme complet
# render_dl_tab(
# app_desc_dl=app_desc_dl,
# level_out=level_out,
# wf_comp=wf_out,
# wt_comp=wt_out,
# selected_program_df=selected_program_state,
# goal_state=goal_state,
# )
# Onglet 4 : DL – Execution generator
render_dl_execution_tab(
app_desc_dl_exec=app_desc_dl_exec
)
return demo
if __name__ == "__main__":
build_app().launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860)))
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