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| import json | |
| import spacy | |
| from langchain.prompts import PromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| from typing import List, Dict | |
| import os | |
| from llm_setup import llm | |
| if not llm: | |
| raise ImportError("Creative LLM could not be loaded. Please check llm_setup.py.") | |
| try: | |
| nlp = spacy.load("en_core_web_sm") | |
| print("✅ spaCy NLP model loaded successfully.") | |
| except OSError: | |
| print("❌ spaCy model not found. Please run 'python -m spacy download en_core_web_sm'") | |
| nlp = None | |
| def chunk_story(story_text: str) -> List[str]: | |
| """ | |
| Splits a long story text into meaningful chunks based on sentence boundaries. | |
| """ | |
| if not nlp: | |
| raise ImportError("spaCy model is not loaded. Cannot chunk story.") | |
| print("--- Chunking story into meaningful sentence groups... ---") | |
| # Process the text with spaCy to identify sentences | |
| doc = nlp(story_text) | |
| sentences = [sent.text.strip() for sent in doc.sents] | |
| chunks = [] | |
| current_chunk = "" | |
| # Target character count for each chunk, similar to our previous logic | |
| target_length = 180 | |
| for sentence in sentences: | |
| # If adding the new sentence doesn't make the chunk too long, add it | |
| if len(current_chunk) + len(sentence) + 1 < target_length: | |
| current_chunk += sentence + " " | |
| # Otherwise, the current chunk is complete | |
| else: | |
| # If the current chunk is not empty, add it to the list | |
| if current_chunk: | |
| chunks.append(current_chunk.strip()) | |
| # Start a new chunk with the current sentence | |
| current_chunk = sentence + " " | |
| # Add the last remaining chunk if it exists | |
| if current_chunk: | |
| chunks.append(current_chunk.strip()) | |
| print(f"✅ Story split into {len(chunks)} natural chunks.") | |
| return chunks | |
| def generate_image_prompt(text_chunk: str) -> str: | |
| print(f"--- Generating image prompt for chunk: '{text_chunk[:50]}...' ---") | |
| prompt_template = PromptTemplate.from_template( | |
| """You are an award-winning art director creating a prompt for an AI image generator. | |
| Translate the following story segment into a single, rich, descriptive visual prompt in English. | |
| **Instructions:** | |
| 1. **Style:** 'Epic fantasy digital painting', 'cinematic sci-fi concept art', or 'historical oil painting'. | |
| 2. **Atmosphere & Lighting:** Describe the mood and lighting. | |
| 3. **Composition:** Frame the scene (e.g., 'wide-angle shot', 'close-up portrait'). | |
| 4. **Output:** Combine everything into one single paragraph. | |
| **Story Segment:** "{chunk}" | |
| **Image Generation Prompt (English):**""" | |
| ) | |
| chain = prompt_template | llm | StrOutputParser() | |
| try: | |
| image_prompt = chain.invoke({"chunk": text_chunk}) | |
| return image_prompt.strip().replace("\n", " ") | |
| except Exception as e: | |
| print(f"An error occurred during image prompt generation: {e}") | |
| return "Error: Could not generate image prompt." | |
| def process_story_for_multimedia(story_text: str) -> List[Dict[str, str]]: | |
| # ... (This function remains unchanged) | |
| story_chunks = chunk_story(story_text) | |
| multimedia_data = [] | |
| for chunk in story_chunks: | |
| image_prompt = generate_image_prompt(chunk) | |
| multimedia_data.append({ | |
| "audio_text": chunk, | |
| "image_prompt": image_prompt | |
| }) | |
| return multimedia_data | |
| def save_multimedia_data(data: List[Dict[str, str]], output_path: str = "multimedia_data.json"): | |
| try: | |
| with open(output_path, 'w', encoding='utf-8') as f: | |
| json.dump(data, f, indent=2, ensure_ascii=False) | |
| print(f"✅ Multimedia data successfully saved to {output_path}") | |
| except Exception as e: | |
| print(f"❌ Error saving data to JSON file: {e}") |