Update app.py

app.py

@@ -84,13 +84,17 @@ def generate_html_page(username, models, datasets):
 
     return html_file_path
 
-# Cache the HTML generation process using Streamlit's caching decorator
+# Cache the HTML file path using Streamlit's caching decorator
 @st.cache_data(show_spinner=False)
-def get_cached_html_page(username):
+def get_cached_html_file(username):
+    return generate_html_page(username, *get_user_content(username))
+
+# Fetch user content from the API (without caching)
+def get_user_content(username):
     user_data = asyncio.run(fetch_user_content(username))
     if "error" in user_data:
         return None, user_data["error"]
-    return generate_html_page(username, user_data["models"], user_data["datasets"]), user_data
+    return user_data["models"], user_data["datasets"]
 
 # Streamlit app setup - the nerve center of our operation! 🎛️
 st.title("Hugging Face User Content Display - Let's Automate Some Fun! 🎉")
@@ -116,31 +120,31 @@ if st.button("Show User Content"):
                 st.markdown(f"**{username}** [🔗 Profile](https://huggingface.co/{username})")
 
                 # Generate HTML page and provide download link - because who wouldn't want a custom webpage? 🌐
-                html_file_path, user_data = get_cached_html_page(username)
-
-                if not html_file_path:
-                    st.warning(f"{username}: {user_data} - Looks like the AI needs a coffee break ☕")
+                models, datasets = get_user_content(username)
+                if models is None:
+                    st.warning(f"{username}: {datasets} - Looks like the AI needs a coffee break ☕")
                 else:
+                    html_file_path = get_cached_html_file(username)
                     st.markdown(f"[📄 Download {username}'s HTML Page]({html_file_path})")
 
                     # Add to statistics for Plotly graphs
                     stats["username"].append(username)
-                    stats["models_count"].append(len(user_data["models"]))
-                    stats["datasets_count"].append(len(user_data["datasets"]))
+                    stats["models_count"].append(len(models))
+                    stats["datasets_count"].append(len(datasets))
 
                     # Models section with expander - 🧠 because AI models are brainy! 🧠
-                    with st.expander(f"🧠 Models ({len(user_data['models'])})", expanded=False):
-                        if user_data['models']:
-                            for model in user_data['models']:
+                    with st.expander(f"🧠 Models ({len(models)})", expanded=False):
+                        if models:
+                            for model in models:
                                 model_name = model.modelId.split("/")[-1]
                                 st.markdown(f"- [{model_name}](https://huggingface.co/{model.modelId})")
                         else:
                             st.markdown("No models found. Did you check under the rug? 🕵️‍♂️")
 
                     # Datasets section with expander - 📚 because data is the foundation of AI! 📚
-                    with st.expander(f"📚 Datasets ({len(user_data['datasets'])})", expanded=False):
-                        if user_data['datasets']:
-                            for dataset in user_data['datasets']:
+                    with st.expander(f"📚 Datasets ({len(datasets)})", expanded=False):
+                        if datasets:
+                            for dataset in datasets:
                                 dataset_name = dataset.id.split("/")[-1]
                                 st.markdown(f"- [{dataset_name}](https://huggingface.co/datasets/{dataset.id})")
                         else: