AntoineToubhans

from PIL import Image

model = load_model(rev=selected_commit.hexsha)
uploaded_file = st.file_uploader("Upload an image")

if uploaded_file:
    image = Image.open(uploaded_file)
    image_name = uploaded_file.name
    resized_image = image.resize(IMG_SIZE)

AntoineToubhans

from pathlib import Path
import tensorflow as tf
# Warning: this is private internal dvc api, it may change with future version
import dvc.repo.get

ROOT_MODEL_CACHE_DIR = Path(".model_cache")
ROOT_MODEL_CACHE_DIR.mkdir(exist_ok=True)

@st.cache
def load_model(rev: str):

AntoineToubhans

experiments = pd.DataFrame([
    {
        "hash": model_commit.hexsha,
        "message": model_commit.message,
        "committed_datetime": str(model_commit.committed_datetime),
        "committer": str(model_commit.committer),
        **MODELS_PARAMETERS[model_commit.hexsha],
        **MODELS_EVALUATION_METRICS[model_commit.hexsha],
    }
    for model_commit in MODELS_COMMITS

AntoineToubhans

import json

def _read_model_evaluation_metrics(rev: str) -> dict:
    with dvc.api.open("data/evaluation/metrics.json", rev=rev) as file:
        return json.load(file)

MODELS_EVALUATION_METRICS = {
    commit.hexsha: _read_model_evaluation_metrics(model_rev=commit.hexsha)
    for commit in MODELS_COMMITS
}

AntoineToubhans

import yaml

def _read_train_params(rev: str) -> dict:
    with git_open("dvc.lock", rev=rev) as file:
        dvc_lock = yaml.safe_load(file)
        return dvc_lock["stages"]["train"]["params"]["params.yaml"]

MODELS_PARAMETERS = {
    commit.hexsha: _read_train_params(rev=commit.hexsha)
    for commit in MODELS_COMMITS

AntoineToubhans

FIRST_COMMIT = list(REPO.iter_commits())[-1]

@contextmanager
def git_open(path: str, rev: str):
    commit = REPO.commit(rev)
    # Hack to get the full blob data stream: compute diff with initial commit
    diff = commit.diff(FIRST_COMMIT, str(path))[0]
    yield diff.a_blob.data_stream

AntoineToubhans

st.dataframe(disagree_predictions)

for _, row in disagree_predictions.iterrows():
    st.image(
        row["image_path"],
        caption=f"{row['image_name']}: A={row['predicted_label_x']}, B={row['predicted_label_y']} (true={row['true_label']})",
        width=150,
    )

AntoineToubhans

predictions_a = load_predictions(rev=selected_commit_a.hexsha)
predictions_b = load_predictions(rev=selected_commit_b.hexsha)

disagree_predictions = pd.merge(
    left=predictions_a.drop(columns=["prediction"]),
    right=predictions_b.drop(columns=["true_label", "image_path", "prediction"]),
    on="image_name",
).loc[lambda df: df.predicted_label_x != df.predicted_label_y]

AntoineToubhans

selected_commit_a = st.selectbox(
    "Choose commit A",
    [commit for commit in MODELS_COMMITS],
    format_func=lambda commit: f"{commit.hexsha[:6]} - {commit.message} - {commit.committed_datetime}",
)

selected_commit_b = st.selectbox(
    "Choose commit B",
    [commit for commit in MODELS_COMMITS],
    format_func=lambda commit: f"{commit.hexsha[:6]} - {commit.message} - {commit.committed_datetime}",

AntoineToubhans

import dvc.api
import pandas as pd

@st.cache
def load_predictions(rev: str) -> pd.DataFrame:
    with dvc.api.open("data/evaluation/predictions.csv", rev=rev) as f:
        return pd.read_csv(f)

selected_commit = ...  # Use the commit selector introduced previous section