import streamlit as st import json from history_tree import HistoryTree from utils import save_json from streamlit_agraph import agraph, Node, Edge, Config def render_timeline_wip(data, file_path): tree_data = data.get("history_tree", {}) if not tree_data: st.info("No history timeline exists.") return htree = HistoryTree(tree_data) # --- 1. BUILD GRAPH --- nodes = [] edges = [] sorted_nodes = sorted(htree.nodes.values(), key=lambda x: x["timestamp"]) for n in sorted_nodes: nid = n["id"] note = n.get('note', 'Step') short_note = (note[:15] + '..') if len(note) > 15 else note color = "#ffffff" border = "#666666" if nid == htree.head_id: color = "#fff6cd" border = "#eebb00" if nid in htree.branches.values(): if color == "#ffffff": color = "#e6ffe6" border = "#44aa44" nodes.append(Node( id=nid, label=f"{short_note}\n({nid[:4]})", size=25, shape="box", color=color, borderWidth=1, borderColor=border, font={'color': 'black', 'face': 'Arial', 'size': 14} )) if n["parent"] and n["parent"] in htree.nodes: edges.append(Edge( source=n["parent"], target=nid, color="#aaaaaa", type="STRAIGHT" )) # --- UPDATED CONFIGURATION --- config = Config( width="100%", # Increased height from 400px to 600px for better visibility height="600px", directed=True, physics=False, hierarchical=True, layout={ "hierarchical": { "enabled": True, # Increased separation to widen the tree structure "levelSeparation": 200, # Was 150 "nodeSpacing": 150, # Was 100 "treeSpacing": 150, # Was 100 "direction": "LR", "sortMethod": "directed" } } ) st.subheader("✨ Interactive Timeline") st.caption("Click a node to view its settings below.") # --- FIX: REMOVED 'key' ARGUMENT --- selected_id = agraph(nodes=nodes, edges=edges, config=config) st.markdown("---") # --- 2. DETERMINE TARGET --- target_node_id = selected_id if selected_id else htree.head_id if target_node_id and target_node_id in htree.nodes: selected_node = htree.nodes[target_node_id] node_data = selected_node["data"] # Header c_h1, c_h2 = st.columns([3, 1]) c_h1.markdown(f"### 📄 Previewing: {selected_node.get('note', 'Step')}") c_h1.caption(f"ID: {target_node_id}") # Restore Button with c_h2: st.write(""); st.write("") if st.button("⏪ Restore This Version", type="primary", use_container_width=True, key=f"rst_{target_node_id}"): # --- FIX: Cleanup 'batch_data' if restoring a Single File --- if "batch_data" not in node_data and "batch_data" in data: del data["batch_data"] # ------------------------------------------------------------- data.update(node_data) htree.head_id = target_node_id data["history_tree"] = htree.to_dict() save_json(file_path, data) st.session_state.ui_reset_token += 1 label = f"{selected_node.get('note')} ({target_node_id[:4]})" st.session_state.restored_indicator = label st.toast(f"Restored {target_node_id}!", icon="🔄") st.rerun() # --- 3. PREVIEW LOGIC (BATCH VS SINGLE) --- # Helper to render one set of inputs def render_preview_fields(item_data, prefix): # A. Prompts p_col1, p_col2 = st.columns(2) with p_col1: val_gp = item_data.get("general_prompt", "") st.text_area("General Positive", value=val_gp, 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: val_gn = item_data.get("general_negative", "") st.text_area("General Negative", value=val_gn, height=80, disabled=True, key=f"{prefix}_gn") val_sn = item_data.get("negative", "") st.text_area("Specific Negative", value=val_sn, height=80, disabled=True, key=f"{prefix}_sn") # B. 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") # C. 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") # D. VACE vace_keys = ["frame_to_skip", "vace schedule", "video file path"] has_vace = any(k in item_data for k in vace_keys) if has_vace: 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") # --- DETECT BATCH VS SINGLE --- batch_list = node_data.get("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)): # Unique prefix for every sequence in every node prefix = f"p_{target_node_id}_s{i}" render_preview_fields(seq_data, prefix) else: # Single File Preview prefix = f"p_{target_node_id}_single" render_preview_fields(node_data, prefix)