Update app-backup.py

app-backup.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from huggingface_hub import HfApi
 import pandas as pd
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 # Default list of Hugging Face usernames
 default_users = {
@@ -24,13 +25,13 @@ def get_user_content(username):
         spaces = api.list_spaces(author=username)
         
         return {
+            "username": username,
             "models": models,
             "datasets": datasets,
             "spaces": spaces
         }
     except Exception as e:
-        st.error(f"Error fetching content for {username}: {str(e)}")
-        return None
+        return {"username": username, "error": str(e)}
 
 st.title("Hugging Face User Content Display")
 
@@ -44,42 +45,45 @@ if st.button("Show User Content"):
     if usernames:
         username_list = [username.strip() for username in usernames.split('\n') if username.strip()]
         results = []
+        status_bars = {}
 
-        progress_bar = st.progress(0)
-        for i, username in enumerate(username_list):
+        # Set up the progress bars for each user
+        for username in username_list:
+            status_bars[username] = st.progress(0, text=f"Fetching data for {username}...")
+
+        def fetch_and_display(username):
             content = get_user_content(username)
-            if content:
-                profile_link = f"https://huggingface.co/{username}"
-                profile_emoji = "🔗"
-                
-                models = [f"[{model.modelId}](https://huggingface.co/{model.modelId})" for model in content['models']]
-                datasets = [f"[{dataset.id}](https://huggingface.co/datasets/{dataset.id})" for dataset in content['datasets']]
-                spaces = [f"[{space.id}](https://huggingface.co/spaces/{space.id})" for space in content['spaces']]
+            status_bars[username].progress(100, text=f"Data fetched for {username}")
+            return content
 
-                results.append({
-                    "Hugging Face": username,
-                    "Profile Link": f"[{profile_emoji} Profile]({profile_link})",
-                    "Models": models,
-                    "Datasets": datasets,
-                    "Spaces": spaces
-                })
-            else:
-                results.append({"Hugging Face": username, "Error": "User content not found"})
-            progress_bar.progress((i + 1) / len(username_list))
+        # Use ThreadPoolExecutor for concurrent execution
+        with ThreadPoolExecutor(max_workers=len(username_list)) as executor:
+            future_to_username = {executor.submit(fetch_and_display, username): username for username in username_list}
+            for future in as_completed(future_to_username):
+                result = future.result()
+                results.append(result)
 
         st.markdown("### User Content Overview")
         for result in results:
-            if "Error" not in result:
-                st.markdown(f"**{result['Hugging Face']}** {result['Profile Link']}")
+            username = result["username"]
+            if "error" not in result:
+                profile_link = f"https://huggingface.co/{username}"
+                profile_emoji = "🔗"
+                
+                models = [f"[{model.modelId}](https://huggingface.co/{model.modelId})" for model in result['models']]
+                datasets = [f"[{dataset.id}](https://huggingface.co/datasets/{dataset.id})" for dataset in result['datasets']]
+                spaces = [f"[{space.id}](https://huggingface.co/spaces/{space.id})" for space in result['spaces']]
+
+                st.markdown(f"**{username}** {profile_emoji} [Profile]({profile_link})")
                 st.markdown("**Models:**")
-                st.markdown("\n".join(result["Models"]) if result["Models"] else "No models found")
+                st.markdown("\n".join(models) if models else "No models found")
                 st.markdown("**Datasets:**")
-                st.markdown("\n".join(result["Datasets"]) if result["Datasets"] else "No datasets found")
+                st.markdown("\n".join(datasets) if datasets else "No datasets found")
                 st.markdown("**Spaces:**")
-                st.markdown("\n".join(result["Spaces"]) if result["Spaces"] else "No spaces found")
+                st.markdown("\n".join(spaces) if spaces else "No spaces found")
                 st.markdown("---")
             else:
-                st.warning(f"{result['Hugging Face']}: {result['Error']}")
+                st.warning(f"{username}: {result['error']}")
 
     else:
         st.warning("Please enter at least one username.")
@@ -89,5 +93,5 @@ st.sidebar.markdown("""
 1. The text area is pre-filled with a list of Hugging Face usernames. You can edit this list or add more usernames.
 2. Click 'Show User Content'.
 3. View the user's models, datasets, and spaces along with a link to their Hugging Face profile.
-4. The progress bar shows the status of content retrieval.
+4. The progress bars show the status of content retrieval for each user.
 """)