183 lines
7.6 KiB
Python
183 lines
7.6 KiB
Python
import streamlit as st
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import json
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from history_tree import HistoryTree
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from utils import save_json
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from streamlit_agraph import agraph, Node, Edge, Config
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def render_timeline_wip(data, file_path):
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tree_data = data.get("history_tree", {})
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if not tree_data:
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st.info("No history timeline exists.")
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return
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htree = HistoryTree(tree_data)
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# --- 1. BUILD GRAPH ---
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nodes = []
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edges = []
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sorted_nodes = sorted(htree.nodes.values(), key=lambda x: x["timestamp"])
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for n in sorted_nodes:
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nid = n["id"]
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note = n.get('note', 'Step')
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short_note = (note[:15] + '..') if len(note) > 15 else note
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color = "#ffffff"
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border = "#666666"
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if nid == htree.head_id:
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color = "#fff6cd"
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border = "#eebb00"
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if nid in htree.branches.values():
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if color == "#ffffff":
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color = "#e6ffe6"
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border = "#44aa44"
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nodes.append(Node(
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id=nid,
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label=f"{short_note}\n({nid[:4]})",
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size=25,
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shape="box",
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color=color,
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borderWidth=1,
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borderColor=border,
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font={'color': 'black', 'face': 'Arial', 'size': 14}
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))
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if n["parent"] and n["parent"] in htree.nodes:
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edges.append(Edge(
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source=n["parent"],
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target=nid,
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color="#aaaaaa",
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type="STRAIGHT"
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))
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# --- UPDATED CONFIGURATION ---
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config = Config(
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width="100%",
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# Increased height from 400px to 600px for better visibility
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height="600px",
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directed=True,
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physics=False,
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hierarchical=True,
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layout={
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"hierarchical": {
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"enabled": True,
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# Increased separation to widen the tree structure
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"levelSeparation": 200, # Was 150
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"nodeSpacing": 150, # Was 100
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"treeSpacing": 150, # Was 100
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"direction": "LR",
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"sortMethod": "directed"
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}
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}
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)
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st.subheader("✨ Interactive Timeline")
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st.caption("Click a node to view its settings below.")
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# --- FIX: REMOVED 'key' ARGUMENT ---
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selected_id = agraph(nodes=nodes, edges=edges, config=config)
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st.markdown("---")
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# --- 2. DETERMINE TARGET ---
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target_node_id = selected_id if selected_id else htree.head_id
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if target_node_id and target_node_id in htree.nodes:
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selected_node = htree.nodes[target_node_id]
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node_data = selected_node["data"]
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# Header
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c_h1, c_h2 = st.columns([3, 1])
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c_h1.markdown(f"### 📄 Previewing: {selected_node.get('note', 'Step')}")
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c_h1.caption(f"ID: {target_node_id}")
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# Restore Button
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with c_h2:
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st.write(""); st.write("")
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if st.button("⏪ Restore This Version", type="primary", use_container_width=True, key=f"rst_{target_node_id}"):
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# --- FIX: Cleanup 'batch_data' if restoring a Single File ---
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if "batch_data" not in node_data and "batch_data" in data:
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del data["batch_data"]
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# -------------------------------------------------------------
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data.update(node_data)
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htree.head_id = target_node_id
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data["history_tree"] = htree.to_dict()
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save_json(file_path, data)
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st.session_state.ui_reset_token += 1
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label = f"{selected_node.get('note')} ({target_node_id[:4]})"
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st.session_state.restored_indicator = label
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st.toast(f"Restored {target_node_id}!", icon="🔄")
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st.rerun()
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# --- 3. PREVIEW LOGIC (BATCH VS SINGLE) ---
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# Helper to render one set of inputs
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def render_preview_fields(item_data, prefix):
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# A. Prompts
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p_col1, p_col2 = st.columns(2)
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with p_col1:
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val_gp = item_data.get("general_prompt", "")
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st.text_area("General Positive", value=val_gp, height=80, disabled=True, key=f"{prefix}_gp")
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val_sp = item_data.get("current_prompt", "") or item_data.get("prompt", "")
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st.text_area("Specific Positive", value=val_sp, height=80, disabled=True, key=f"{prefix}_sp")
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with p_col2:
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val_gn = item_data.get("general_negative", "")
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st.text_area("General Negative", value=val_gn, height=80, disabled=True, key=f"{prefix}_gn")
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val_sn = item_data.get("negative", "")
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st.text_area("Specific Negative", value=val_sn, height=80, disabled=True, key=f"{prefix}_sn")
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# B. Settings
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s_col1, s_col2, s_col3 = st.columns(3)
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s_col1.text_input("Camera", value=str(item_data.get("camera", "static")), disabled=True, key=f"{prefix}_cam")
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s_col2.text_input("FLF", value=str(item_data.get("flf", "0.0")), disabled=True, key=f"{prefix}_flf")
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s_col3.text_input("Seed", value=str(item_data.get("seed", "-1")), disabled=True, key=f"{prefix}_seed")
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# C. LoRAs
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with st.expander("💊 LoRA Configuration", expanded=False):
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l1, l2, l3 = st.columns(3)
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with l1:
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st.text_input("L1 Name", value=item_data.get("lora 1 high", ""), disabled=True, key=f"{prefix}_l1h")
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st.text_input("L1 Str", value=str(item_data.get("lora 1 low", "")), disabled=True, key=f"{prefix}_l1l")
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with l2:
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st.text_input("L2 Name", value=item_data.get("lora 2 high", ""), disabled=True, key=f"{prefix}_l2h")
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st.text_input("L2 Str", value=str(item_data.get("lora 2 low", "")), disabled=True, key=f"{prefix}_l2l")
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with l3:
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st.text_input("L3 Name", value=item_data.get("lora 3 high", ""), disabled=True, key=f"{prefix}_l3h")
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st.text_input("L3 Str", value=str(item_data.get("lora 3 low", "")), disabled=True, key=f"{prefix}_l3l")
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# D. VACE
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vace_keys = ["frame_to_skip", "vace schedule", "video file path"]
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has_vace = any(k in item_data for k in vace_keys)
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if has_vace:
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with st.expander("🎞️ VACE / I2V Settings", expanded=False):
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v1, v2, v3 = st.columns(3)
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v1.text_input("Skip Frames", value=str(item_data.get("frame_to_skip", "")), disabled=True, key=f"{prefix}_fts")
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v2.text_input("Schedule", value=str(item_data.get("vace schedule", "")), disabled=True, key=f"{prefix}_vsc")
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v3.text_input("Video Path", value=str(item_data.get("video file path", "")), disabled=True, key=f"{prefix}_vid")
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# --- DETECT BATCH VS SINGLE ---
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batch_list = node_data.get("batch_data", [])
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if batch_list and isinstance(batch_list, list) and len(batch_list) > 0:
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st.info(f"📚 This snapshot contains {len(batch_list)} sequences.")
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for i, seq_data in enumerate(batch_list):
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seq_num = seq_data.get("sequence_number", i+1)
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with st.expander(f"🎬 Sequence #{seq_num}", expanded=(i==0)):
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# Unique prefix for every sequence in every node
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prefix = f"p_{target_node_id}_s{i}"
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render_preview_fields(seq_data, prefix)
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else:
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# Single File Preview
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prefix = f"p_{target_node_id}_single"
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render_preview_fields(node_data, prefix) |