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| import streamlit as st | |
| import wandb | |
| import pandas as pd | |
| import os | |
| # import matplotlib.pyplot as plt | |
| from utils import fetch_runs_to_df, fetch_run, fetch_models_to_df | |
| # Access the API key from the environment variable | |
| wandb_api_key = os.getenv('WANDB_API_KEY') | |
| # Log in to wandb using the API key | |
| if wandb_api_key: | |
| wandb.login(key=wandb_api_key) | |
| else: | |
| st.error("WANDB_API_KEY not found in environment variables.") | |
| # Initialize W&B API | |
| api = wandb.Api() | |
| # Define available projects (bookmarks) | |
| projects = { | |
| "Competition 1": {"entity": "urbaniak-bruno-safescanai", "project": "pytorch-intro"}, | |
| "Competition 2": {"entity": "urbaniak-bruno-safescanai", "project": "basic-intro"}, | |
| "Competition 3 (mamymodelexd)": {"entity": "urbaniak-bruno-safescanai", "project": "simple-cnn"}, | |
| # Add more projects as needed | |
| } | |
| competitions = list(projects.keys()) | |
| competitions.append("All") | |
| # Sidebar for project selection | |
| st.sidebar.title("Competitions") | |
| selected_project = st.sidebar.selectbox("Select a competition:", competitions) | |
| # Sidebar with buttons | |
| st.sidebar.title("Iluzja wyboru") | |
| option = st.sidebar.radio( | |
| "Select an option:", | |
| ["General", "Researchers", "Validators", "Models"] | |
| ) | |
| df = fetch_runs_to_df(api, projects, selected_project) | |
| if option == "General": | |
| # Streamlit UI | |
| st.title("W&B Data in Streamlit") | |
| # Sidebar filter options | |
| st.sidebar.header("Filter Options") | |
| run_name_filter = st.sidebar.text_input("Filter by Run Name", "") | |
| state_filter = st.sidebar.selectbox("Filter by State", ["All"] + df["State"].unique().tolist()) | |
| tag_filter = st.sidebar.text_input("Filter by Tags (comma separated)", "") | |
| # Apply filters | |
| if run_name_filter: | |
| df = df[df["Run Name"].str.contains(run_name_filter, case=False, na=False)] | |
| if state_filter != "All": | |
| df = df[df["State"] == state_filter] | |
| if tag_filter: | |
| tag_list = [tag.strip() for tag in tag_filter.split(",")] | |
| df = df[df["Tags"].apply(lambda tags: any(tag in tags for tag in tag_list))] | |
| # Display the filtered DataFrame | |
| st.dataframe(df) | |
| # Display details of selected run | |
| if not df.empty: | |
| selected_run_id = st.selectbox("Select a Run ID to see details", df["ID"].tolist()) | |
| run = fetch_run(api, projects, selected_project, selected_run_id) | |
| if run: | |
| run_df = run.history() | |
| st.write(f"Details for run: {run.name}") | |
| st.dataframe(run_df) | |
| else: | |
| st.warning("No runs available to select.") | |
| if option == "Models": | |
| st.subheader("Model Ranking") | |
| # Ensure the DataFrame is not empty | |
| if not df.empty: | |
| # Convert to DataFrame | |
| ranking_df = fetch_models_to_df(api, projects, selected_project, df) | |
| # Rank by Accuracy (or another metric) | |
| ranking_df = ranking_df.sort_values(by="Accuracy", ascending=False).reset_index(drop=True) | |
| ranking_df.index += 1 # Start ranking from 1 | |
| # Display the ranking table | |
| st.dataframe(ranking_df) | |
| else: | |
| st.warning("No runs available for ranking.") |