app.py

# Copyright 2025 Google LLC. Based on work by Yousif Ahmed.
# Concept: ChronoWeave - Branching Narrative Generation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

import streamlit as st
import google.generativeai as genai
import os
import json
import numpy as np
from io import BytesIO
import time
import wave
import contextlib
import asyncio
import uuid # For unique identifiers
import shutil # For directory operations
import logging # For better logging

# Image handling
from PIL import Image
# Pydantic for data validation
from pydantic import BaseModel, Field, ValidationError, field_validator, model_validator
from typing import List, Optional, Literal, Dict, Any

# Video and audio processing
from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips
# from moviepy.config import change_settings # Potential for setting imagemagick path if needed

# Type hints
import typing_extensions as typing

# Async support for Streamlit/Google API
import nest_asyncio
nest_asyncio.apply() # Apply patch for asyncio in environments like Streamlit/Jupyter

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# --- Configuration ---
st.set_page_config(page_title="ChronoWeave", layout="wide", initial_sidebar_state="expanded")
st.title("🌀 ChronoWeave: Advanced Branching Narrative Generator")
st.markdown("""
Generate multiple, branching story timelines from a single theme using AI, complete with images and narration.
*Based on the work by Yousif Ahmed. Copyright 2025 Google LLC.*
""")

# --- Constants ---
# Text/JSON Model
TEXT_MODEL_ID = "models/gemini-1.5-flash" # Or "gemini-1.5-pro"
# Audio Model Config
AUDIO_MODEL_ID = "models/gemini-1.5-flash" # Model used for audio tasks
AUDIO_SAMPLING_RATE = 24000
# Image Model Config
IMAGE_MODEL_ID = "imagen-3" # <<< NOTE: Likely needs Vertex AI SDK access
DEFAULT_ASPECT_RATIO = "1:1"
# Video Config
VIDEO_FPS = 24
VIDEO_CODEC = "libx264"
AUDIO_CODEC = "aac"
# File Management
TEMP_DIR_BASE = ".chrono_temp"

# --- API Key Handling ---
GOOGLE_API_KEY = None
try:
    GOOGLE_API_KEY = st.secrets["GOOGLE_API_KEY"]
    logger.info("Google API Key loaded from Streamlit secrets.")
except KeyError:
    GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
    if GOOGLE_API_KEY:
        logger.info("Google API Key loaded from environment variable.")
    else:
        st.error("🚨 **Google API Key Not Found!** Please configure it.", icon="🚨"); st.stop()

# --- Initialize Google Clients ---
try:
    genai.configure(api_key=GOOGLE_API_KEY)
    logger.info("Configured google-generativeai with API key.")
    client_standard = genai.GenerativeModel(TEXT_MODEL_ID)
    logger.info(f"Initialized text/JSON model handle: {TEXT_MODEL_ID}.")
    live_model = genai.GenerativeModel(AUDIO_MODEL_ID)
    logger.info(f"Initialized audio model handle: {AUDIO_MODEL_ID}.")
    image_model_genai = genai.GenerativeModel(IMAGE_MODEL_ID) # Retained but likely needs Vertex SDK
    logger.info(f"Initialized google-generativeai handle for image model: {IMAGE_MODEL_ID} (May require Vertex AI SDK).")
    # ---> TODO: Initialize Vertex AI client here if switching SDK <---

except AttributeError as ae:
     logger.exception("AttributeError during Client Init."); st.error(f"🚨 Init Error: {ae}. Update library?", icon="🚨"); st.stop()
except Exception as e:
    logger.exception("Failed to initialize Google Clients/Models."); st.error(f"🚨 Failed Init: {e}", icon="🚨"); st.stop()

# --- Define Pydantic Schemas (Using V2 Syntax) ---
class StorySegment(BaseModel):
    scene_id: int = Field(..., ge=0)
    image_prompt: str = Field(..., min_length=10, max_length=250)
    audio_text: str = Field(..., min_length=5, max_length=150)
    character_description: str = Field(..., max_length=250)
    timeline_visual_modifier: Optional[str] = Field(None, max_length=50)
    @field_validator('image_prompt')
    @classmethod
    def image_prompt_no_humans(cls, v: str) -> str:
        if any(w in v.lower() for w in ["person", "people", "human", "man", "woman", "boy", "girl", "child"]): logger.warning(f"Prompt '{v[:50]}...' may contain humans.")
        return v
class Timeline(BaseModel):
    timeline_id: int = Field(..., ge=0)
    divergence_reason: str = Field(..., min_length=5)
    segments: List[StorySegment] = Field(..., min_items=1)
class ChronoWeaveResponse(BaseModel):
    core_theme: str = Field(..., min_length=5)
    timelines: List[Timeline] = Field(..., min_items=1)
    total_scenes_per_timeline: int = Field(..., gt=0)
    @model_validator(mode='after')
    def check_timeline_segment_count(self) -> 'ChronoWeaveResponse':
        expected = self.total_scenes_per_timeline
        for i, t in enumerate(self.timelines):
            if len(t.segments) != expected: raise ValueError(f"Timeline {i} ID {t.timeline_id}: Expected {expected} segments, found {len(t.segments)}.")
        return self

# --- Helper Functions ---
@contextlib.contextmanager
def wave_file_writer(filename: str, channels: int = 1, rate: int = AUDIO_SAMPLING_RATE, sample_width: int = 2):
    """Context manager to safely write WAV files."""
    wf = None
    try:
        wf = wave.open(filename, "wb"); wf.setnchannels(channels); wf.setsampwidth(sample_width); wf.setframerate(rate)
        yield wf
    except Exception as e: logger.error(f"Error wave file {filename}: {e}"); raise
    finally:
        if wf: try: wf.close()
        except Exception as e_close: logger.error(f"Error closing wave file {filename}: {e_close}")


async def generate_audio_live_async(api_text: str, output_filename: str, voice: Optional[str] = None) -> Optional[str]:
    """Generates audio using Gemini Live API (async version) via the GenerativeModel."""
    collected_audio = bytearray(); task_id = os.path.basename(output_filename).split('.')[0]
    logger.info(f"🎙️ [{task_id}] Requesting audio: '{api_text[:60]}...'")
    try:
        # CORRECTED config structure for audio generation <<<<<<-------
        config = {
            "response_modalities": ["AUDIO"],
            # Removed 'audio_config' nesting
            "audio_encoding": "LINEAR16",
            "sample_rate_hertz": AUDIO_SAMPLING_RATE,
            # Add other parameters like "voice" here directly if needed
        }
        directive_prompt = f"Narrate directly: \"{api_text}\""
        async with live_model.connect(config=config) as session: # Pass corrected config
            await session.send_request([directive_prompt])
            async for response in session.stream_content():
                if response.audio_chunk and response.audio_chunk.data: collected_audio.extend(response.audio_chunk.data)
                if hasattr(response, 'error') and response.error: logger.error(f"   ❌ [{task_id}] Audio stream error: {response.error}"); st.error(f"Audio stream error {task_id}: {response.error}", icon="🔊"); return None
        if not collected_audio: logger.warning(f"⚠️ [{task_id}] No audio data received."); st.warning(f"No audio data for {task_id}.", icon="🔊"); return None
        with wave_file_writer(output_filename, rate=AUDIO_SAMPLING_RATE) as wf: wf.writeframes(bytes(collected_audio))
        logger.info(f"   ✅ [{task_id}] Audio saved: {os.path.basename(output_filename)} ({len(collected_audio)} bytes)")
        return output_filename
    except genai.types.generation_types.BlockedPromptException as bpe: logger.error(f"   ❌ [{task_id}] Audio blocked: {bpe}"); st.error(f"Audio blocked {task_id}.", icon="🔇"); return None
    # Catch TypeError specifically for config issues
    except TypeError as te:
         logger.exception(f"   ❌ [{task_id}] Audio config TypeError: {te}")
         st.error(f"Audio configuration error for {task_id} (TypeError): {te}. Check library version/config structure.", icon="⚙️")
         return None
    except Exception as e: logger.exception(f"   ❌ [{task_id}] Audio failed: {e}"); st.error(f"Audio failed {task_id}: {e}", icon="🔊"); return None


def generate_story_sequence_chrono(theme: str, num_scenes: int, num_timelines: int, divergence_prompt: str = "") -> Optional[ChronoWeaveResponse]:
    """Generates branching story sequences using Gemini structured output and validates with Pydantic."""
    st.info(f"📚 Generating {num_timelines} timeline(s) x {num_scenes} scenes for: '{theme}'...")
    logger.info(f"Requesting story structure: Theme='{theme}', Timelines={num_timelines}, Scenes={num_scenes}")
    divergence_instruction = (f"Introduce clear points of divergence between timelines, after first scene if possible. Hint: '{divergence_prompt}'. State divergence reason clearly. **For timeline_id 0, use 'Initial path' or 'Baseline scenario'.**")
    prompt = f"""Act as narrative designer. Create story for theme: "{theme}". Instructions: 1. Exactly **{num_timelines}** timelines. 2. Each timeline exactly **{num_scenes}** scenes. 3. **NO humans/humanoids**. Focus: animals, fantasy creatures, animated objects, nature. 4. {divergence_instruction}. 5. Style: **'Simple, friendly kids animation, bright colors, rounded shapes'**, unless `timeline_visual_modifier` alters. 6. `audio_text`: single concise sentence (max 30 words). 7. `image_prompt`: descriptive, concise (target 15-35 words MAX). Focus on scene elements. **AVOID repeating general style**. 8. `character_description`: VERY brief (name, features). Target < 20 words. Output: ONLY valid JSON object adhering to schema. No text before/after. JSON Schema: ```json\n{json.dumps(ChronoWeaveResponse.model_json_schema(), indent=2)}\n```"""
    try:
        response = client_standard.generate_content(contents=prompt, generation_config=genai.types.GenerationConfig(response_mime_type="application/json", temperature=0.7))
        try: raw_data = json.loads(response.text)
        except json.JSONDecodeError as json_err: logger.error(f"Failed JSON decode: {json_err}\nResponse:\n{response.text}"); st.error(f"🚨 Failed parse story: {json_err}", icon="📄"); st.text_area("Problem Response:", response.text, height=150); return None
        except Exception as e: logger.error(f"Error processing text: {e}"); st.error(f"🚨 Error processing AI response: {e}", icon="📄"); return None
        try: validated_data = ChronoWeaveResponse.model_validate(raw_data); logger.info("✅ Story structure OK!"); st.success("✅ Story structure OK!"); return validated_data
        except ValidationError as val_err: logger.error(f"JSON validation failed: {val_err}\nData:\n{json.dumps(raw_data, indent=2)}"); st.error(f"🚨 Gen structure invalid: {val_err}", icon="🧬"); st.json(raw_data); return None
    except genai.types.generation_types.BlockedPromptException as bpe: logger.error(f"Story gen blocked: {bpe}"); st.error("🚨 Story prompt blocked.", icon="🚫"); return None
    except Exception as e: logger.exception("Error during story gen:"); st.error(f"🚨 Story gen error: {e}", icon="💥"); return None


def generate_image_imagen(prompt: str, aspect_ratio: str = "1:1", task_id: str = "IMG") -> Optional[Image.Image]:
    """
    Generates an image.
    <<< IMPORTANT: This function needs to be rewritten using the Vertex AI SDK
    (google-cloud-aiplatform) to correctly call Imagen models. >>>
    """
    logger.info(f"🖼️ [{task_id}] Requesting image: '{prompt[:70]}...' (Aspect: {aspect_ratio})")
    logger.error(f"   ❌ [{task_id}] Image generation skipped: Function needs update to use Vertex AI SDK for Imagen.")
    st.error(f"Image generation for {task_id} skipped: Requires Vertex AI SDK implementation.", icon="🖼️")
    # Return None because the current method is known to fail based on previous logs
    return None

# --- Streamlit UI Elements ---
st.sidebar.header("⚙️ Configuration")
if GOOGLE_API_KEY: st.sidebar.success("Google API Key Loaded", icon="✅")
else: st.sidebar.error("Google API Key Missing!", icon="🚨")
theme = st.sidebar.text_input("📖 Story Theme:", "A curious squirrel finds a mysterious, glowing acorn")
num_scenes = st.sidebar.slider("🎬 Scenes per Timeline:", min_value=2, max_value=7, value=3)
num_timelines = st.sidebar.slider("🌿 Number of Timelines:", min_value=1, max_value=4, value=2)
divergence_prompt = st.sidebar.text_input("↔️ Divergence Hint (Optional):", placeholder="e.g., What if a bird tried to steal it?")
st.sidebar.subheader("🎨 Visual & Audio Settings")
aspect_ratio = st.sidebar.selectbox("🖼️ Image Aspect Ratio:", ["1:1", "16:9", "9:16"], index=0)
audio_voice = None
generate_button = st.sidebar.button("✨ Generate ChronoWeave ✨", type="primary", disabled=(not GOOGLE_API_KEY), use_container_width=True)
st.sidebar.markdown("---"); st.sidebar.info("⏳ Generation can take minutes."); st.sidebar.markdown(f"<small>Txt:{TEXT_MODEL_ID}, Img:{IMAGE_MODEL_ID}, Aud:{AUDIO_MODEL_ID}</small>", unsafe_allow_html=True)

# --- Main Logic ---
if generate_button:
    if not theme: st.error("Please enter a story theme.", icon="👈")
    else:
        run_id = str(uuid.uuid4()).split('-')[0]; temp_dir = os.path.join(TEMP_DIR_BASE, f"run_{run_id}")
        try: os.makedirs(temp_dir, exist_ok=True); logger.info(f"Created temp dir: {temp_dir}")
        except OSError as e: st.error(f"🚨 Failed create temp dir {temp_dir}: {e}", icon="📂"); st.stop()
        final_video_paths, generation_errors = {}, {}

        chrono_response: Optional[ChronoWeaveResponse] = None
        with st.spinner("Generating narrative structure... 🤔"): chrono_response = generate_story_sequence_chrono(theme, num_scenes, num_timelines, divergence_prompt)

        if chrono_response:
            overall_start_time = time.time(); all_timelines_successful = True
            with st.status("Generating assets and composing videos...", expanded=True) as status:
                for timeline_index, timeline in enumerate(chrono_response.timelines):
                    timeline_id, divergence, segments = timeline.timeline_id, timeline.divergence_reason, timeline.segments
                    timeline_label = f"Timeline {timeline_id}"; st.subheader(f"Processing {timeline_label}: {divergence}")
                    logger.info(f"--- Processing {timeline_label} (Idx: {timeline_index}) ---"); generation_errors[timeline_id] = []
                    temp_image_files, temp_audio_files, video_clips = {}, {}, []
                    timeline_start_time = time.time(); scene_success_count = 0

                    for scene_index, segment in enumerate(segments):
                        scene_id = segment.scene_id; task_id = f"T{timeline_id}_S{scene_id}"
                        status.update(label=f"Processing {timeline_label}, Scene {scene_id + 1}/{len(segments)}...")
                        st.markdown(f"--- **Scene {scene_id + 1} ({task_id})** ---")
                        logger.info(f"Processing {timeline_label}, Scene {scene_id + 1}/{len(segments)}...")
                        scene_has_error = False
                        st.write(f"   *Img Prompt:* {segment.image_prompt}" + (f" *(Mod: {segment.timeline_visual_modifier})*" if segment.timeline_visual_modifier else "")); st.write(f"   *Audio Text:* {segment.audio_text}")

                        # --- 2a. Image Generation ---
                        generated_image: Optional[Image.Image] = None
                        with st.spinner(f"[{task_id}] Generating image... 🎨"):
                            combined_prompt = segment.image_prompt
                            if segment.character_description: combined_prompt += f" Featuring: {segment.character_description}"
                            if segment.timeline_visual_modifier: combined_prompt += f" Style hint: {segment.timeline_visual_modifier}."
                            generated_image = generate_image_imagen(combined_prompt, aspect_ratio, task_id) # <<< Needs Vertex AI SDK update
                        if generated_image:
                            image_path = os.path.join(temp_dir, f"{task_id}_image.png")
                            try: generated_image.save(image_path); temp_image_files[scene_id] = image_path; st.image(generated_image, width=180, caption=f"Scene {scene_id+1}")
                            except Exception as e: logger.error(f"   ❌ [{task_id}] Img save error: {e}"); st.error(f"Save image {task_id} failed.", icon="💾"); scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Img save fail.")
                        else: scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Img gen fail."); continue

                        # --- 2b. Audio Generation ---
                        generated_audio_path: Optional[str] = None
                        if not scene_has_error: # Should not be reached currently due to image fail
                            with st.spinner(f"[{task_id}] Generating audio... 🔊"):
                                audio_path_temp = os.path.join(temp_dir, f"{task_id}_audio.wav")
                                try: generated_audio_path = asyncio.run(generate_audio_live_async(segment.audio_text, audio_path_temp, audio_voice))
                                except RuntimeError as e: logger.error(f"   ❌ [{task_id}] Asyncio error: {e}"); st.error(f"Asyncio audio error {task_id}: {e}", icon="⚡"); scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Audio async err.")
                                except Exception as e: logger.exception(f"   ❌ [{task_id}] Audio error: {e}"); st.error(f"Audio error {task_id}: {e}", icon="💥"); scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Audio gen err.")
                            if generated_audio_path:
                                temp_audio_files[scene_id] = generated_audio_path; try: open(generated_audio_path,'rb') as ap: st.audio(ap.read(), format='audio/wav')
                                except Exception as e: logger.warning(f"   ⚠️ [{task_id}] Audio preview error: {e}")
                            else: scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Audio gen fail."); continue

                        # --- 2c. Create Video Clip ---
                        if not scene_has_error and scene_id in temp_image_files and scene_id in temp_audio_files: # Should not be reached currently
                            st.write(f"   🎬 Creating clip S{scene_id+1}..."); img_path, aud_path = temp_image_files[scene_id], temp_audio_files[scene_id]
                            audio_clip_instance, image_clip_instance, composite_clip = None, None, None
                            try:
                                if not os.path.exists(img_path): raise FileNotFoundError(f"Img missing: {img_path}")
                                if not os.path.exists(aud_path): raise FileNotFoundError(f"Aud missing: {aud_path}")
                                audio_clip_instance = AudioFileClip(aud_path); np_image = np.array(Image.open(img_path))
                                image_clip_instance = ImageClip(np_image).set_duration(audio_clip_instance.duration)
                                composite_clip = image_clip_instance.set_audio(audio_clip_instance); video_clips.append(composite_clip)
                                logger.info(f"      ✅ [{task_id}] Clip created (Dur: {audio_clip_instance.duration:.2f}s)."); st.write(f"      ✅ Clip created (Dur: {audio_clip_instance.duration:.2f}s)."); scene_success_count += 1
                            except Exception as e: logger.exception(f"      ❌ [{task_id}] Failed clip creation: {e}"); st.error(f"Failed clip {task_id}: {e}", icon="🎬"); scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Clip fail.")
                            finally:
                                if audio_clip_instance: audio_clip_instance.close();
                                if image_clip_instance: image_clip_instance.close()

                    # --- 2d. Assemble Timeline Video ---
                    timeline_duration = time.time() - timeline_start_time
                    if video_clips and scene_success_count == len(segments):
                         status.update(label=f"Composing video {timeline_label}..."); st.write(f"🎞️ Assembling video {timeline_label}..."); logger.info(f"🎞️ Assembling video {timeline_label}...")
                         output_filename = os.path.join(temp_dir, f"timeline_{timeline_id}_final.mp4"); final_timeline_video = None
                         try: final_timeline_video = concatenate_videoclips(video_clips, method="compose"); final_timeline_video.write_videofile(output_filename, fps=VIDEO_FPS, codec=VIDEO_CODEC, audio_codec=AUDIO_CODEC, logger=None); final_video_paths[timeline_id] = output_filename; logger.info(f"   ✅ [{timeline_label}] Video saved: {os.path.basename(output_filename)}"); st.success(f"✅ Video {timeline_label} completed in {timeline_duration:.2f}s.")
                         except Exception as e: logger.exception(f"   ❌ [{timeline_label}] Video assembly failed: {e}"); st.error(f"Assemble video {timeline_label} failed: {e}", icon="📼"); all_timelines_successful = False; generation_errors[timeline_id].append(f"T{timeline_id}: Assembly fail.")
                         finally:
                             logger.debug(f"[{timeline_label}] Closing {len(video_clips)} clips...");
                             for i, clip in enumerate(video_clips): try: clip.close() except Exception as e_close: logger.warning(f"   ⚠️ [{timeline_label}] Clip close err {i}: {e_close}")
                             if final_timeline_video: try: final_timeline_video.close() except Exception as e_close_final: logger.warning(f"   ⚠️ [{timeline_label}] Final vid close err: {e_close_final}")
                    elif not video_clips: logger.warning(f"[{timeline_label}] No clips. Skip assembly."); st.warning(f"No scenes for {timeline_label}. No video.", icon="🚫"); all_timelines_successful = False
                    else: error_count = len(generation_errors[timeline_id]); logger.warning(f"[{timeline_label}] {error_count} scene err(s). Skip assembly."); st.warning(f"{timeline_label}: {error_count} err(s). Video not assembled.", icon="⚠️"); all_timelines_successful = False
                    if generation_errors[timeline_id]: logger.error(f"Errors {timeline_label}: {generation_errors[timeline_id]}")

                # --- End of Timelines Loop ---
                overall_duration = time.time() - overall_start_time
                if all_timelines_successful and final_video_paths: status_msg = f"Complete! ({len(final_video_paths)} videos in {overall_duration:.2f}s)"; status.update(label=status_msg, state="complete", expanded=False); logger.info(status_msg)
                elif final_video_paths: status_msg = f"Partially Complete ({len(final_video_paths)} videos, errors). {overall_duration:.2f}s"; status.update(label=status_msg, state="warning", expanded=True); logger.warning(status_msg)
                else: status_msg = f"Failed. No videos. {overall_duration:.2f}s"; status.update(label=status_msg, state="error", expanded=True); logger.error(status_msg)

            # --- 3. Display Results ---
            st.header("🎬 Generated Timelines")
            if final_video_paths:
                sorted_timeline_ids = sorted(final_video_paths.keys()); num_cols = min(len(sorted_timeline_ids), 3); cols = st.columns(num_cols)
                for idx, timeline_id in enumerate(sorted_timeline_ids):
                    col = cols[idx % num_cols]; video_path = final_video_paths[timeline_id]
                    timeline_data = next((t for t in chrono_response.timelines if t.timeline_id == timeline_id), None)
                    reason = timeline_data.divergence_reason if timeline_data else "Unknown"
                    with col:
                        st.subheader(f"Timeline {timeline_id}"); st.caption(f"Divergence: {reason}")
                        try:
                            with open(video_path, 'rb') as vf: video_bytes = vf.read()
                            st.video(video_bytes); logger.info(f"Displaying T{timeline_id}")
                            st.download_button(f"Download T{timeline_id}", video_bytes, f"timeline_{timeline_id}.mp4", "video/mp4", key=f"dl_{timeline_id}")
                            if generation_errors.get(timeline_id):
                                scene_errors = [err for err in generation_errors[timeline_id] if not err.startswith(f"T{timeline_id}:")]
                                if scene_errors:
                                     with st.expander(f"⚠️ View {len(scene_errors)} Scene Issues"):
                                         for err in scene_errors: st.warning(f"- {err}")
                        except FileNotFoundError: logger.error(f"Video missing: {video_path}"); st.error(f"Error: Video missing T{timeline_id}.", icon="🚨")
                        except Exception as e: logger.exception(f"Display error {video_path}: {e}"); st.error(f"Display error T{timeline_id}: {e}", icon="🚨")
            else: # No videos generated
                st.warning("No final videos were successfully generated.")
                st.subheader("Summary of Generation Issues")
                has_errors = any(generation_errors.values())
                if has_errors:
                    with st.expander("View All Errors", expanded=True):
                        for tid, errors in generation_errors.items():
                            if errors:
                                st.error(f"**Timeline {tid}:**")
                                for msg in errors: st.error(f"  - {msg}") # Use standard loop
                else: st.info("No generation errors recorded.")

            # --- 4. Cleanup ---
            st.info(f"Attempting cleanup: {temp_dir}")
            try: shutil.rmtree(temp_dir); logger.info(f"✅ Temp dir removed: {temp_dir}"); st.success("✅ Temp files cleaned.")
            except Exception as e: logger.error(f"⚠️ Failed remove temp dir {temp_dir}: {e}"); st.warning(f"Could not remove temp files: {temp_dir}.", icon="⚠️")

        elif not chrono_response: logger.error("Story gen/validation failed.")
        else: st.error("Unexpected issue post-gen.", icon="🛑"); logger.error("Chrono_response truthy but invalid.")

else: st.info("Configure settings and click '✨ Generate ChronoWeave ✨' to start.")