Update app.py

app.py

@@ -44,7 +44,7 @@ st.set_page_config(page_title="ChronoWeave", layout="wide", initial_sidebar_stat
 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.*
+*Based on the work by Yousif Ahmed. Copyright 2025 Google LLC.*
 """)
 
 # --- Constants ---
@@ -54,7 +54,7 @@ TEXT_MODEL_ID = "models/gemini-1.5-flash" # Or "gemini-1.5-pro"
 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" # <<< YOUR IMAGE MODEL
+IMAGE_MODEL_ID = "imagen-3" # <<< NOTE: Likely needs Vertex AI SDK access
 DEFAULT_ASPECT_RATIO = "1:1"
 # Video Config
 VIDEO_FPS = 24
@@ -73,87 +73,68 @@ except KeyError:
     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.error("🚨 **Google API Key Not Found!** Please configure it.", icon="🚨"); st.stop()
 
 # --- Initialize Google Clients ---
-# Initialize handles for Text, Audio (using Text model), and Image models
+# Initialize handles for Text, Audio (using Text model), and potentially Image models
 try:
     genai.configure(api_key=GOOGLE_API_KEY)
     logger.info("Configured google-generativeai with API key.")
-
-    # Handle for Text/JSON Generation
     client_standard = genai.GenerativeModel(TEXT_MODEL_ID)
     logger.info(f"Initialized text/JSON model handle: {TEXT_MODEL_ID}.")
-
-    # Handle for Audio Generation (uses a text-capable model via connect)
     live_model = genai.GenerativeModel(AUDIO_MODEL_ID)
     logger.info(f"Initialized audio model handle: {AUDIO_MODEL_ID}.")
-
-    # Handle for Image Generation <<<<------ NEW/CORRECTED
-    image_model = genai.GenerativeModel(IMAGE_MODEL_ID)
-    logger.info(f"Initialized image model handle: {IMAGE_MODEL_ID}.")
+    # This handle remains, but the call in generate_image_imagen is likely incorrect for this library
+    image_model_genai = genai.GenerativeModel(IMAGE_MODEL_ID)
+    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 <---
+    # from google.cloud import aiplatform
+    # aiplatform.init(project='YOUR_PROJECT_ID', location='YOUR_REGION') # Example
+    # logger.info("Initialized Vertex AI Platform.")
 
 except AttributeError as ae:
-     logger.exception("AttributeError during Google AI Client Initialization.")
-     st.error(f"🚨 Initialization Error: {ae}. Ensure library is up-to-date.", icon="🚨")
-     st.stop()
+     logger.exception("AttributeError during Client Init."); st.error(f"🚨 Init Error: {ae}. Update library?", icon="🚨"); st.stop()
 except Exception as e:
-    # Catch potential errors if a model ID is invalid or inaccessible
-    logger.exception("Failed to initialize Google AI Clients/Models.")
-    st.error(f"🚨 Failed to initialize Google AI Clients/Models: {e}", icon="🚨")
-    st.stop()
-
+    logger.exception("Failed to initialize Google Clients/Models."); st.error(f"🚨 Failed Init: {e}", icon="🚨"); st.stop()
 
 # --- Define Pydantic Schemas (Using V2 Syntax) ---
+# (Schemas remain the same as previous version)
 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(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.")
+        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) # Relying on prompt for 1st timeline
+    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_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}: Expected {expected_scenes} segments, found {len(timeline.segments)}.")
+        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 ---
-
+# (wave_file_writer and generate_audio_live_async remain the same)
 @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
+    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}")
-
+        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."""
@@ -161,7 +142,7 @@ async def generate_audio_live_async(api_text: str, output_filename: str, voice:
     logger.info(f"🎙️ [{task_id}] Requesting audio: '{api_text[:60]}...'")
     try:
         config = {"response_modalities": ["AUDIO"], "audio_config": {"audio_encoding": "LINEAR16", "sample_rate_hertz": AUDIO_SAMPLING_RATE}}
-        directive_prompt = f"Narrate directly: \"{api_text}\"" # Shorter directive
+        directive_prompt = f"Narrate directly: \"{api_text}\""
         async with live_model.connect(config=config) as session:
             await session.send_request([directive_prompt])
             async for response in session.stream_content():
@@ -179,82 +160,73 @@ def generate_story_sequence_chrono(theme: str, num_scenes: int, num_timelines: i
     """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 the first scene if possible. "
-        f"Use hint if provided: '{divergence_prompt}'. "
-        f"State divergence reason clearly. **For timeline_id 0, use 'Initial path' or 'Baseline scenario'.**" # Explicit instruction for first timeline
-    )
-    prompt = f"""
-    Act as narrative designer. Create story based on theme: "{theme}".
-    **Instructions:**
-    1. Generate 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. Maintain consistent style: **'Simple, friendly kids animation, bright colors, rounded shapes'**, unless `timeline_visual_modifier` alters it.
-    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 description**.
-    8. `character_description`: VERY brief description of characters in scene prompt (name, features). Target < 20 words total.
-    **Output Format:** ONLY valid JSON object adhering to schema. No text before/after.
-    **JSON Schema:** ```json\n{json.dumps(ChronoWeaveResponse.model_json_schema(), indent=2)}\n```"""
+    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 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}\nData:\n{json.dumps(raw_data, indent=2)}"); st.error(f"🚨 Generated structure invalid: {val_err}", icon="🧬"); st.json(raw_data); 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 using the dedicated image model handle."""
+    """
+    Generates an image.
+    <<< IMPORTANT: This function needs to be rewritten using the Vertex AI SDK
+    (google-cloud-aiplatform) to correctly call Imagen models.
+    The current implementation using google-generativeai's generate_content
+    is likely incompatible with the 'imagen-3' model ID on the standard endpoint. >>>
+    """
     logger.info(f"🖼️ [{task_id}] Requesting image: '{prompt[:70]}...' (Aspect: {aspect_ratio})")
-    full_prompt = (f"Simple kids animation style, bright colors, rounded shapes. NO humans/humanoids. Aspect ratio {aspect_ratio}. Scene: {prompt}")
-    try:
-        # Use the dedicated image_model handle <<<<<------ CORRECTED CALL
-        response = image_model.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}] Img decode error: {img_err}"); st.warning(f"Decode image data {task_id} failed.", icon="🖼️"); return None
-        else:
-            fail_reason = "Unknown reason."
-            if block_reason: fail_reason = f"Blocked ({block_reason})."
-            elif finish_reason and finish_reason not in ['STOP', 'FINISH_REASON_UNSPECIFIED']: fail_reason = f"Finished early ({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: {', '.join(filtered_ratings)}."
-            # Log full response only if reason remains unknown
-            if fail_reason == "Unknown reason.": logger.warning(f"   ⚠️ [{task_id}] Full API response object: {response}") # Keep this debug log for now
-            logger.warning(f"   ⚠️ [{task_id}] No image data. Reason: {fail_reason} Prompt: '{prompt[:70]}...'")
-            st.warning(f"No image data {task_id}. Reason: {fail_reason}", icon="🖼️"); return None
-    except genai.types.generation_types.BlockedPromptException as bpe: logger.error(f"   ❌ [{task_id}] Image blocked (exception): {bpe}"); st.error(f"Image blocked {task_id} (exception).", icon="🚫"); return None
-    except Exception as e: logger.exception(f"   ❌ [{task_id}] Image gen failed: {e}"); st.error(f"Image gen failed {task_id}: {e}", icon="🖼️"); return None
+    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="🖼️")
+
+    # --- Placeholder for Vertex AI SDK Implementation ---
+    # Example conceptual structure (replace with actual Vertex AI SDK code):
+    # try:
+    #     from vertexai.preview.generative_models import ImageGenerationModel # Example import
+    #
+    #     # Assuming vertex_image_model is initialized globally or passed in
+    #     # vertex_image_model = ImageGenerationModel.from_pretrained("imagegeneration@006") # Example init
+    #
+    #     response = vertex_image_model.generate_images(
+    #         prompt=f"Simple kids animation style... NO humans... Aspect ratio {aspect_ratio}. Scene: {prompt}",
+    #         number_of_images=1,
+    #         # Add other relevant parameters like negative_prompt, seed, etc.
+    #     )
+    #
+    #     if response.images:
+    #         image_bytes = response.images[0]._image_bytes # Access image bytes (check actual attribute name)
+    #         image = Image.open(BytesIO(image_bytes))
+    #         logger.info(f"   ✅ [{task_id}] Image generated (Vertex AI).")
+    #         # Check safety attributes if available in Vertex AI response
+    #         return image
+    #     else:
+    #         # Check Vertex AI response for errors / blocking reasons
+    #         logger.warning(f"   ⚠️ [{task_id}] No image data received from Vertex AI.")
+    #         st.warning(f"No image data {task_id} (Vertex AI).", icon="🖼️")
+    #         return None
+    #
+    # except ImportError:
+    #      logger.error(f"   ❌ [{task_id}] Vertex AI SDK ('google-cloud-aiplatform') not installed.")
+    #      st.error(f"Vertex AI SDK not installed for image generation.", icon="🚨")
+    #      return None
+    # except Exception as e:
+    #      logger.exception(f"   ❌ [{task_id}] Vertex AI image generation failed: {e}")
+    #      st.error(f"Image gen failed {task_id} (Vertex AI): {e}", icon="🖼️")
+    #      return None
+    # --- End Placeholder ---
+
+    # Keep the old failing logic commented out or remove, returning None for now
+    return None # Return None until Vertex AI SDK is implemented
+
 
 # --- Streamlit UI Elements ---
+# (Identical to previous version)
 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="🚨")
@@ -266,9 +238,7 @@ 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>Txt:{TEXT_MODEL_ID}, Img:{IMAGE_MODEL_ID}, Aud:{AUDIO_MODEL_ID}</small>", unsafe_allow_html=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:
@@ -277,7 +247,7 @@ if generate_button:
         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 = {}
+        final_video_paths, generation_errors = {}, {}
 
         # --- 1. Generate Narrative Structure ---
         chrono_response: Optional[ChronoWeaveResponse] = None
@@ -288,6 +258,7 @@ if generate_button:
             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 setup - same as before) ...
                     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] = []
@@ -295,6 +266,7 @@ if generate_button:
                     timeline_start_time = time.time(); scene_success_count = 0
 
                     for scene_index, segment in enumerate(segments):
+                        # ... (Scene setup - same as before) ...
                         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})** ---")
@@ -303,93 +275,77 @@ if generate_button:
                         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 ---
+                        # !!! This call will currently return None until Vertex AI SDK is implemented !!!
                         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:
+                            generated_image = generate_image_imagen(combined_prompt, aspect_ratio, task_id) # Needs Vertex AI SDK update
+
+                        if generated_image: # This block will likely not execute currently
                             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
+                        else:
+                            # Error logged within generate_image_imagen if it fails
+                            scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Img gen fail.")
+                            continue # Skip rest of scene processing if image fails
 
                         # --- 2b. Audio Generation ---
+                        # (Audio generation logic remains the same, but won't be reached if image fails)
                         generated_audio_path: Optional[str] = None
                         if not scene_has_error:
+                             # ... (Audio generation logic - same as before) ...
                             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:
-                                    with open(generated_audio_path, 'rb') as ap: st.audio(ap.read(), format='audio/wav')
+                                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.")
-                                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 img due to audio fail."); del temp_image_files[scene_id]
-                                    except OSError as e: logger.warning(f"   ⚠️ [{task_id}] Failed remove img after audio fail: {e}")
-                                continue
+                            else: scene_has_error = True; generation_errors[timeline_id].append(f"S{scene_id+1}: Audio gen fail."); continue # Skip clip if audio fails
 
                         # --- 2c. Create Video Clip ---
+                        # (Clip creation logic remains the same, but won't be reached if image/audio fails)
                         if not scene_has_error and scene_id in temp_image_files and scene_id in temp_audio_files:
-                            st.write(f"   🎬 Creating clip S{scene_id+1}...")
-                            img_path, aud_path = temp_image_files[scene_id], temp_audio_files[scene_id]
+                            # ... (Video clip creation logic - same as before) ...
+                            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.")
+                                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: # Ensure clips are closed even on error here
                                 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 err: {e_rem}")
+                                # Don't remove files here on error, let assembly logic handle based on overall success
 
                     # --- 2d. Assemble Timeline Video ---
+                    # (Video assembly logic remains the same)
                     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 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}] Clip close err {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}] Final vid close err: {e_close_final}")
+                    if video_clips and scene_success_count == len(segments): # Only if ALL scenes succeeded
+                        # ... (Video assembly logic) ...
+                         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: # Close clips used in assembly
+                             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(segments) - scene_success_count; 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
+                    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 ---
+                # (Final status update logic remains the same)
                 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)
@@ -398,6 +354,7 @@ if generate_button:
             # --- 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]
@@ -409,20 +366,33 @@ if generate_button:
                             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):
-                                with st.expander(f"⚠️ View {len(generation_errors[timeline_id])} Issues"): [st.warning(f"- {err}") for err in generation_errors[timeline_id]]
+                            if generation_errors.get(timeline_id): # Check if errors exist for this timeline
+                                # Filter out non-assembly errors for display below video
+                                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}") # Use standard loop
                         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:
+            else: # No videos generated
                 st.warning("No final videos were successfully generated.")
-                all_errors = [msg for err_list in generation_errors.values() for msg in err_list]
-                if all_errors:
-                    st.subheader("Summary of Generation Issues");
+                # Display summary of ALL errors using a standard loop to avoid ValueError
+                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"T{tid}:"); [st.error(f"  - {msg}") for msg in errors]
+                            if errors:
+                                st.error(f"**Timeline {tid}:**") # Use markdown bold
+                                # Use standard for loop here - FIX for ValueError
+                                for msg in errors:
+                                    st.error(f"  - {msg}")
+                else: # Should not happen if no videos, but handle defensively
+                     st.info("No generation errors were recorded.")
+
 
             # --- 4. Cleanup ---
+            # (Cleanup logic remains the same)
             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="⚠️")