mocne zmiany

app.py

@@ -4,7 +4,7 @@ import pandas as pd
 import os
 import matplotlib.pyplot as plt
 
-# import utils.py
+from utils import fetch_runs_to_df
 
 # Access the API key from the environment variable
 wandb_api_key = os.getenv('WANDB_API_KEY')
@@ -25,64 +25,42 @@ projects = {
     "Competition 2": {"entity": "urbaniak-bruno-safescanai", "project": "basic-intro"},
     # Add more projects as needed
 }
+bookmarks = ["All", "Competition 1", "Competition 2"]
 
 # Sidebar for project selection
 st.sidebar.title("Bookmarks")
-selected_project = st.sidebar.selectbox("Select a competition:", list(projects.keys()))
-
-# Get the selected project's details
-entity = projects[selected_project]["entity"]
-project = projects[selected_project]["project"]
+selected_project = st.sidebar.selectbox("Select a competition:", bookmarks)
 
 
 # Sidebar with buttons
 st.sidebar.title("Iluzja wyboru")
 option = st.sidebar.radio(
     "Select an option:",
-    ["Overview", "Metrics", "Graphs", "Other"]
+    ["General", "Researchers", "Validators", "Models"]
 )
 
+df = fetch_runs_to_df(api, projects, selected_project)
 
 # Streamlit UI
 st.title("W&B Data in Streamlit")
 
-# Fetch all runs from the project
-runs = api.runs(f"{entity}/{project}")
-
-# Create a dropdown
-run_names = [run.name for run in runs]
-selected_run_name = st.selectbox("Select a run:", run_names)
-
-# Find the selected run
-selected_run = next(run for run in runs if run.name == selected_run_name)
-
-# Fetch the run data as a pandas DataFrame
-run_df = selected_run.history()
-
-if run_df.empty:
-    st.warning("The selected run has no data available.")
-else:
-    
-    st.write(f"Displaying data for run: {selected_run.name}")
+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())
+
+# 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]
+
+# Show the filtered DataFrame
+st.dataframe(df)
+
+# Display details of selected run
+selected_run_id = st.selectbox("Select a Run ID to see details", df["ID"].tolist())
+if selected_run_id:
+    selected_run = api.run(f"{entity}/{project}/{selected_run_id}")
+    run_df = selected_run.history()
+    st.write(f"Details for run: {selected_run.name}")
     st.dataframe(run_df)
-
-    # Example of creating a simple line plot
-    st.subheader("Run Metrics Over Time")
-    
-    # Select columns to plot
-    metrics = st.multiselect("Select metrics to plot", run_df.columns, default=run_df.columns[0])
-
-    if metrics:
-        plt.figure(figsize=(10, 5))
-        for metric in metrics:
-            plt.plot(run_df['_step'], run_df[metric], label=metric)
-
-        plt.xlabel("Step")
-        plt.ylabel("Value")
-        plt.legend()
-        plt.title("Metrics Over Time")
-        
-        # Display the plot in Streamlit
-        st.pyplot(plt)
-    else:
-        st.info("Please select at least one metric to plot.")

utils.py

@@ -0,0 +1,37 @@
+import streamlit as st
+import pandas as pd
+
+def fetch_runs_to_df(api, projects, selected_project):
+    if selected_project == "All":
+        # return all runs from all projects
+        data = []
+        for project_name, details in projects.items():
+            entity = details["entity"]
+            project = details["project"]
+            runs = api.runs(f"{entity}/{project}")
+            for run in runs:
+                data.append({
+                    "Run Name": run.name,
+                    "ID": run.id,
+                    "Created At": run.created_at,
+                    "State": run.state,
+                    "Tags": ", ".join(run.tags)  # Join tags into a single string
+                })
+        df = pd.DataFrame(data)
+    
+    else:
+        # Get the selected project's details
+        entity = projects[selected_project]["entity"]
+        project = projects[selected_project]["project"]
+        runs = api.runs(f"{entity}/{project}")
+        for run in runs:
+            data.append({
+                "Run Name": run.name,
+                "ID": run.id,
+                "Created At": run.created_at,
+                "State": run.state,
+                "Tags": ", ".join(run.tags)  # Join tags into a single string
+            })
+        df = pd.DataFrame(data)
+
+    return df