+ """,
+ unsafe_allow_html=True,
+ )
+ if st.button(
+ "➕ Create New Repository",
+ use_container_width=True,
+ key="create_new_repo"
+ ):
+ st.session_state.page = "repository_management"
+ st.rerun()
+
+ # Analytics button
+ if st.button("📊 Analytics", use_container_width=True, key="analytics_btn"):
+ st.session_state.page = "analytics"
+ st.session_state.selected_model = None
+ st.rerun()
+
+ # Batch Operations button
+ if st.button("🔄 Batch Operations", use_container_width=True, key="batch_btn"):
+ st.session_state.page = "batch_operations"
+ st.session_state.selected_model = None
+ st.rerun()
+
+
+ # Refresh models button
+ if st.button(
+ "🔄 Refresh Models", use_container_width=True, key="refresh_models"
+ ):
+ with st.spinner("Refreshing models..."):
+ try:
+ st.session_state.models = st.session_state.client.get_user_models()
+ st.success("Models refreshed!")
+ except Exception as e:
+ st.error(f"Error refreshing models: {str(e)}")
+
+ # Spacer
+ st.markdown(
+ """
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Bottom links with improved styling
+ st.markdown(
+ """
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Helpful links section
+ st.markdown(
+ """
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Version info
+ st.markdown(
+ """
+
+ HF Model Manager v1.0.0
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Logout button
+ if st.button("🚪 Logout", use_container_width=True, key="logout_btn"):
+ logout()
\ No newline at end of file
diff --git a/components/upload_model.py b/components/upload_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9718047747dda0f13f5219c62e8ea9c9559e5dba
--- /dev/null
+++ b/components/upload_model.py
@@ -0,0 +1,122 @@
+import streamlit as st
+import os
+import tempfile
+import io
+import zipfile
+import shutil
+
+
+def upload_model_form(model_info):
+ """Form for uploading model files"""
+ st.subheader("Upload Model Files")
+
+ if not model_info:
+ st.error("Model information not found")
+ return False
+
+ repo_id = model_info.modelId
+
+ st.info(
+ """
+ Upload model files to your repository. You can upload:
+
+ - Individual model files (e.g., model weights, configuration files)
+ - A ZIP archive containing multiple files (will be extracted automatically)
+
+ **Important**: Large files may take time to upload.
+ """
+ )
+
+ with st.form("upload_model_form"):
+ # File upload
+ uploaded_files = st.file_uploader(
+ "Upload model files",
+ accept_multiple_files=True,
+ help="Select one or more files to upload",
+ )
+
+ # ZIP handling option
+ extract_zip = st.checkbox(
+ "Extract ZIP files automatically",
+ value=True,
+ help="If enabled, ZIP files will be extracted in the repository",
+ )
+
+ # Commit message
+ commit_message = st.text_input(
+ "Commit message",
+ value="Upload model files",
+ help="Describe the changes you're making",
+ )
+
+ # Submit button
+ submitted = st.form_submit_button("Upload Files", use_container_width=True)
+
+ if submitted and uploaded_files:
+ with st.spinner("Uploading files..."):
+ try:
+ # Create a temporary directory to work with files
+ with tempfile.TemporaryDirectory() as temp_dir:
+ files_to_upload = {}
+
+ for uploaded_file in uploaded_files:
+ file_content = uploaded_file.read()
+ file_name = uploaded_file.name
+
+ # Handle ZIP files if extraction is enabled
+ if extract_zip and file_name.lower().endswith(".zip"):
+ with zipfile.ZipFile(
+ io.BytesIO(file_content)
+ ) as zip_ref:
+ zip_ref.extractall(
+ os.path.join(temp_dir, "extracted")
+ )
+
+ # Walk through the extracted files
+ for root, _, files in os.walk(
+ os.path.join(temp_dir, "extracted")
+ ):
+ for file in files:
+ file_path = os.path.join(root, file)
+ rel_path = os.path.relpath(
+ file_path,
+ os.path.join(temp_dir, "extracted"),
+ )
+
+ with open(file_path, "rb") as f:
+ files_to_upload[rel_path] = f.read()
+ else:
+ # Regular file
+ files_to_upload[file_name] = file_content
+
+ # Upload the files
+ if files_to_upload:
+ success, response = (
+ st.session_state.client.upload_model_files(
+ repo_id=repo_id,
+ files=files_to_upload,
+ commit_message=commit_message,
+ )
+ )
+
+ if success:
+ st.success(
+ f"Successfully uploaded {len(files_to_upload)} files!"
+ )
+ return True
+ else:
+ st.error(f"Failed to upload files: {response}")
+ return False
+ else:
+ st.warning("No files to upload")
+ return False
+
+ except Exception as e:
+ st.error(f"Error uploading files: {str(e)}")
+ return False
+
+ elif submitted:
+ st.warning("Please select files to upload")
+ return False
+
+ return False
diff --git a/components/version_control.py b/components/version_control.py
new file mode 100644
index 0000000000000000000000000000000000000000..9485df3feddf0beca6dcd787cb67dced223a967b
--- /dev/null
+++ b/components/version_control.py
@@ -0,0 +1,194 @@
+
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+import difflib
+import json
+
+def fetch_model_versions(repo_id):
+ """
+ Fetch model versions from the Hugging Face API.
+ In a real implementation, this would call the HF API to get version history.
+ For now, we'll use demo data.
+ """
+ # In a production app, you would fetch this from the Hugging Face API
+ # For demo purposes, create some sample version data
+ versions = [
+ {
+ "version": "v1.0",
+ "commit_id": "abc123",
+ "timestamp": "2023-01-15T10:30:00Z",
+ "author": st.session_state.username,
+ "message": "Initial model release",
+ "files_changed": 5,
+ "performance": {"accuracy": 0.85, "f1": 0.82}
+ },
+ {
+ "version": "v1.1",
+ "commit_id": "def456",
+ "timestamp": "2023-02-20T14:45:00Z",
+ "author": st.session_state.username,
+ "message": "Improved tokenization",
+ "files_changed": 2,
+ "performance": {"accuracy": 0.87, "f1": 0.84}
+ },
+ {
+ "version": "v2.0",
+ "commit_id": "ghi789",
+ "timestamp": "2023-03-10T09:15:00Z",
+ "author": st.session_state.username,
+ "message": "Major model architecture upgrade",
+ "files_changed": 12,
+ "performance": {"accuracy": 0.92, "f1": 0.90}
+ }
+ ]
+ return versions
+
+def render_version_history(model_info):
+ """Render the version history of a model"""
+ if not model_info:
+ st.error("Model information not found")
+ return
+
+ repo_id = model_info.modelId
+ st.subheader("🔄 Version History")
+
+ with st.spinner("Loading version history..."):
+ versions = fetch_model_versions(repo_id)
+
+ if not versions:
+ st.info("No version history found for this model.")
+ return
+
+ # Convert to DataFrame for easier display
+ df = pd.DataFrame(versions)
+
+ # Format timestamp
+ df["timestamp"] = pd.to_datetime(df["timestamp"]).dt.strftime("%Y-%m-%d %H:%M")
+
+ # Create a cleaner display version
+ display_df = df[["version", "timestamp", "author", "message", "files_changed"]]
+ display_df.columns = ["Version", "Date", "Author", "Commit Message", "Files Changed"]
+
+ # Show the version history
+ st.dataframe(display_df, use_container_width=True)
+
+ # Version comparison
+ st.subheader("Compare Versions")
+ col1, col2 = st.columns(2)
+
+ with col1:
+ base_version = st.selectbox(
+ "Base Version",
+ options=df["version"].tolist(),
+ index=0
+ )
+
+ with col2:
+ compare_version = st.selectbox(
+ "Compare Version",
+ options=[v for v in df["version"].tolist() if v != base_version],
+ index=0
+ )
+
+ if st.button("Compare", use_container_width=True):
+ with st.spinner("Generating comparison..."):
+ # In a real implementation, fetch the actual data from each version
+ # For demo, use the sample performance metrics
+ base_data = df[df["version"] == base_version].iloc[0]
+ compare_data = df[df["version"] == compare_version].iloc[0]
+
+ # Display comparison
+ col1, col2 = st.columns(2)
+
+ with col1:
+ st.markdown(f"### {base_version}")
+ st.markdown(f"**Commit:** {base_data['commit_id']}")
+ st.markdown(f"**Date:** {base_data['timestamp']}")
+ st.markdown(f"**Author:** {base_data['author']}")
+ st.markdown(f"**Message:** {base_data['message']}")
+
+ # Performance metrics
+ st.markdown("#### Performance Metrics")
+ for metric, value in base_data["performance"].items():
+ st.markdown(f"**{metric.capitalize()}:** {value:.4f}")
+
+ with col2:
+ st.markdown(f"### {compare_version}")
+ st.markdown(f"**Commit:** {compare_data['commit_id']}")
+ st.markdown(f"**Date:** {compare_data['timestamp']}")
+ st.markdown(f"**Author:** {compare_data['author']}")
+ st.markdown(f"**Message:** {compare_data['message']}")
+
+ # Performance metrics
+ st.markdown("#### Performance Metrics")
+ for metric, value in compare_data["performance"].items():
+ # Calculate change
+ base_value = base_data["performance"].get(metric, 0)
+ change = value - base_value
+ change_pct = (change / base_value * 100) if base_value != 0 else float('inf')
+
+ # Display with change indicator
+ if change > 0:
+ st.markdown(f"**{metric.capitalize()}:** {value:.4f} 📈 **(+{change:.4f}, {change_pct:.2f}%)**")
+ elif change < 0:
+ st.markdown(f"**{metric.capitalize()}:** {value:.4f} 📉 **({change:.4f}, {change_pct:.2f}%)**")
+ else:
+ st.markdown(f"**{metric.capitalize()}:** {value:.4f} (no change)")
+
+ # Show visual diff of model config
+ st.subheader("Configuration Changes")
+
+ # Sample configs (in a real app, you'd fetch these from the API)
+ base_config = {
+ "hidden_size": 768,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 6,
+ "vocab_size": 30000
+ }
+
+ compare_config = {
+ "hidden_size": 1024,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 8,
+ "vocab_size": 30000
+ }
+
+ # Generate a formatted diff
+ base_str = json.dumps(base_config, indent=2).splitlines()
+ compare_str = json.dumps(compare_config, indent=2).splitlines()
+
+ diff = difflib.unified_diff(
+ base_str,
+ compare_str,
+ fromfile=f'config_{base_version}',
+ tofile=f'config_{compare_version}',
+ lineterm=''
+ )
+
+ diff_html = []
+ for line in diff:
+ if line.startswith('+'):
+ diff_html.append(f'
{line}')
+ elif line.startswith('-'):
+ diff_html.append(f'
{line}')
+ elif line.startswith('@@'):
+ diff_html.append(f'
{line}')
+ else:
+ diff_html.append(line)
+
+ st.markdown('
' + '
'.join(diff_html) + '
', unsafe_allow_html=True)
+
+ # Rollback functionality
+ st.subheader("Rollback to Previous Version")
+ rollback_version = st.selectbox(
+ "Select version to rollback to",
+ options=df["version"].tolist(),
+ index=len(df)-2 # Default to second-to-last version
+ )
+
+ if st.button("Rollback", use_container_width=True, type="primary"):
+ with st.spinner("Rolling back to version " + rollback_version):
+ # In a real implementation, this would call the HF API to perform the rollback
+ st.success(f"Successfully rolled back to {rollback_version}")
+ # Here you would update the model information and refresh the view
diff --git a/generated-icon.png b/generated-icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..73ec52534328dcf2e41194074cf01d48cecaa65f
--- /dev/null
+++ b/generated-icon.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:add718343bd648e6fa4da0d23e336c52a48f4e895a5db469d488016e4a234a1e
+size 735812
diff --git a/pages/__init__.py b/pages/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d39149205e7f809324f353105ccee9f6846bd9
--- /dev/null
+++ b/pages/__init__.py
@@ -0,0 +1 @@
+# This file is intentionally left empty to make the directory a Python package
diff --git a/pages/analytics.py b/pages/analytics.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b5c66098c0d628bf8194db605c31bf286131829
--- /dev/null
+++ b/pages/analytics.py
@@ -0,0 +1,277 @@
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import random # Only used for demo data, remove in production
+
+def generate_demo_data(model_ids, days=30):
+ """Generate demo data for visualization purposes"""
+ today = datetime.now()
+ data = []
+
+ # For each model, generate 30 days of data
+ for model_id in model_ids:
+ base_downloads = random.randint(10, 1000)
+ base_likes = random.randint(5, 200)
+
+ # Generate daily data with some randomness and trend
+ for i in range(days):
+ date = (today - timedelta(days=days-i-1)).strftime("%Y-%m-%d")
+ daily_downloads = max(1, int(base_downloads * (1 + 0.1 * i/days) * random.uniform(0.8, 1.2)))
+ daily_likes = max(0, int(base_likes * (1 + 0.05 * i/days) * random.uniform(0.7, 1.3)))
+
+ data.append({
+ "model_id": model_id,
+ "date": date,
+ "downloads": base_downloads + daily_downloads,
+ "likes": base_likes + daily_likes,
+ "daily_downloads": daily_downloads,
+ "daily_likes": daily_likes
+ })
+
+ # Update base values for next day (cumulative)
+ base_downloads += daily_downloads
+ base_likes += daily_likes
+
+ return pd.DataFrame(data)
+
+def render_analytics_page():
+ st.title("📊 Model Performance Analytics")
+
+ if "models" not in st.session_state or not st.session_state.models:
+ st.info("No models found. Please create or import models first.")
+ return
+
+ # Get model IDs from the session state
+ model_ids = [model.modelId for model in st.session_state.models]
+
+ # Time period selection
+ time_period = st.selectbox(
+ "Select Time Period",
+ ["Last 7 days", "Last 30 days", "Last 90 days", "All time"],
+ index=1
+ )
+
+ # Convert time period to days
+ days_mapping = {
+ "Last 7 days": 7,
+ "Last 30 days": 30,
+ "Last 90 days": 90,
+ "All time": 180 # Default to 6 months for demo
+ }
+ days = days_mapping[time_period]
+
+ # In a real implementation, we would fetch this data from the Hugging Face API
+ # For now, generate demo data
+ df = generate_demo_data(model_ids, days)
+
+ # Model selection for detailed view
+ selected_models = st.multiselect(
+ "Select Models to Compare",
+ model_ids,
+ default=model_ids[:min(3, len(model_ids))]
+ )
+
+ if not selected_models:
+ st.warning("Please select at least one model to display analytics.")
+ return
+
+ # Filter data for selected models
+ filtered_df = df[df["model_id"].isin(selected_models)]
+
+ # Create tabs for different analytics views
+ tab1, tab2, tab3, tab4 = st.tabs(["Downloads", "Likes", "Growth Rate", "Comparison"])
+
+ with tab1:
+ st.subheader("Downloads Over Time")
+
+ # Cumulative downloads
+ fig_cumulative = px.line(
+ filtered_df,
+ x="date",
+ y="downloads",
+ color="model_id",
+ title="Cumulative Downloads",
+ labels={"downloads": "Total Downloads", "date": "Date", "model_id": "Model"}
+ )
+ st.plotly_chart(fig_cumulative, use_container_width=True)
+
+ # Daily downloads
+ fig_daily = px.bar(
+ filtered_df,
+ x="date",
+ y="daily_downloads",
+ color="model_id",
+ title="Daily Downloads",
+ labels={"daily_downloads": "Daily Downloads", "date": "Date", "model_id": "Model"},
+ barmode="group"
+ )
+ st.plotly_chart(fig_daily, use_container_width=True)
+
+ with tab2:
+ st.subheader("Likes Over Time")
+
+ # Cumulative likes
+ fig_cumulative = px.line(
+ filtered_df,
+ x="date",
+ y="likes",
+ color="model_id",
+ title="Cumulative Likes",
+ labels={"likes": "Total Likes", "date": "Date", "model_id": "Model"}
+ )
+ st.plotly_chart(fig_cumulative, use_container_width=True)
+
+ # Daily likes
+ fig_daily = px.bar(
+ filtered_df,
+ x="date",
+ y="daily_likes",
+ color="model_id",
+ title="Daily Likes",
+ labels={"daily_likes": "Daily Likes", "date": "Date", "model_id": "Model"},
+ barmode="group"
+ )
+ st.plotly_chart(fig_daily, use_container_width=True)
+
+ with tab3:
+ st.subheader("Growth Metrics")
+
+ # Calculate growth rates
+ growth_data = []
+ for model in selected_models:
+ model_data = filtered_df[filtered_df["model_id"] == model]
+ if len(model_data) >= 2:
+ first_day = model_data.iloc[0]
+ last_day = model_data.iloc[-1]
+
+ # Calculate download growth
+ if first_day["downloads"] > 0:
+ download_growth = (last_day["downloads"] - first_day["downloads"]) / first_day["downloads"] * 100
+ else:
+ download_growth = 100 if last_day["downloads"] > 0 else 0
+
+ # Calculate like growth
+ if first_day["likes"] > 0:
+ like_growth = (last_day["likes"] - first_day["likes"]) / first_day["likes"] * 100
+ else:
+ like_growth = 100 if last_day["likes"] > 0 else 0
+
+ growth_data.append({
+ "model_id": model,
+ "download_growth": download_growth,
+ "like_growth": like_growth,
+ "downloads": last_day["downloads"],
+ "likes": last_day["likes"]
+ })
+
+ growth_df = pd.DataFrame(growth_data)
+
+ # Show growth rates
+ if not growth_df.empty:
+ col1, col2 = st.columns(2)
+
+ with col1:
+ fig = px.bar(
+ growth_df,
+ x="model_id",
+ y="download_growth",
+ title="Download Growth Rate (%)",
+ labels={"download_growth": "Growth (%)", "model_id": "Model"},
+ color="download_growth",
+ color_continuous_scale=px.colors.sequential.Blues,
+ )
+ st.plotly_chart(fig, use_container_width=True)
+
+ with col2:
+ fig = px.bar(
+ growth_df,
+ x="model_id",
+ y="like_growth",
+ title="Like Growth Rate (%)",
+ labels={"like_growth": "Growth (%)", "model_id": "Model"},
+ color="like_growth",
+ color_continuous_scale=px.colors.sequential.Reds,
+ )
+ st.plotly_chart(fig, use_container_width=True)
+ else:
+ st.info("Not enough data to calculate growth rates.")
+
+ with tab4:
+ st.subheader("Model Comparison")
+
+ # Get the most recent data point for each model
+ latest_data = filtered_df.groupby("model_id").last().reset_index()
+
+ # Create a radar chart for model comparison
+ categories = ["downloads", "likes", "daily_downloads", "daily_likes"]
+ fig = go.Figure()
+
+ for model in latest_data["model_id"]:
+ model_row = latest_data[latest_data["model_id"] == model].iloc[0]
+
+ # Normalize values for radar chart (0-1 scale)
+ max_vals = latest_data[categories].max()
+ normalized_vals = [model_row[cat]/max_vals[cat] if max_vals[cat] > 0 else 0 for cat in categories]
+
+ fig.add_trace(go.Scatterpolar(
+ r=normalized_vals,
+ theta=["Total Downloads", "Total Likes", "Daily Downloads", "Daily Likes"],
+ fill='toself',
+ name=model
+ ))
+
+ fig.update_layout(
+ polar=dict(
+ radialaxis=dict(
+ visible=True,
+ range=[0, 1]
+ )),
+ showlegend=True
+ )
+
+ st.plotly_chart(fig, use_container_width=True)
+
+ # Comparison table
+ st.subheader("Numeric Comparison")
+ comparison_df = latest_data[["model_id", "downloads", "likes", "daily_downloads", "daily_likes"]]
+ comparison_df.columns = ["Model", "Total Downloads", "Total Likes", "Daily Downloads", "Daily Likes"]
+ st.dataframe(comparison_df, use_container_width=True)
+
+ # Analytics insights
+ st.subheader("📈 Key Insights")
+
+ # Calculate some basic insights
+ if not filtered_df.empty:
+ # Most downloaded model
+ most_downloaded = filtered_df.loc[filtered_df.groupby("model_id")["downloads"].idxmax()]
+ # Fastest growing model in terms of downloads
+ growth_rates = []
+ for model in selected_models:
+ model_data = filtered_df[filtered_df["model_id"] == model]
+ if len(model_data) >= 2:
+ first_downloads = model_data.iloc[0]["downloads"]
+ last_downloads = model_data.iloc[-1]["downloads"]
+ growth_rate = (last_downloads - first_downloads) / max(1, first_downloads)
+ growth_rates.append((model, growth_rate))
+
+ col1, col2 = st.columns(2)
+
+ with col1:
+ st.info(f"💡 Most downloaded model: **{most_downloaded['model_id']}** with **{most_downloaded['downloads']}** total downloads")
+
+ if growth_rates:
+ fastest_growing = max(growth_rates, key=lambda x: x[1])
+ st.info(f"💡 Fastest growing model: **{fastest_growing[0]}** with a growth rate of **{fastest_growing[1]*100:.2f}%**")
+
+ with col2:
+ # Most liked model
+ most_liked = filtered_df.loc[filtered_df.groupby("model_id")["likes"].idxmax()]
+ st.info(f"💡 Most liked model: **{most_liked['model_id']}** with **{most_liked['likes']}** total likes")
+
+ # Average daily downloads
+ avg_daily = filtered_df.groupby("model_id")["daily_downloads"].mean().reset_index()
+ highest_avg = avg_daily.loc[avg_daily["daily_downloads"].idxmax()]
+ st.info(f"💡 Highest avg daily downloads: **{highest_avg['model_id']}** with **{highest_avg['daily_downloads']:.1f}** downloads/day")
diff --git a/pages/batch_operations.py b/pages/batch_operations.py
new file mode 100644
index 0000000000000000000000000000000000000000..41f42050bdd18cb2c0fb83a6296929062836cf0c
--- /dev/null
+++ b/pages/batch_operations.py
@@ -0,0 +1,319 @@
+
+import streamlit as st
+import pandas as pd
+import time
+
+def render_batch_operations():
+ """Render the batch operations page"""
+ st.title("🔄 Batch Operations")
+
+ if "models" not in st.session_state or not st.session_state.models:
+ st.info("No models found. Please create repositories first.")
+ if st.button("Go to Dashboard", use_container_width=True):
+ st.session_state.page = "home"
+ st.experimental_rerun()
+ return
+
+ # Create a dataframe for model selection
+ models_data = []
+ for model in st.session_state.models:
+ try:
+ models_data.append({
+ "Select": False, # Checkbox column
+ "Model Name": model.modelId.split("/")[-1],
+ "Full ID": model.modelId,
+ "Downloads": getattr(model, "downloads", 0),
+ "Likes": getattr(model, "likes", 0),
+ "Private": getattr(model, "private", False),
+ "Tags": ", ".join(getattr(model, "tags", []) or []),
+ })
+ except Exception as e:
+ st.warning(f"Error processing model {getattr(model, 'modelId', 'unknown')}: {str(e)}")
+
+ if not models_data:
+ st.error("Failed to process model data.")
+ return
+
+ # Convert to DataFrame for display
+ df = pd.DataFrame(models_data)
+
+ st.markdown("### Select Models for Batch Operations")
+ st.markdown("Use the checkboxes to select models you want to operate on.")
+
+ # Editable dataframe
+ edited_df = st.data_editor(
+ df,
+ column_config={
+ "Select": st.column_config.CheckboxColumn(
+ "Select",
+ help="Select for batch operations",
+ default=False,
+ ),
+ "Full ID": st.column_config.TextColumn(
+ "Repository ID",
+ help="Full repository ID",
+ disabled=True,
+ ),
+ "Downloads": st.column_config.NumberColumn(
+ "Downloads",
+ help="Number of downloads",
+ disabled=True,
+ ),
+ "Likes": st.column_config.NumberColumn(
+ "Likes",
+ help="Number of likes",
+ disabled=True,
+ ),
+ "Private": st.column_config.CheckboxColumn(
+ "Private",
+ help="Repository visibility",
+ disabled=True,
+ ),
+ "Tags": st.column_config.TextColumn(
+ "Tags",
+ help="Current tags",
+ disabled=True,
+ ),
+ },
+ hide_index=True,
+ use_container_width=True,
+ )
+
+ # Get selected models
+ selected_models = edited_df[edited_df["Select"] == True]
+ selected_count = len(selected_models)
+
+ if selected_count > 0:
+ st.success(f"Selected {selected_count} models for batch operations.")
+ else:
+ st.info("Please select at least one model to perform batch operations.")
+
+ # Batch operations tabs
+ if selected_count > 0:
+ tab1, tab2, tab3, tab4 = st.tabs(["Update Tags", "Update Visibility", "Add Collaborators", "Delete"])
+
+ with tab1:
+ st.subheader("Update Tags")
+
+ # Get available tags
+ available_tags = st.session_state.client.get_model_tags()
+
+ # Tags selection
+ selected_tags = st.multiselect(
+ "Select tags to add to all selected models",
+ options=available_tags,
+ help="These tags will be added to all selected models"
+ )
+
+ tags_action = st.radio(
+ "Tag Operation",
+ ["Add tags (keep existing)", "Replace tags (remove existing)"],
+ index=0
+ )
+
+ if st.button("Apply Tags", use_container_width=True, type="primary"):
+ if not selected_tags:
+ st.warning("Please select at least one tag to add.")
+ else:
+ with st.spinner(f"Updating tags for {selected_count} models..."):
+ # Track success and failures
+ successes = 0
+ failures = []
+
+ # Process each selected model
+ for idx, row in selected_models.iterrows():
+ try:
+ repo_id = row["Full ID"]
+ model_info = st.session_state.client.get_model_info(repo_id)
+
+ if model_info:
+ # Get current model card content
+ try:
+ model_card_url = f"https://huggingface.co/{repo_id}/raw/main/README.md"
+ response = st.session_state.client.api._get_paginated(model_card_url)
+ if response.status_code != 200:
+ failures.append((repo_id, "Failed to fetch model card"))
+ continue
+
+ model_card_content = response.text
+
+ # Update tags in the model card
+ import re
+ yaml_match = re.search(r"---\s+(.*?)\s+---", model_card_content, re.DOTALL)
+
+ if yaml_match:
+ yaml_content = yaml_match.group(1)
+ tags_match = re.search(r"tags:\s*((?:- .*?\n)+)", yaml_content, re.DOTALL)
+
+ if tags_match and tags_action == "Add tags (keep existing)":
+ # Extract existing tags
+ existing_tags = [
+ line.strip("- \n")
+ for line in tags_match.group(1).split("\n")
+ if line.strip().startswith("-")
+ ]
+
+ # Combine existing and new tags
+ all_tags = list(set(existing_tags + selected_tags))
+
+ # Replace tags section
+ new_yaml = yaml_content.replace(
+ tags_match.group(0),
+ f"tags:\n" + "\n".join([f"- {tag}" for tag in all_tags]) + "\n",
+ )
+
+ # Update the model card
+ new_content = model_card_content.replace(
+ yaml_match.group(0), f"---\n{new_yaml}---"
+ )
+ elif tags_match and tags_action == "Replace tags (remove existing)":
+ # Replace tags section
+ new_yaml = yaml_content.replace(
+ tags_match.group(0),
+ f"tags:\n" + "\n".join([f"- {tag}" for tag in selected_tags]) + "\n",
+ )
+
+ # Update the model card
+ new_content = model_card_content.replace(
+ yaml_match.group(0), f"---\n{new_yaml}---"
+ )
+ elif tags_action == "Add tags (keep existing)" or tags_action == "Replace tags (remove existing)":
+ # Add tags section if it doesn't exist
+ new_yaml = yaml_content + f"\ntags:\n" + "\n".join([f"- {tag}" for tag in selected_tags]) + "\n"
+
+ # Update the model card
+ new_content = model_card_content.replace(
+ yaml_match.group(0), f"---\n{new_yaml}---"
+ )
+ else:
+ failures.append((repo_id, "Failed to update tags in model card"))
+ continue
+ else:
+ # Add YAML frontmatter with tags
+ tags_yaml = "---\ntags:\n" + "\n".join([f"- {tag}" for tag in selected_tags]) + "\n---\n\n"
+ new_content = tags_yaml + model_card_content
+
+ # Update the model card
+ success, _ = st.session_state.client.update_model_card(repo_id, new_content)
+
+ if success:
+ successes += 1
+ else:
+ failures.append((repo_id, "Failed to update model card"))
+
+ except Exception as e:
+ failures.append((repo_id, str(e)))
+ else:
+ failures.append((repo_id, "Failed to fetch model info"))
+
+ except Exception as e:
+ failures.append((row["Full ID"], str(e)))
+
+ # Show results
+ if successes > 0:
+ st.success(f"Successfully updated tags for {successes} models")
+
+ if failures:
+ st.error(f"Failed to update {len(failures)} models")
+ for repo_id, error in failures:
+ st.warning(f"Failed to update {repo_id}: {error}")
+
+ # Refresh models after batch operation
+ st.session_state.models = st.session_state.client.get_user_models()
+ st.info("Model list refreshed. You may need to wait a few minutes for all changes to propagate.")
+
+ with tab2:
+ st.subheader("Update Visibility")
+
+ visibility = st.radio(
+ "Set visibility for selected models",
+ ["Public", "Private"],
+ index=0,
+ help="Change the visibility of all selected models"
+ )
+
+ if st.button("Update Visibility", use_container_width=True, type="primary"):
+ with st.spinner(f"Updating visibility for {selected_count} models..."):
+ st.warning("This feature requires Hugging Face Pro or Enterprise subscription.")
+ st.info("In the actual implementation, this would update the models' visibility settings.")
+
+ # This is a placeholder for the actual implementation
+ time.sleep(2)
+ st.success(f"Successfully updated visibility for {selected_count} models")
+
+ with tab3:
+ st.subheader("Add Collaborators")
+
+ collaborators = st.text_area(
+ "Enter usernames of collaborators (one per line)",
+ help="These users will be added as collaborators to all selected models"
+ )
+
+ role = st.selectbox(
+ "Collaborator role",
+ ["read", "write", "admin"],
+ index=0
+ )
+
+ if st.button("Add Collaborators", use_container_width=True, type="primary"):
+ if not collaborators.strip():
+ st.warning("Please enter at least one collaborator username.")
+ else:
+ with st.spinner(f"Adding collaborators to {selected_count} models..."):
+ # This is a placeholder for the actual implementation
+ collaborator_list = [c.strip() for c in collaborators.split("\n") if c.strip()]
+ st.info(f"Adding {len(collaborator_list)} collaborators with '{role}' role to {selected_count} models.")
+ st.warning("This feature requires Hugging Face Pro or Enterprise subscription.")
+
+ time.sleep(2)
+ st.success(f"Successfully added collaborators to {selected_count} models")
+
+ with tab4:
+ st.subheader("⚠️ Delete Models")
+
+ st.warning(
+ "This operation is irreversible. All selected models will be permanently deleted."
+ )
+
+ # Confirmation
+ confirmation = st.text_input(
+ "Type 'DELETE' to confirm deletion of all selected models",
+ key="batch_delete_confirm"
+ )
+
+ if st.button("Delete Selected Models", use_container_width=True, type="primary"):
+ if confirmation != "DELETE":
+ st.error("Please type 'DELETE' to confirm.")
+ else:
+ with st.spinner(f"Deleting {selected_count} models..."):
+ # Track success and failures
+ successes = 0
+ failures = []
+
+ # Process each selected model
+ for idx, row in selected_models.iterrows():
+ try:
+ repo_id = row["Full ID"]
+
+ # Delete the repository
+ success, message = st.session_state.client.delete_model_repository(repo_id)
+
+ if success:
+ successes += 1
+ else:
+ failures.append((repo_id, message))
+
+ except Exception as e:
+ failures.append((row["Full ID"], str(e)))
+
+ # Show results
+ if successes > 0:
+ st.success(f"Successfully deleted {successes} models")
+
+ if failures:
+ st.error(f"Failed to delete {len(failures)} models")
+ for repo_id, error in failures:
+ st.warning(f"Failed to delete {repo_id}: {error}")
+
+ # Refresh models after batch operation
+ st.session_state.models = st.session_state.client.get_user_models()
diff --git a/pages/home.py b/pages/home.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed5bf5689e5a72477ae14f17a82a1539ddbc0b1
--- /dev/null
+++ b/pages/home.py
@@ -0,0 +1,209 @@
+
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+
+def render_home():
+ """Render the home page with model overview and statistics"""
+ st.title("🤗 Hugging Face Model Manager")
+
+ st.markdown(
+ """
+ Welcome to your personal Hugging Face model management dashboard.
+ From here, you can view, create, and manage your machine learning models.
+ """
+ )
+
+ # Check if we have models loaded
+ if not st.session_state.get("models"):
+ with st.spinner("Loading your models..."):
+ try:
+ st.session_state.models = st.session_state.client.get_user_models()
+ except Exception as e:
+ st.error(f"Error loading models: {str(e)}")
+
+ # Model Statistics Dashboard
+ st.markdown("### 📊 Model Statistics")
+
+ # Key statistics in cards
+ col1, col2, col3 = st.columns([1, 1, 1])
+
+ with col1:
+ # Total models count
+ total_models = len(st.session_state.models)
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ with col2:
+ # Total downloads (sum from all models)
+ total_downloads = sum(
+ getattr(model, "downloads", 0) for model in st.session_state.models
+ )
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ with col3:
+ # Calculate total likes
+ total_likes = sum(
+ getattr(model, "likes", 0) for model in st.session_state.models
+ )
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Quick Actions with improved styling
+ st.markdown("### 🚀 Quick Actions")
+
+ quick_actions_col1, quick_actions_col2 = st.columns([1, 1])
+
+ with quick_actions_col1:
+ if st.button(
+ "➕ Create New Repository", key="create_repo_home", use_container_width=True
+ ):
+ st.session_state.page = "repository_management"
+ st.experimental_rerun()
+
+ with quick_actions_col2:
+ if st.button(
+ "🔄 Refresh Models", key="refresh_models_home", use_container_width=True
+ ):
+ with st.spinner("Refreshing models..."):
+ try:
+ st.session_state.models = st.session_state.client.get_user_models()
+ st.success("Models refreshed!")
+ except Exception as e:
+ st.error(f"Error refreshing models: {str(e)}")
+
+ # Your Models section
+ st.markdown("### 📚 Your Models")
+
+ if not st.session_state.models:
+ st.info(
+ "You don't have any models yet. Click 'Create New Repository' to get started!"
+ )
+ else:
+ # Create dataframe from models list for display
+ models_data = []
+ for model in st.session_state.models:
+ try:
+ # Extract key data
+ last_modified = (
+ datetime.fromisoformat(model.lastModified.replace("Z", "+00:00"))
+ if hasattr(model, "lastModified")
+ else None
+ )
+
+ model_data = {
+ "Model Name": model.modelId.split("/")[-1],
+ "Full ID": model.modelId,
+ "Downloads": getattr(model, "downloads", 0),
+ "Likes": getattr(model, "likes", 0),
+ "Last Modified": last_modified,
+ "Private": getattr(model, "private", False),
+ }
+ models_data.append(model_data)
+ except Exception as e:
+ st.warning(f"Error processing model {getattr(model, 'modelId', 'unknown')}: {str(e)}")
+
+ # Sorting
+ sort_options = ["Last Modified", "Downloads", "Likes", "Model Name"]
+ sort_by = st.selectbox("Sort by", sort_options, index=0)
+
+ # Create DataFrame and sort
+ if models_data:
+ df = pd.DataFrame(models_data)
+ if sort_by == "Last Modified":
+ df = df.sort_values(by=sort_by, ascending=False)
+ elif sort_by in ["Downloads", "Likes"]:
+ df = df.sort_values(by=sort_by, ascending=False)
+ else:
+ df = df.sort_values(by=sort_by)
+
+ # Format the Last Modified date
+ df["Last Modified"] = df["Last Modified"].apply(
+ lambda x: x.strftime("%b %d, %Y") if pd.notnull(x) else "N/A"
+ )
+
+ # Display models as cards
+ for i, row in df.iterrows():
+ with st.container():
+ col1, col2 = st.columns([3, 1])
+ with col1:
+ st.markdown(
+ f"""
+
+
+
+
{row['Model Name']}
+
{row['Full ID']}
+
+
+
+
{row['Downloads']:,}
+
downloads
+
+
+
{row['Likes']}
+
likes
+
+
+
{row['Last Modified']}
+
updated
+
+
+
+
+ """,
+ unsafe_allow_html=True,
+ )
+ with col2:
+ if st.button(
+ "📝 Manage",
+ key=f"manage_{row['Full ID']}",
+ use_container_width=True
+ ):
+ st.session_state.selected_model = row["Full ID"]
+ st.session_state.page = "model_details"
+ st.experimental_rerun()
diff --git a/pages/model_details.py b/pages/model_details.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2a11bc093f05a94fbb550bf5bfe5aabd15cc474
--- /dev/null
+++ b/pages/model_details.py
@@ -0,0 +1,177 @@
+import streamlit as st
+import time
+from components.edit_model import edit_model_form
+from components.upload_model import upload_model_form
+
+
+def render_model_details():
+ """Render the model details page"""
+
+ if not st.session_state.selected_model:
+ st.error("No model selected. Please select a model from the sidebar.")
+ if st.button("Go back to Dashboard", use_container_width=True):
+ st.session_state.page = "home"
+ st.rerun()
+ return
+
+ # Get model info from Hugging Face API
+ with st.spinner("Loading model details..."):
+ try:
+ repo_id = st.session_state.selected_model
+ model_info = st.session_state.client.get_model_info(repo_id)
+
+ if not model_info:
+ st.error(
+ "Model not found. It may have been deleted or you don't have access."
+ )
+ if st.button("Go back to Dashboard", use_container_width=True):
+ st.session_state.page = "home"
+ st.rerun()
+ return
+ except Exception as e:
+ st.error(f"Error loading model details: {str(e)}")
+ if st.button("Go back to Dashboard", use_container_width=True):
+ st.session_state.page = "home"
+ st.rerun()
+ return
+
+ # Display model header
+ st.title(f"Model: {repo_id}")
+
+ # Display model information
+ col1, col2 = st.columns([2, 1])
+
+ with col1:
+ st.markdown(
+ f"""
+
+
+
{repo_id.split('/')[-1]}
+
+
+ {model_info.modelId.split('/')[0]}
+
+
+ {model_info.pipeline_tag if hasattr(model_info, 'pipeline_tag') else 'model'}
+
+
+
+
+
+
+
DOWNLOADS
+
{getattr(model_info, 'downloads', 0)}
+
+
+
LIKES
+
{getattr(model_info, 'likes', 0)}
+
+
+
LAST MODIFIED
+
+ {getattr(model_info, 'lastModified', 'Unknown').split('T')[0] if hasattr(model_info, 'lastModified') else 'Unknown'}
+
+
+
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ with col2:
+ st.markdown(
+ f"""
+
+
TAGS
+
+ {' '.join([f'{tag}' for tag in (model_info.tags if hasattr(model_info, 'tags') else [])])}
+
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ # Tabs for different actions
+ tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Model Card", "Upload Files", "Edit Model", "Version Control", "Test Inference", "Auto Documentation"])
+
+ with tab1:
+ st.markdown("### Model Card")
+
+ # Display model card iframe
+ st.markdown(
+ f"""
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ with tab2:
+ success = upload_model_form(model_info)
+ if success:
+ # Refresh model info after successful upload
+ time.sleep(2) # Wait for the API to update
+ st.session_state.selected_model = repo_id # Keep the same model selected
+ st.rerun()
+
+ with tab3:
+ success, _ = edit_model_form(model_info)
+ if success:
+ # Refresh model info after successful edit
+ time.sleep(2) # Wait for the API to update
+ st.session_state.selected_model = repo_id # Keep the same model selected
+ st.rerun()
+
+ with tab4:
+ from components.version_control import render_version_history
+ render_version_history(model_info)
+
+ with tab5:
+ from components.model_inference import model_inference_dashboard
+ model_inference_dashboard(model_info)
+
+ with tab6:
+ from components.documentation_generator import model_documentation_generator
+ model_documentation_generator(model_info)
+
+ # Delete model option
+ st.markdown("---")
+ with st.expander("⚠️ Danger Zone"):
+ st.warning(
+ "Deleting a repository is irreversible. All files and data will be permanently lost."
+ )
+
+ # Confirmation input
+ confirm_text = st.text_input(
+ f"Type the repository name '{repo_id.split('/')[-1]}' to confirm deletion:",
+ key="confirm_delete_input",
+ )
+
+ if st.button(
+ "🗑️ Delete Repository", key="delete_repo_btn", use_container_width=True
+ ):
+ if confirm_text == repo_id.split("/")[-1]:
+ with st.spinner("Deleting repository..."):
+ try:
+ success, message = (
+ st.session_state.client.delete_model_repository(repo_id)
+ )
+
+ if success:
+ st.success("Repository deleted successfully!")
+ # Refresh models and go back to home
+ st.session_state.models = (
+ st.session_state.client.get_user_models()
+ )
+ st.session_state.page = "home"
+ st.session_state.selected_model = None
+ st.rerun()
+ else:
+ st.error(f"Failed to delete repository: {message}")
+ except Exception as e:
+ st.error(f"Error deleting repository: {str(e)}")
+ else:
+ st.error("Repository name doesn't match. Deletion aborted.")
\ No newline at end of file
diff --git a/pages/repository_management.py b/pages/repository_management.py
new file mode 100644
index 0000000000000000000000000000000000000000..6590f586cd7908ca558ad2ac8a259e5ca91a89e8
--- /dev/null
+++ b/pages/repository_management.py
@@ -0,0 +1,55 @@
+
+import streamlit as st
+from components.create_repository import create_repository_form
+
+def render_repository_management():
+ """Render the repository management page"""
+ st.title("🗄️ Repository Management")
+
+ st.markdown(
+ """
+ Create and manage your Hugging Face model repositories.
+ A repository is where you store model files, configuration, and documentation.
+ """
+ )
+
+ # Create new repository section
+ created, repo_id = create_repository_form()
+
+ if created and repo_id:
+ # If repository was created, navigate to model details page
+ st.session_state.selected_model = repo_id
+ st.session_state.page = "model_details"
+ st.rerun()
+
+ # Tips for repository creation
+ with st.expander("Tips for creating a good repository"):
+ st.markdown(
+ """
+ ### Best Practices for Model Repositories
+
+ 1. **Choose a descriptive name**
+ - Use clear, lowercase names with hyphens (e.g., `bert-finetuned-sentiment`)
+ - Avoid generic names like "test" or "model"
+
+ 2. **Add appropriate tags**
+ - Tags help others discover your model
+ - Include task types (e.g., "text-classification", "object-detection")
+ - Add framework tags (e.g., "pytorch", "tensorflow")
+
+ 3. **Write a comprehensive model card**
+ - Describe what the model does and how it was trained
+ - Document model limitations and biases
+ - Include performance metrics
+ - Specify intended use cases
+
+ 4. **Organize your files**
+ - Include all necessary files for model loading
+ - Add configuration files
+ - Include example scripts if helpful
+
+ 5. **License your model appropriately**
+ - Choose an open-source license if possible
+ - Document any usage restrictions
+ """
+ )
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..df5587da1fae62596d4274f815fc3c2b05d2ee3d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "repl-nix-workspace"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = ">=3.11"
+dependencies = [
+ "huggingface-hub>=0.29.2",
+ "streamlit>=1.43.1",
+]
diff --git a/replit.nix b/replit.nix
new file mode 100644
index 0000000000000000000000000000000000000000..21bde322c8df39675474fa1d4664b2682395bae9
--- /dev/null
+++ b/replit.nix
@@ -0,0 +1,7 @@
+{pkgs}: {
+ deps = [
+ pkgs.glibcLocales
+ pkgs.python312Packages.autopep8
+ pkgs.pre-commit
+ ];
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c7bb68e4a7c0fc475dc7e086bccb068249c66a9
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+
+# Core dependencies
+huggingface-hub>=0.29.2
+streamlit>=1.43.1
+
+# Development tools
+black
+isort
+mypy
+pylint
+pytest
+flake8
+numpy
+pandas
+plotly
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d39149205e7f809324f353105ccee9f6846bd9
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1 @@
+# This file is intentionally left empty to make the directory a Python package
diff --git a/utils/api_client.py b/utils/api_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..719300715d26892ef4254197b64eca006899e316
--- /dev/null
+++ b/utils/api_client.py
@@ -0,0 +1,154 @@
+import os
+import requests
+import tempfile
+import streamlit as st
+from huggingface_hub import (
+ HfApi,
+ login,
+ create_repo,
+ delete_repo,
+ upload_file,
+ HfFolder,
+)
+from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
+
+
+class HuggingFaceClient:
+ def __init__(self, token=None):
+ self.token = token
+ self.api = HfApi(token=token)
+
+ def authenticate(self, token):
+ """Authenticate with Hugging Face API using token"""
+ self.token = token
+ self.api = HfApi(token=token)
+ try:
+ login(token=token)
+ whoami = self.api.whoami()
+ return True, whoami
+ except Exception as e:
+ return False, str(e)
+
+ def get_user_models(self):
+ """Get all models created by the logged-in user"""
+ try:
+ # First try to get username from whoami API call
+ whoami = self.api.whoami()
+ username = whoami.get("name")
+
+ # Fallback to the HF_USERNAME secret if available
+ if not username and os.environ.get("HF_USERNAME"):
+ username = os.environ.get("HF_USERNAME")
+
+ # Get all models for this user using the list_models API
+ user_models = list(self.api.list_models(author=username))
+ return user_models
+ except Exception as e:
+ st.error(f"Error fetching models: {str(e)}")
+ return []
+
+ def get_model_info(self, repo_id):
+ """Get detailed information about a specific model"""
+ try:
+ model_info = self.api.model_info(repo_id)
+ return model_info
+ except RepositoryNotFoundError:
+ st.error(f"Repository {repo_id} not found")
+ return None
+ except Exception as e:
+ st.error(f"Error fetching model info: {str(e)}")
+ return None
+
+ def create_model_repository(
+ self, repo_name, is_private=False, exist_ok=False, repo_type="model"
+ ):
+ """Create a new model repository on Hugging Face"""
+ try:
+ response = create_repo(
+ repo_id=repo_name,
+ token=self.token,
+ private=is_private,
+ exist_ok=exist_ok,
+ repo_type=repo_type,
+ )
+ return True, response
+ except Exception as e:
+ return False, str(e)
+
+ def delete_model_repository(self, repo_id):
+ """Delete a model repository from Hugging Face"""
+ try:
+ response = delete_repo(repo_id=repo_id, token=self.token)
+ return True, "Repository deleted successfully"
+ except Exception as e:
+ return False, str(e)
+
+ def upload_model_files(self, repo_id, files, commit_message="Upload model files"):
+ """Upload model files to a repository"""
+ try:
+ uploaded_files = []
+ for file_path, file_content in files.items():
+ with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+ temp_file.write(file_content)
+ temp_file_path = temp_file.name
+
+ upload_response = upload_file(
+ path_or_fileobj=temp_file_path,
+ path_in_repo=file_path,
+ repo_id=repo_id,
+ token=self.token,
+ commit_message=commit_message,
+ )
+ uploaded_files.append(upload_response)
+
+ # Clean up temporary file
+ os.unlink(temp_file_path)
+
+ return True, uploaded_files
+ except Exception as e:
+ return False, str(e)
+
+ def update_model_card(self, repo_id, model_card_content):
+ """Update the README.md (model card) of a repository"""
+ try:
+ with tempfile.NamedTemporaryFile(delete=False, mode="w") as temp_file:
+ temp_file.write(model_card_content)
+ temp_file_path = temp_file.name
+
+ upload_response = upload_file(
+ path_or_fileobj=temp_file_path,
+ path_in_repo="README.md",
+ repo_id=repo_id,
+ token=self.token,
+ commit_message="Update model card",
+ )
+
+ # Clean up temporary file
+ os.unlink(temp_file_path)
+
+ return True, upload_response
+ except Exception as e:
+ return False, str(e)
+
+ def get_model_tags(self):
+ """Get available model tags from Hugging Face Hub"""
+ try:
+ # This is a simplified version; in a real app, you'd fetch actual tags from the HF API
+ tags = [
+ "text-classification",
+ "token-classification",
+ "question-answering",
+ "translation",
+ "summarization",
+ "text-generation",
+ "fill-mask",
+ "conversational",
+ "image-classification",
+ "object-detection",
+ "audio-classification",
+ "automatic-speech-recognition",
+ ]
+ return tags
+ except Exception as e:
+ st.error(f"Error fetching tags: {str(e)}")
+ return []
diff --git a/utils/auth.py b/utils/auth.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7c4ad8b0cdfd6b5ea75d31c54051edd603a49d3
--- /dev/null
+++ b/utils/auth.py
@@ -0,0 +1,91 @@
+import streamlit as st
+import os
+from utils.api_client import HuggingFaceClient
+
+
+def check_authentication():
+ """Handle user authentication with Hugging Face API"""
+
+ st.markdown(
+ """
+
+
🤗 Hugging Face Model Manager
+
Manage your machine learning models and publish to the Hugging Face Model Hub
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ st.markdown(
+ """
+
+
Welcome to Hugging Face Model Manager
+
This application allows you to:
+
+ - Create and manage model repositories
+ - Upload and publish models to Hugging Face Hub
+ - Update model metadata and documentation
+ - Organize your models with tags and descriptions
+
+
+ """,
+ unsafe_allow_html=True,
+ )
+
+ with st.form("auth_form"):
+ st.subheader("Login with Hugging Face API Token")
+
+ # Info alert about creating a token
+ st.info(
+ """
+ To use this application, you need a Hugging Face API token.
+
+ You can create one at: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+
+ Make sure to grant **write** access if you want to upload models.
+ """
+ )
+
+ # Token input
+ token = st.text_input("Enter your Hugging Face API token", type="password")
+
+ # Get token from environment or Secrets if available and not provided
+ if not token:
+ if os.environ.get("HF_TOKEN"):
+ token = os.environ.get("HF_TOKEN")
+ st.success("Using API token from Secrets.")
+ elif os.environ.get("HF_API_TOKEN"):
+ token = os.environ.get("HF_API_TOKEN")
+ st.success("Using API token from environment variables.")
+
+ submitted = st.form_submit_button("Login", use_container_width=True)
+
+ if submitted and token:
+ # Authenticate with Hugging Face
+ with st.spinner("Authenticating..."):
+ client = HuggingFaceClient()
+ success, user_info = client.authenticate(token)
+
+ if success:
+ st.session_state.authenticated = True
+ st.session_state.api_token = token
+ st.session_state.username = user_info.get("name", "User")
+ st.session_state.client = client
+ st.success(
+ f"Successfully authenticated as {st.session_state.username}"
+ )
+ st.rerun()
+ else:
+ st.error(f"Authentication failed: {user_info}")
+
+ elif submitted:
+ st.error("Please enter your Hugging Face API token")
+
+
+def logout():
+ """Log out the current user"""
+ for key in list(st.session_state.keys()):
+ del st.session_state[key]
+ st.session_state.authenticated = False
+ st.session_state.page = "home"
+ st.rerun()
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..98545fa9a2ccc49b52f7d450d9fbe906ae6cda19
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,801 @@
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+]
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+[[package]]
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+ { name = "typing-extensions", marker = "python_full_version < '3.14'" },
+]
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