Update app.py

app.py

@@ -1,345 +1,511 @@
-# 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
+# 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.
+# 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 uuid
 import asyncio
-import uuid # For unique identifiers
-import shutil # For directory operations
-import logging # For better logging
+import logging
+import shutil
+import contextlib
+import wave
+from io import BytesIO
+from typing import List, Optional, Tuple, Dict, Any
 
-# Image handling
+import streamlit as st
+import numpy as np
 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
+# Google Generative AI library and async patch
+import google.generativeai as genai
 import nest_asyncio
-nest_asyncio.apply() # Apply patch for asyncio in environments like Streamlit/Jupyter
+nest_asyncio.apply()  # Ensure asyncio works correctly in Streamlit/Jupyter
 
 # --- Logging Setup ---
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+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
+# --- Constants & Configurations ---
+TEXT_MODEL_ID = "models/gemini-1.5-flash"   # Alternatively "gemini-1.5-pro"
+AUDIO_MODEL_ID = "models/gemini-1.5-flash"    # Synchronous generation for audio now
 AUDIO_SAMPLING_RATE = 24000
-# Image Model Config
-IMAGE_MODEL_ID = "imagen-3" # <<< NOTE: Likely needs Vertex AI SDK access
+IMAGE_MODEL_ID = "imagen-3"  # NOTE: Requires Vertex AI SDK integration in the future
 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) ---
+
+# --- Pydantic Schemas ---
 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')
+
+    @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.")
+        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 include human-related descriptions.")
         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':
+
+    @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)}.")
+        for i, timeline in enumerate(self.timelines):
+            if len(timeline.segments) != expected:
+                raise ValueError(f"Timeline {i} (ID: {timeline.timeline_id}): Expected {expected} segments, got {len(timeline.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."""
+    """
+    Safely writes a WAV file using a context manager.
+    """
     wf = None
     try:
-        wf = wave.open(filename, "wb"); wf.setnchannels(channels); wf.setsampwidth(sample_width); wf.setframerate(rate)
+        wf = wave.open(filename, "wb")
+        wf.setnchannels(channels)
+        wf.setsampwidth(sample_width)  # 16-bit audio (2 bytes)
+        wf.setframerate(rate)
         yield wf
-    except Exception as e: logger.error(f"Error wave file {filename}: {e}"); raise
+    except Exception as exc:
+        logger.error(f"Error writing wave file {filename}: {exc}")
+        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
+        if wf:
+            try:
+                wf.close()
+            except Exception as e_close:
+                logger.error(f"Error closing wave file {filename}: {e_close}")
 
 
-def generate_image_imagen(prompt: str, aspect_ratio: str = "1:1", task_id: str = "IMG") -> Optional[Image.Image]:
+# --- ChronoWeave Generator Class ---
+class ChronoWeaveGenerator:
     """
-    Generates an image.
-    <<< IMPORTANT: This function needs to be rewritten using the Vertex AI SDK
-    (google-cloud-aiplatform) to correctly call Imagen models. >>>
+    Encapsulates the logic for generating branching narratives,
+    processing audio, images, and assembling video outputs.
     """
-    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="👈")
+
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        genai.configure(api_key=self.api_key)
+
+        try:
+            self.client_text = genai.GenerativeModel(TEXT_MODEL_ID)
+            logger.info(f"Initialized text model: {TEXT_MODEL_ID}")
+            self.client_audio = genai.GenerativeModel(AUDIO_MODEL_ID)
+            logger.info(f"Initialized audio model: {AUDIO_MODEL_ID}")
+            self.client_image = genai.GenerativeModel(IMAGE_MODEL_ID)
+            logger.info(f"Initialized image model: {IMAGE_MODEL_ID} (Placeholder: Update to Vertex AI SDK)")
+        except Exception as exc:
+            logger.exception("Failed to initialize Google Clients/Models.")
+            raise exc
+
+    def generate_story_structure(
+        self, theme: str, num_scenes: int, num_timelines: int, divergence_prompt: str = ""
+    ) -> Optional[ChronoWeaveResponse]:
+        """
+        Generates a story structure as JSON using the text model and validates it via Pydantic.
+        """
+        st.info(f"Generating {num_timelines} timeline(s) with {num_scenes} scene(s) for theme: '{theme}'")
+        logger.info(f"Story generation request: Theme='{theme}', Timelines={num_timelines}, Scenes={num_scenes}")
+
+        divergence_instruction = (
+            f"Introduce clear divergence after the first scene. Hint: '{divergence_prompt}'. "
+            f"For timeline_id 0, use 'Initial path' or 'Baseline scenario'."
+        )
+
+        prompt = f"""Act as a narrative designer. Create a story for the theme: "{theme}".
+Instructions:
+1. Exactly **{num_timelines}** timelines.
+2. Each timeline must consist of exactly **{num_scenes}** scenes.
+3. **NO humans/humanoids**; focus on animals, fantasy creatures, animated objects, and nature.
+4. {divergence_instruction}
+5. Style: **'Simple, friendly kids animation, bright colors, rounded shapes'** unless modified by `timeline_visual_modifier`.
+6. `audio_text`: One concise sentence (max 30 words).
+7. `image_prompt`: Descriptive prompt (15–35 words) emphasizing scene elements. **Avoid repeating general style.**
+8. `character_description`: Very brief (name and features; < 20 words).
+
+Output only a valid JSON object conforming exactly to this schema:
+JSON Schema: ```json
+{json.dumps(ChronoWeaveResponse.model_json_schema(), indent=2)}
+```"""
+
+        try:
+            response = self.client_text.generate_content(
+                contents=prompt,
+                generation_config=genai.types.GenerationConfig(
+                    response_mime_type="application/json", temperature=0.7
+                ),
+            )
+            raw_data = json.loads(response.text)
+            validated_data = ChronoWeaveResponse.model_validate(raw_data)
+            st.success("Story structure validated successfully!")
+            return validated_data
+
+        except json.JSONDecodeError as json_err:
+            logger.error(f"JSON decode failed: {json_err}\nResponse: {response.text}")
+            st.error(f"🚨 JSON Parsing Error: {json_err}", icon="📄")
+            st.text_area("Response", response.text, height=150)
+        except ValidationError as val_err:
+            logger.error(f"Pydantic validation error: {val_err}\nData: {json.dumps(raw_data, indent=2)}")
+            st.error(f"🚨 Invalid story structure: {val_err}", icon="🧬")
+            st.json(raw_data)
+        except Exception as e:
+            logger.exception("Story generation error:")
+            st.error(f"🚨 Error generating story: {e}", icon="💥")
+        return None
+
+    async def generate_audio(self, text: str, output_filename: str, voice: Optional[str] = None) -> Optional[str]:
+        """
+        Asynchronously generates audio by wrapping the synchronous generate_content call.
+        The call is executed using asyncio.to_thread to avoid blocking.
+        """
+        task_id = os.path.basename(output_filename).split(".")[0]
+        logger.info(f"🎙️ [{task_id}] Generating audio for text: '{text[:60]}...'")
+
+        try:
+            # Define a synchronous function for audio generation.
+            def sync_generate_audio():
+                prompt = f"Narrate directly: \"{text}\""
+                response = self.client_audio.generate_content(
+                    contents=prompt,
+                    generation_config=genai.types.GenerationConfig(
+                        response_mime_type="application/octet-stream",
+                        temperature=0.7,
+                        audio_config={"audio_encoding": "LINEAR16", "sample_rate_hertz": AUDIO_SAMPLING_RATE}
+                    )
+                )
+                return response
+
+            # Execute the synchronous call in a separate thread.
+            response = await asyncio.to_thread(sync_generate_audio)
+
+            # Process the response. Adjust as necessary based on the API’s actual response structure.
+            if not response or not hasattr(response, "audio_chunk") or not response.audio_chunk.data:
+                logger.error(f"❌ [{task_id}] No audio data returned.")
+                st.error(f"Audio generation failed for {task_id}: No audio data.", icon="🔊")
+                return None
+
+            audio_data = response.audio_chunk.data
+            with wave_file_writer(output_filename) as wf:
+                wf.writeframes(audio_data)
+            logger.info(f"✅ [{task_id}] Audio saved: {os.path.basename(output_filename)} ({len(audio_data)} bytes)")
+            return output_filename
+
+        except Exception as e:
+            logger.exception(f"❌ [{task_id}] Audio generation error: {e}")
+            st.error(f"Audio generation failed for {task_id}: {e}", icon="🔊")
+            return None
+
+    async def generate_image_async(self, prompt: str, aspect_ratio: str, task_id: str) -> Optional[Image.Image]:
+        """
+        Placeholder for image generation.
+        Currently logs an error and returns None. Update this function once integrating Vertex AI SDK.
+        """
+        logger.info(f"🖼️ [{task_id}] Requesting image for prompt: '{prompt[:70]}...' (Aspect Ratio: {aspect_ratio})")
+        logger.error(f"❌ [{task_id}] Image generation not implemented. Update required for Vertex AI.")
+        st.error(f"Image generation for {task_id} skipped: Requires Vertex AI SDK implementation.", icon="🖼️")
+        return None
+
+    async def process_scene(
+        self,
+        timeline_id: int,
+        segment: StorySegment,
+        temp_dir: str,
+        aspect_ratio: str,
+        audio_voice: Optional[str] = None,
+    ) -> Tuple[Optional[str], Optional[str], Optional[Any], List[str]]:
+        """
+        Processes a single scene: concurrently generates image and audio,
+        and then creates a video clip if both outputs are available.
+        Returns a tuple of (image_path, audio_path, video_clip, [error messages]).
+        """
+        errors: List[str] = []
+        task_id = f"T{timeline_id}_S{segment.scene_id}"
+        image_path = os.path.join(temp_dir, f"{task_id}_image.png")
+        audio_path = os.path.join(temp_dir, f"{task_id}_audio.wav")
+        video_clip = None
+
+        # Launch image and audio generation concurrently.
+        image_future = asyncio.create_task(
+            self.generate_image_async(
+                prompt=f"{segment.image_prompt} Featuring: {segment.character_description} " +
+                       (f"Style hint: {segment.timeline_visual_modifier}" if segment.timeline_visual_modifier else ""),
+                aspect_ratio=aspect_ratio,
+                task_id=task_id,
+            )
+        )
+        audio_future = asyncio.create_task(self.generate_audio(segment.audio_text, audio_path, audio_voice))
+
+        image_result, audio_result = await asyncio.gather(image_future, audio_future)
+
+        if image_result:
+            try:
+                image_result.save(image_path)
+                st.image(image_result, width=180, caption=f"Scene {segment.scene_id + 1}")
+            except Exception as e:
+                logger.error(f"❌ [{task_id}] Error saving image: {e}")
+                errors.append(f"Scene {segment.scene_id + 1}: Image save error.")
+        else:
+            errors.append(f"Scene {segment.scene_id + 1}: Image generation failed.")
+
+        if audio_result:
+            try:
+                with open(audio_result, "rb") as ap:
+                    st.audio(ap.read(), format="audio/wav")
+            except Exception as e:
+                logger.warning(f"⚠️ [{task_id}] Audio preview error: {e}")
+        else:
+            errors.append(f"Scene {segment.scene_id + 1}: Audio generation failed.")
+
+        if not errors and os.path.exists(image_path) and os.path.exists(audio_path):
+            try:
+                audio_clip = AudioFileClip(audio_path)
+                np_img = np.array(Image.open(image_path))
+                img_clip = ImageClip(np_img).set_duration(audio_clip.duration)
+                video_clip = img_clip.set_audio(audio_clip)
+                logger.info(f"✅ [{task_id}] Video clip created (Duration: {audio_clip.duration:.2f}s).")
+            except Exception as e:
+                logger.exception(f"❌ [{task_id}] Failed to create video clip: {e}")
+                errors.append(f"Scene {segment.scene_id + 1}: Video clip creation failed.")
+            finally:
+                try:
+                    if 'audio_clip' in locals():
+                        audio_clip.close()
+                    if 'img_clip' in locals():
+                        img_clip.close()
+                except Exception:
+                    pass
+
+        return (
+            image_path if os.path.exists(image_path) else None,
+            audio_path if os.path.exists(audio_path) else None,
+            video_clip,
+            errors,
+        )
+
+    async def process_timeline(
+        self,
+        timeline: Timeline,
+        temp_dir: str,
+        aspect_ratio: str,
+        audio_voice: Optional[str] = None,
+    ) -> Tuple[Optional[str], List[str]]:
+        """
+        Processes an entire timeline by concurrently processing all its scenes,
+        then assembling a final video if every scene produced a valid clip.
+        Returns a tuple of (final video path, list of error messages).
+        """
+        timeline_id = timeline.timeline_id
+        scene_tasks = [
+            self.process_scene(timeline_id, segment, temp_dir, aspect_ratio, audio_voice)
+            for segment in timeline.segments
+        ]
+        results = await asyncio.gather(*scene_tasks)
+        video_clips = []
+        timeline_errors: List[str] = []
+        for idx, (img_path, aud_path, clip, errs) in enumerate(results):
+            if errs:
+                timeline_errors.extend(errs)
+            if clip is not None:
+                video_clips.append(clip)
+
+        if video_clips and len(video_clips) == len(timeline.segments):
+            output_filename = os.path.join(temp_dir, f"timeline_{timeline_id}_final.mp4")
+            try:
+                final_video = concatenate_videoclips(video_clips, method="compose")
+                final_video.write_videofile(
+                    output_filename, fps=VIDEO_FPS, codec=VIDEO_CODEC, audio_codec=AUDIO_CODEC, logger=None
+                )
+                logger.info(f"✅ Timeline {timeline_id} video saved: {output_filename}")
+                for clip in video_clips:
+                    clip.close()
+                final_video.close()
+                return output_filename, timeline_errors
+            except Exception as e:
+                logger.exception(f"❌ Timeline {timeline_id} video assembly failed: {e}")
+                timeline_errors.append(f"Timeline {timeline_id}: Video assembly failed.")
+        else:
+            timeline_errors.append(f"Timeline {timeline_id}: Incomplete scenes; skipping video assembly.")
+        return None, timeline_errors
+
+
+# --- Streamlit UI and Main Process ---
+def main():
+    # API Key Retrieval
+    GOOGLE_API_KEY: Optional[str] = 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()
+
+    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 work by Yousif Ahmed. Copyright 2025 Google LLC.*
+    """)
+
+    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 may take several minutes.")
+    st.sidebar.markdown(f"<small>Txt: {TEXT_MODEL_ID}, Img: {IMAGE_MODEL_ID}, Aud: {AUDIO_MODEL_ID}</small>", unsafe_allow_html=True)
+
+    if generate_button:
+        if not theme:
+            st.error("Please enter a story theme.", icon="👈")
+            return
+
+        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()
+
+        # Instantiate ChronoWeaveGenerator and generate story structure.
+        generator = ChronoWeaveGenerator(GOOGLE_API_KEY)
+        chrono_response = None
+        with st.spinner("Generating narrative structure... 🤔"):
+            chrono_response = generator.generate_story_structure(theme, num_scenes, num_timelines, divergence_prompt)
+
+        if not chrono_response:
+            logger.error("Story generation or validation failed.")
+            return
+
+        overall_start_time = time.time()
+        final_video_paths: Dict[int, str] = {}
+        generation_errors: Dict[int, List[str]] = {}
+
+        async def process_all_timelines():
+            timeline_tasks = {
+                timeline.timeline_id: asyncio.create_task(
+                    generator.process_timeline(timeline, temp_dir, aspect_ratio, audio_voice)
+                )
+                for timeline in chrono_response.timelines
+            }
+            results = await asyncio.gather(*timeline_tasks.values(), return_exceptions=False)
+            return results
+
+        with st.spinner("Processing scenes and assembling videos..."):
+            timeline_results = asyncio.run(process_all_timelines())
+
+        for timeline, (video_path, errors) in zip(chrono_response.timelines, timeline_results):
+            generation_errors[timeline.timeline_id] = errors
+            if video_path:
+                final_video_paths[timeline.timeline_id] = video_path
+
+        overall_duration = time.time() - overall_start_time
+        if final_video_paths:
+            st.success(f"Complete! ({len(final_video_paths)} video(s) created in {overall_duration:.2f}s)")
+        else:
+            st.error(f"Failed. No final videos generated in {overall_duration:.2f}s")
+
+        st.header("🎬 Generated Timelines")
+        if final_video_paths:
+            sorted_ids = sorted(final_video_paths.keys())
+            num_cols = min(len(sorted_ids), 3)
+            cols = st.columns(num_cols)
+            for idx, timeline_id in enumerate(sorted_ids):
+                video_path = final_video_paths[timeline_id]
+                timeline_data = next((t for t in chrono_response.timelines if t.timeline_id == timeline_id), None)
+                divergence = timeline_data.divergence_reason if timeline_data else "Unknown"
+                with cols[idx % num_cols]:
+                    st.subheader(f"Timeline {timeline_id}")
+                    st.caption(f"Divergence: {divergence}")
+                    try:
+                        with open(video_path, "rb") as vf:
+                            video_bytes = vf.read()
+                        st.video(video_bytes)
+                        st.download_button(
+                            f"Download Timeline {timeline_id}",
+                            video_bytes,
+                            file_name=f"timeline_{timeline_id}.mp4",
+                            mime="video/mp4",
+                            key=f"dl_{timeline_id}"
+                        )
+                        if generation_errors.get(timeline_id):
+                            scene_errs = generation_errors[timeline_id]
+                            if scene_errs:
+                                with st.expander(f"⚠️ View Scene Issues ({len(scene_errs)})"):
+                                    for err in scene_errs:
+                                        st.warning(f"- {err}")
+                    except FileNotFoundError:
+                        st.error(f"Error: Video for Timeline {timeline_id} is missing.", icon="🚨")
+                    except Exception as e:
+                        st.error(f"Display error for Timeline {timeline_id}: {e}", icon="🚨")
+        else:
+            st.warning("No final videos were successfully generated.")
+            with st.expander("View All Generation Errors", expanded=True):
+                for tid, errs in generation_errors.items():
+                    if errs:
+                        st.error(f"Timeline {tid}:")
+                        for msg in errs:
+                            st.error(f"  - {msg}")
+
+        st.info(f"Cleaning up temporary files: {temp_dir}")
+        try:
+            shutil.rmtree(temp_dir)
+            st.success("✅ Temporary files cleaned up.")
+            logger.info(f"Temporary directory removed: {temp_dir}")
+        except Exception as e:
+            st.warning(f"Could not remove temporary files at: {temp_dir}", icon="⚠️")
+            logger.error(f"Failed to remove temporary directory {temp_dir}: {e}")
     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.")
+        st.info("Configure settings and click '✨ Generate ChronoWeave ✨' to start.")
+
+
+if __name__ == "__main__":
+    main()