rankingowanie modeli

app.py

@@ -41,35 +41,74 @@ option = st.sidebar.radio(
 
 df = fetch_runs_to_df(api, projects, selected_project)
 
-# 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 == "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:
+        # Fetch metrics for ranking (e.g., accuracy or loss)
+        ranking_data = []
+        for index, row in df.iterrows():
+            try:
+                # Fetch the run details
+                run = api.run(f"{projects[selected_project]['entity']}/{projects[selected_project]['project']}/{row['ID']}")
+                metrics = run.summary
+                model_name = run.config.get("model_name", "Unknown")  # Fetch model name from the config, defaulting to "Unknown"
+                
+                ranking_data.append({
+                    "Model Name": model_name,  # Add model name to the table
+                    "Run Name": row["Run Name"],
+                    "ID": row["ID"],
+                    "Accuracy": metrics.get("accuracy"),  # Example metric
+                    "Loss": metrics.get("loss")  # Example metric
+                })
+            except wandb.errors.CommError:
+                continue
+
+        # Convert to DataFrame
+        ranking_df = pd.DataFrame(ranking_data)
+
+        # 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.")