Create app.py

app.py

@@ -0,0 +1,97 @@
+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 = {
+    "users": [
+        "awacke1", "rogerxavier", "jonatasgrosman", "kenshinn", "Csplk", "DavidVivancos",
+        "cdminix", "Jaward", "TuringsSolutions", "Severian", "Wauplin",
+        "phosseini", "Malikeh1375", "gokaygokay", "MoritzLaurer", "mrm8488",
+        "TheBloke", "lhoestq", "xw-eric", "Paul", "Muennighoff",
+        "ccdv", "haonan-li", "chansung", "lukaemon", "hails",
+        "pharmapsychotic", "KingNish", "merve", "ameerazam08", "ashleykleynhans"
+    ]
+}
+
+api = HfApi()
+
+def get_user_content(username):
+    try:
+        # Fetch models, datasets, and spaces associated with the user
+        models = api.list_models(author=username)
+        datasets = api.list_datasets(author=username)
+        spaces = api.list_spaces(author=username)
+        
+        return {
+            "username": username,
+            "models": models,
+            "datasets": datasets,
+            "spaces": spaces
+        }
+    except Exception as e:
+        return {"username": username, "error": str(e)}
+
+st.title("Hugging Face User Content Display")
+
+# Convert the default users list to a string
+default_users_str = "\n".join(default_users["users"])
+
+# Text area with default list of usernames
+usernames = st.text_area("Enter Hugging Face usernames (one per line):", value=default_users_str, height=300)
+
+if st.button("Show User Content"):
+    if usernames:
+        username_list = [username.strip() for username in usernames.split('\n') if username.strip()]
+        results = []
+        status_bars = {}
+
+        # 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)
+            status_bars[username].progress(100, text=f"Data fetched for {username}")
+            return content
+
+        # 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:
+            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(models) if models else "No models found")
+                st.markdown("**Datasets:**")
+                st.markdown("\n".join(datasets) if datasets else "No datasets found")
+                st.markdown("**Spaces:**")
+                st.markdown("\n".join(spaces) if spaces else "No spaces found")
+                st.markdown("---")
+            else:
+                st.warning(f"{username}: {result['error']}")
+
+    else:
+        st.warning("Please enter at least one username.")
+
+st.sidebar.markdown("""
+## How to use:
+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 bars show the status of content retrieval for each user.
+""")