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
# Updated imports for Pydantic v2 syntax
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 of Yousif Ahmed. Copyright 2025 Google LLC.*
""")

# --- Constants ---
# Text/JSON Model
TEXT_MODEL_ID = "models/gemini-1.5-flash" # Or "gemini-1.5-pro" for potentially higher quality/cost
# Audio Model Config
AUDIO_API_VERSION = 'v1alpha' # May not be strictly needed for endpoint if library handles it
AUDIO_MODEL_ID = f"models/gemini-1.5-flash" # Model used for audio tasks
AUDIO_SAMPLING_RATE = 24000 # Standard for TTS models like Google's
# Image Model Config
IMAGE_MODEL_ID = "imagen-3" # Or specific version like "imagen-3.0-generate-002"
DEFAULT_ASPECT_RATIO = "1:1"
# Video Config
VIDEO_FPS = 24
VIDEO_CODEC = "libx264" # Widely compatible H.264
AUDIO_CODEC = "aac" # Common audio codec for MP4
# File Management
TEMP_DIR_BASE = ".chrono_temp" # Base name for temporary directories

# --- 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 via Streamlit secrets or environment variable.",
            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 standard GenerativeModel for {TEXT_MODEL_ID}.")

    live_model = genai.GenerativeModel(AUDIO_MODEL_ID)
    logger.info(f"Initialized GenerativeModel handle for audio ({AUDIO_MODEL_ID}).")

except AttributeError as ae:
     logger.exception("AttributeError during Google AI Client Initialization.")
     st.error(f"🚨 Initialization Error: {ae}. Ensure 'google-generativeai' is up-to-date.", icon="🚨")
     st.stop()
except Exception as e:
    logger.exception("Failed to initialize Google AI Clients.")
    st.error(f"🚨 Failed to initialize Google AI Clients: {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) # Keep increased limits
    audio_text: str = Field(..., min_length=5, max_length=150)
    character_description: str = Field(..., max_length=250) # Keep increased limits
    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(word in v.lower() for word in ["person", "people", "human", "man", "woman", "boy", "girl", "child"]):
            logger.warning(f"Image prompt '{v[:50]}...' may contain human descriptions.")
        return v

class Timeline(BaseModel):
    timeline_id: int = Field(..., ge=0)
    # Keep min_length=5 for divergence_reason, rely on improved prompt
    divergence_reason: str = Field(..., min_length=5, description="Clear reason why this timeline branched off.")
    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_scenes = self.total_scenes_per_timeline
        for i, timeline in enumerate(self.timelines):
            if len(timeline.segments) != expected_scenes:
                raise ValueError(f"Timeline {i} (ID: {timeline.timeline_id}) has {len(timeline.segments)} segments, but expected {expected_scenes}.")
        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 opening/configuring 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 for: '{api_text[:60]}...'")

    try:
        config = {"response_modalities": ["AUDIO"], "audio_config": {"audio_encoding": "LINEAR16", "sample_rate_hertz": AUDIO_SAMPLING_RATE}}
        directive_prompt = f"Narrate the following sentence directly and engagingly. Do not add any introductory or concluding remarks. Speak only the sentence itself:\n\n\"{api_text}\""

        async with live_model.connect(config=config) as session:
            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}] Error during audio stream: {response.error}")
                     st.error(f"Audio stream error for scene {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 generated for scene {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 generation blocked: {bpe}")
         st.error(f"Audio generation blocked for scene {task_id}.", icon="🔇")
         return None
    except Exception as e:
        logger.exception(f"   ❌ [{task_id}] Audio generation failed unexpectedly: {e}")
        st.error(f"Audio generation failed for scene {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: '{theme}'...")
    logger.info(f"Requesting story structure: Theme='{theme}', Timelines={num_timelines}, Scenes={num_scenes}")

    # Updated divergence instruction to guide the first timeline's reason
    divergence_instruction = (
        f"Introduce clear points of divergence between timelines, starting potentially after the first scene. "
        f"If provided, use this hint for divergence: '{divergence_prompt}'. "
        f"Clearly state the divergence reason for each timeline. **For the first timeline (timeline_id 0), use a descriptive reason like 'Initial path' or 'Baseline scenario' that is at least 5 characters long.**"
    )

    prompt = f"""
    Act as an expert narrative designer specializing in short, visual, branching stories for children.
    Create a story based on the core theme: "{theme}".

    **Instructions:**
    1.  Generate exactly **{num_timelines}** distinct timelines.
    2.  Each timeline must contain exactly **{num_scenes}** sequential scenes.
    3.  **Crucially, DO NOT include any humans, people, or humanoid figures** in the descriptions or actions. Focus strictly on animals, fantasy creatures, animated objects, or natural elements.
    4.  {divergence_instruction}
    5.  Maintain a consistent visual style: **'Simple, friendly kids animation style with bright colors and rounded shapes'**, unless a `timeline_visual_modifier` subtly alters it.
    6.  `audio_text` should be a single, concise sentence (max 30 words).
    7.  `image_prompt` should be descriptive **and concise (target 15-35 words MAXIMUM)**, focusing only on the non-human character(s), setting, action, and essential visual style elements for *this specific scene*. **Do NOT repeat the general style description** unless essential.
    8.  `character_description` should **very briefly** describe recurring non-human characters mentioned *in the scene's image prompt* (name, key features). **Keep descriptions extremely concise (target under 20 words total per scene).**

    **Output Format:**
    Respond ONLY with a valid JSON object adhering strictly to the provided schema. Do not include any text before or after the JSON object.

    **JSON Schema:**
    ```json
    {json.dumps(ChronoWeaveResponse.model_json_schema(), indent=2)}
    ```
    """

    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 to decode JSON: {json_err}")
            logger.error(f"Response Text:\n{response.text}")
            st.error(f"🚨 Failed to parse story structure: {json_err}", icon="📄")
            st.text_area("Problematic AI Response:", response.text, height=200)
            return None
        except Exception as e:
             logger.error(f"Error processing response 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 generated and validated successfully!")
            st.success("✅ Story structure generated and validated!")
            return validated_data
        except ValidationError as val_err:
            logger.error(f"JSON validation failed: {val_err}")
            logger.error(f"Received Data:\n{json.dumps(raw_data, indent=2)}")
            st.error(f"🚨 Generated story structure invalid: {val_err}", icon="🧬")
            st.json(raw_data)
            return None

    except genai.types.generation_types.BlockedPromptException as bpe:
         logger.error(f"Story generation prompt blocked: {bpe}")
         st.error("🚨 Story generation prompt blocked (safety filters).", icon="🚫")
         return None
    except Exception as e:
        logger.exception("Error during story sequence generation:")
        st.error(f"🚨 Unexpected error during story generation: {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 using Imagen via the standard client."""
    logger.info(f"🖼️ [{task_id}] Requesting image: '{prompt[:70]}...' (Aspect: {aspect_ratio})")

    full_prompt = (
        f"Generate an image in a child-friendly, simple animation style with bright colors and rounded shapes. "
        f"Ensure absolutely NO humans or human-like figures. Focus on animals or objects. "
        f"Aspect ratio {aspect_ratio}. Scene: {prompt}"
    )

    try:
        response = client_standard.generate_content(
            full_prompt, generation_config=genai.types.GenerationConfig(candidate_count=1)
        )

        image_bytes, safety_ratings, block_reason, finish_reason = None, [], None, None

        if hasattr(response, 'candidates') and response.candidates:
            candidate = response.candidates[0]
            if hasattr(candidate, 'finish_reason'): finish_reason = getattr(candidate.finish_reason, 'name', str(candidate.finish_reason))
            if hasattr(candidate, 'content') and candidate.content and hasattr(candidate.content, 'parts') and candidate.content.parts:
                part = candidate.content.parts[0]
                if hasattr(part, 'inline_data') and part.inline_data and hasattr(part.inline_data, 'data'): image_bytes = part.inline_data.data
            if hasattr(candidate, 'safety_ratings'): safety_ratings = candidate.safety_ratings

        if hasattr(response, 'prompt_feedback') and response.prompt_feedback:
            if hasattr(response.prompt_feedback, 'block_reason') and response.prompt_feedback.block_reason.name != 'BLOCK_REASON_UNSPECIFIED': block_reason = response.prompt_feedback.block_reason.name
            if hasattr(response.prompt_feedback, 'safety_ratings'): safety_ratings.extend(response.prompt_feedback.safety_ratings)

        if image_bytes:
            try:
                image = Image.open(BytesIO(image_bytes))
                logger.info(f"   ✅ [{task_id}] Image generated.")
                filtered_ratings = [f"{r.category.name}: {r.probability.name}" for r in safety_ratings if hasattr(r,'probability') and r.probability.name != 'NEGLIGIBLE']
                if filtered_ratings:
                    logger.warning(f"   ⚠️ [{task_id}] Image flagged: {', '.join(filtered_ratings)}.")
                    st.warning(f"Image {task_id} flagged: {', '.join(filtered_ratings)}", icon="⚠️")
                return image
            except Exception as img_err:
                logger.error(f"   ❌ [{task_id}] Failed to decode image data: {img_err}")
                st.warning(f"Failed decode image data {task_id}.", icon="🖼️")
                return None
        else:
            fail_reason = "Unknown reason."
            if block_reason: fail_reason = f"Blocked (Reason: {block_reason})."
            elif finish_reason and finish_reason not in ['STOP', 'FINISH_REASON_UNSPECIFIED']: fail_reason = f"Finished early (Reason: {finish_reason})."
            else:
                filtered_ratings = [f"{r.category.name}: {r.probability.name}" for r in safety_ratings if hasattr(r,'probability') and r.probability.name != 'NEGLIGIBLE']
                if filtered_ratings: fail_reason = f"Safety filters triggered: {', '.join(filtered_ratings)}."

            # Add the full response logging here for persistent unknown failures
            if fail_reason == "Unknown reason.":
                 logger.warning(f"   ⚠️ [{task_id}] Full API response object: {response}")

            logger.warning(f"   ⚠️ [{task_id}] No image data. Reason: {fail_reason} Prompt: '{prompt[:70]}...'")
            st.warning(f"No image data for {task_id}. Reason: {fail_reason}", icon="🖼️")
            return None

    except genai.types.generation_types.BlockedPromptException as bpe:
         logger.error(f"   ❌ [{task_id}] Image generation blocked (exception): {bpe}")
         st.error(f"Image generation blocked for {task_id} (exception).", icon="🚫")
         return None
    except Exception as e:
        logger.exception(f"   ❌ [{task_id}] Image generation failed unexpectedly: {e}")
        st.error(f"Image generation failed for {task_id}: {e}", icon="🖼️")
        return None

# --- Streamlit UI Elements ---
# (Identical to previous version - No changes needed here)
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 several minutes.", icon="⏳")
st.sidebar.markdown(f"<small>Models: Text={TEXT_MODEL_ID}, Image={IMAGE_MODEL_ID}, Audio={AUDIO_MODEL_ID}</small>", unsafe_allow_html=True)

# --- Main Logic ---
if generate_button:
    if not theme:
        st.error("Please enter a story theme in the sidebar.", 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 temporary directory: {temp_dir}")
        except OSError as e:
            st.error(f"🚨 Failed to create temporary directory {temp_dir}: {e}", icon="📂")
            st.stop()

        final_video_paths = {}
        generation_errors = {}

        # --- 1. Generate Narrative Structure ---
        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:
            # --- 2. Process Each Timeline ---
            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 = timeline.timeline_id
                    divergence = timeline.divergence_reason
                    segments = timeline.segments
                    timeline_label = f"Timeline {timeline_id}"
                    st.subheader(f"Processing {timeline_label}: {divergence}")
                    logger.info(f"--- Processing {timeline_label} (Index: {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_message = f"Processing {timeline_label}, Scene {scene_id + 1}/{len(segments)}..."
                        status.update(label=status_message)
                        st.markdown(f"--- **Scene {scene_id + 1} ({task_id})** ---")
                        logger.info(status_message)
                        scene_has_error = False

                        st.write(f"   *Image 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)

                        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} Image")
                            except Exception as e:
                                logger.error(f"   ❌ [{task_id}] Failed to save image: {e}")
                                st.error(f"Failed save image {task_id}.", 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 # Skip rest of scene processing

                        # --- 2b. Audio Generation ---
                        generated_audio_path: Optional[str] = None
                        if not scene_has_error: # Only proceed if image succeeded
                            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}] Unexpected audio error: {e}")
                                    st.error(f"Unexpected 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:
                                    with 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: # Audio generation failed
                                scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Audio gen fail.")
                                # Clean up corresponding image file
                                if scene_id in temp_image_files and os.path.exists(temp_image_files[scene_id]):
                                    try: os.remove(temp_image_files[scene_id]); logger.info(f"   🗑️ [{task_id}] Removed image due to audio fail."); del temp_image_files[scene_id]
                                    except OSError as e: logger.warning(f"   ⚠️ [{task_id}] Failed remove image after audio fail: {e}")
                                continue # Skip video clip creation

                        # --- 2c. Create Video Clip ---
                        if not scene_has_error and scene_id in temp_image_files and scene_id in temp_audio_files:
                            st.write(f"   🎬 Creating video 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 create clip {task_id}: {e}", icon="🎬")
                                scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Clip creation fail.")
                                # Cleanup resources from failed clip attempt
                                if audio_clip_instance: audio_clip_instance.close()
                                if image_clip_instance: image_clip_instance.close()
                                try:
                                     if os.path.exists(img_path): os.remove(img_path)
                                     if os.path.exists(aud_path): os.remove(aud_path)
                                except OSError as e_rem: logger.warning(f"   ⚠️ [{task_id}] Failed remove files after clip error: {e_rem}")

                    # --- 2d. Assemble Timeline Video ---
                    timeline_duration = time.time() - timeline_start_time
                    if video_clips and scene_success_count == len(segments):
                        # ... (Video assembly logic - same as before) ...
                        status.update(label=f"Composing final video for {timeline_label}...")
                        st.write(f"🎞️ Assembling final video for {timeline_label}...")
                        logger.info(f"🎞️ Assembling final video for {timeline_label} ({len(video_clips)} clips)...")
                        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}] Final video saved: {os.path.basename(output_filename)}")
                            st.success(f"✅ Video for {timeline_label} completed in {timeline_duration:.2f}s.")
                        except Exception as e:
                            logger.exception(f"   ❌ [{timeline_label}] Failed to write final video: {e}")
                            st.error(f"Failed assemble video {timeline_label}: {e}", icon="📼")
                            all_timelines_successful = False; generation_errors[timeline_id].append(f"Timeline {timeline_id}: Video assembly failed.")
                        finally:
                            logger.debug(f"[{timeline_label}] Closing clips...")
                            for i, clip in enumerate(video_clips):
                                try:
                                    if clip:
                                      if clip.audio: clip.audio.close()
                                      clip.close()
                                except Exception as e_close: logger.warning(f"   ⚠️ [{timeline_label}] Error closing source clip {i}: {e_close}")
                            if final_timeline_video:
                                try:
                                     if final_timeline_video.audio: final_timeline_video.audio.close()
                                     final_timeline_video.close()
                                except Exception as e_close_final: logger.warning(f"   ⚠️ [{timeline_label}] Error closing final video object: {e_close_final}")
                    elif not video_clips:
                         logger.warning(f"[{timeline_label}] No clips generated. Skipping assembly.")
                         st.warning(f"No scenes processed for {timeline_label}. Cannot create video.", icon="🚫")
                         all_timelines_successful = False
                    else: # Some scenes failed
                        error_count = len(segments) - scene_success_count
                        logger.warning(f"[{timeline_label}] Errors in {error_count} scene(s). Skipping assembly.")
                        st.warning(f"{timeline_label} had errors in {error_count} scene(s). Video not assembled.", icon="⚠️")
                        all_timelines_successful = False
                    if generation_errors[timeline_id]: logger.error(f"Error summary {timeline_label}: {generation_errors[timeline_id]}")

                # --- End of Timelines Loop ---
                overall_duration = time.time() - overall_start_time
                # ... (Final status update logic - same as before) ...
                if all_timelines_successful and final_video_paths: status_msg = f"ChronoWeave 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"ChronoWeave Partially Complete ({len(final_video_paths)} videos, errors). Time: {overall_duration:.2f}s"; status.update(label=status_msg, state="warning", expanded=True); logger.warning(status_msg)
                else: status_msg = f"ChronoWeave Failed. No videos. Time: {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:
                # ... (Display logic - same as before) ...
                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 video 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):
                                with st.expander(f"⚠️ View {len(generation_errors[timeline_id])} Issues"):
                                     for err in generation_errors[timeline_id]: st.warning(f"- {err}")
                        except FileNotFoundError: logger.error(f"Video file missing: {video_path}"); st.error(f"Error: Video file missing T{timeline_id}.", icon="🚨")
                        except Exception as e: logger.exception(f"Display error {video_path}: {e}"); st.error(f"Error display T{timeline_id}: {e}", icon="🚨")
            else:
                st.warning("No final videos were successfully generated.")
                # ... (Error summary display - same as before) ...
                all_errors = [msg for err_list in generation_errors.values() for msg in err_list]
                if all_errors:
                    st.subheader("Summary of Generation Issues")
                    with st.expander("View All Errors", expanded=True):
                        for tid, errors in generation_errors.items():
                            if errors: st.error(f"T{tid}:"); [st.error(f"  - {msg}") for msg in errors]

            # --- 4. Cleanup ---
            st.info(f"Attempting cleanup: {temp_dir}")
            try:
                shutil.rmtree(temp_dir)
                logger.info(f"✅ Temp dir removed: {temp_dir}")
                st.success("✅ Temporary files cleaned up.")
            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 generation/validation failed.")
        else: st.error("Unexpected issue post-generation.", icon="🛑"); logger.error("Chrono_response truthy but invalid state.")

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