import streamlit as st import copy import time from history_tree import HistoryTree from utils import save_json, KEY_BATCH_DATA, KEY_HISTORY_TREE def render_timeline_tab(data, file_path): tree_data = data.get(KEY_HISTORY_TREE, {}) if not tree_data: st.info("No history timeline exists. Make some changes in the Editor first!") return htree = HistoryTree(tree_data) if 'restored_indicator' in st.session_state and st.session_state.restored_indicator: st.info(f"📍 Editing Restored Version: **{st.session_state.restored_indicator}**") # --- VIEW SWITCHER --- c_title, c_view = st.columns([2, 1]) c_title.subheader("🕰️ Version History") view_mode = c_view.radio( "View Mode", ["🌳 Horizontal", "🌲 Vertical", "📜 Linear Log"], horizontal=True, label_visibility="collapsed" ) # --- Build sorted node list (shared by all views) --- all_nodes = list(htree.nodes.values()) all_nodes.sort(key=lambda x: x["timestamp"], reverse=True) # --- RENDER GRAPH VIEWS --- if view_mode in ["🌳 Horizontal", "🌲 Vertical"]: direction = "LR" if view_mode == "🌳 Horizontal" else "TB" try: graph_dot = htree.generate_graph(direction=direction) st.graphviz_chart(graph_dot, use_container_width=True) except Exception as e: st.error(f"Graph Error: {e}") # --- RENDER LINEAR LOG VIEW --- elif view_mode == "📜 Linear Log": st.caption("A simple chronological list of all snapshots.") for n in all_nodes: is_head = (n["id"] == htree.head_id) with st.container(): c1, c2, c3 = st.columns([0.5, 4, 1]) with c1: st.markdown("### 📍" if is_head else "### ⚫") with c2: note_txt = n.get('note', 'Step') ts = time.strftime('%b %d %H:%M', time.localtime(n['timestamp'])) if is_head: st.markdown(f"**{note_txt}** (Current)") else: st.write(f"**{note_txt}**") st.caption(f"ID: {n['id'][:6]} • {ts}") with c3: if not is_head: if st.button("⏪", key=f"log_rst_{n['id']}", help="Restore this version"): _restore_node(data, n, htree, file_path) st.divider() st.markdown("---") # --- NODE SELECTOR --- col_sel, col_act = st.columns([3, 1]) def fmt_node(n): ts = time.strftime('%b %d %H:%M', time.localtime(n['timestamp'])) return f"{n.get('note', 'Step')} • {ts} ({n['id'][:6]})" with col_sel: current_idx = 0 for i, n in enumerate(all_nodes): if n["id"] == htree.head_id: current_idx = i break selected_node = st.selectbox( "Select Version to Manage:", all_nodes, format_func=fmt_node, index=current_idx ) if not selected_node: return node_data = selected_node["data"] # --- RESTORE --- with col_act: st.write(""); st.write("") if st.button("⏪ Restore Version", type="primary", use_container_width=True): _restore_node(data, selected_node, htree, file_path) # --- RENAME --- rn_col1, rn_col2 = st.columns([3, 1]) new_label = rn_col1.text_input("Rename Label", value=selected_node.get("note", "")) if rn_col2.button("Update Label"): selected_node["note"] = new_label data[KEY_HISTORY_TREE] = htree.to_dict() save_json(file_path, data) st.rerun() # --- DANGER ZONE --- st.markdown("---") with st.expander("⚠️ Danger Zone (Delete)"): st.warning("Deleting a node cannot be undone.") if st.button("🗑️ Delete This Node", type="primary"): if selected_node['id'] in htree.nodes: if "history_tree_backup" not in data: data["history_tree_backup"] = [] data["history_tree_backup"].append(copy.deepcopy(htree.to_dict())) del htree.nodes[selected_node['id']] for b, tip in list(htree.branches.items()): if tip == selected_node['id']: del htree.branches[b] if htree.head_id == selected_node['id']: if htree.nodes: fallback = sorted(htree.nodes.values(), key=lambda x: x["timestamp"])[-1] htree.head_id = fallback["id"] else: htree.head_id = None data[KEY_HISTORY_TREE] = htree.to_dict() save_json(file_path, data) st.toast("Node Deleted", icon="🗑️") st.rerun() # --- DATA PREVIEW --- st.markdown("---") with st.expander("🔍 Data Preview", expanded=False): batch_list = node_data.get(KEY_BATCH_DATA, []) if batch_list and isinstance(batch_list, list) and len(batch_list) > 0: st.info(f"📚 This snapshot contains {len(batch_list)} sequences.") for i, seq_data in enumerate(batch_list): seq_num = seq_data.get("sequence_number", i + 1) with st.expander(f"🎬 Sequence #{seq_num}", expanded=(i == 0)): prefix = f"p_{selected_node['id']}_s{i}" _render_preview_fields(seq_data, prefix) else: prefix = f"p_{selected_node['id']}_single" _render_preview_fields(node_data, prefix) def _restore_node(data, node, htree, file_path): """Restore a history node as the current version.""" node_data = node["data"] if KEY_BATCH_DATA not in node_data and KEY_BATCH_DATA in data: del data[KEY_BATCH_DATA] data.update(node_data) htree.head_id = node['id'] data[KEY_HISTORY_TREE] = htree.to_dict() save_json(file_path, data) st.session_state.ui_reset_token += 1 label = f"{node.get('note')} ({node['id'][:4]})" st.session_state.restored_indicator = label st.toast("Restored!", icon="🔄") st.rerun() def _render_preview_fields(item_data, prefix): """Render a read-only preview of prompts, settings, and LoRAs.""" # Prompts p_col1, p_col2 = st.columns(2) with p_col1: st.text_area("General Positive", value=item_data.get("general_prompt", ""), height=80, disabled=True, key=f"{prefix}_gp") val_sp = item_data.get("current_prompt", "") or item_data.get("prompt", "") st.text_area("Specific Positive", value=val_sp, height=80, disabled=True, key=f"{prefix}_sp") with p_col2: st.text_area("General Negative", value=item_data.get("general_negative", ""), height=80, disabled=True, key=f"{prefix}_gn") st.text_area("Specific Negative", value=item_data.get("negative", ""), height=80, disabled=True, key=f"{prefix}_sn") # Settings s_col1, s_col2, s_col3 = st.columns(3) s_col1.text_input("Camera", value=str(item_data.get("camera", "static")), disabled=True, key=f"{prefix}_cam") s_col2.text_input("FLF", value=str(item_data.get("flf", "0.0")), disabled=True, key=f"{prefix}_flf") s_col3.text_input("Seed", value=str(item_data.get("seed", "-1")), disabled=True, key=f"{prefix}_seed") # LoRAs with st.expander("💊 LoRA Configuration", expanded=False): l1, l2, l3 = st.columns(3) with l1: st.text_input("L1 Name", value=item_data.get("lora 1 high", ""), disabled=True, key=f"{prefix}_l1h") st.text_input("L1 Str", value=str(item_data.get("lora 1 low", "")), disabled=True, key=f"{prefix}_l1l") with l2: st.text_input("L2 Name", value=item_data.get("lora 2 high", ""), disabled=True, key=f"{prefix}_l2h") st.text_input("L2 Str", value=str(item_data.get("lora 2 low", "")), disabled=True, key=f"{prefix}_l2l") with l3: st.text_input("L3 Name", value=item_data.get("lora 3 high", ""), disabled=True, key=f"{prefix}_l3h") st.text_input("L3 Str", value=str(item_data.get("lora 3 low", "")), disabled=True, key=f"{prefix}_l3l") # VACE vace_keys = ["frame_to_skip", "vace schedule", "video file path"] if any(k in item_data for k in vace_keys): with st.expander("🎞️ VACE / I2V Settings", expanded=False): v1, v2, v3 = st.columns(3) v1.text_input("Skip Frames", value=str(item_data.get("frame_to_skip", "")), disabled=True, key=f"{prefix}_fts") v2.text_input("Schedule", value=str(item_data.get("vace schedule", "")), disabled=True, key=f"{prefix}_vsc") v3.text_input("Video Path", value=str(item_data.get("video file path", "")), disabled=True, key=f"{prefix}_vid")