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Browse files- tools/image_generation_tools.py +157 -0
tools/image_generation_tools.py
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"""
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Image generation tools for visualizing song analysis results.
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"""
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import os
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from typing import Dict
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from loguru import logger
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from smolagents import Tool
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from api_utils import make_api_call_with_retry
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def caption_gen_tool(analysis_json: Dict, title: str, artist: str) -> str:
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"""
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Generate a descriptive caption for image generation based on song analysis.
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Uses LLM to create a high-quality image prompt based on the analysis.
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Args:
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analysis_json: Dictionary containing the song analysis results
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title: Song title (required)
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artist: Song artist
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Returns:
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A descriptive caption suitable for image generation
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"""
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logger.info("Generating image caption from analysis results")
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# Use the provided title and artist
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logger.info(f"Using song: '{title}' by '{artist}' for caption generation")
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mood = analysis_json.get("mood") or "emotional"
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themes = ", ".join(analysis_json.get("main_themes") or ["music"])
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summary = analysis_json.get("summary") or ""
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conclusion = analysis_json.get("conclusion") or ""
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# Create an API prompt to generate a high-quality image caption
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prompt = f"""Generate a detailed, vivid, and artistic image generation prompt based on the following song analysis.
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This prompt will be used by an AI image generator to create a visual representation of the song's essence.
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Song: {title} by {artist}
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Mood: {mood}
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Themes: {themes}
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Summary: {summary[:200] if summary else ""}
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Conclusion: {conclusion[:200] if conclusion else ""}
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Your task is to create a single paragraph (approximately 100-150 words) that vividly describes a scene or abstract image
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that captures the emotional essence and themes of this song. The description should be detailed, visual, and evocative.
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DO NOT include any text, words, or lyrics in the image description. Focus on colors, composition, mood, symbols, and visuals only.
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ONLY output the final image generation prompt with no additional text, explanations, or formatting.
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"""
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# Use the same model as in lyrics analysis
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model_to_use = "openrouter/google/gemini-2.0-flash-lite-preview-02-05:free"
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logger.info("Using {} for caption generation", model_to_use)
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# Call the API to generate a caption
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logger.info("Generating image caption for song: '{}' by '{}'", title, artist)
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response_text = make_api_call_with_retry(model_to_use, prompt)
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# Clean up the response if needed
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caption = response_text.strip()
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logger.debug(f"Generated image caption: {caption[:100]}...")
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return caption
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class GenerateImageTool(Tool):
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"""Tool for generating images based on song analysis"""
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name = "generate_image"
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description = "Generates an image based on the song analysis results"
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inputs = {
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"analysis_json": {"type": "any", "description": "JSON dictionary containing the analysis results"},
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"title": {"type": "string", "description": "Title of the song"},
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"artist": {"type": "string", "description": "Artist of the song"}
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}
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output_type = "string"
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def generate_with_gemini(self, caption: str) -> str:
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"""
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Generate image using Gemini API directly
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Args:
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caption: The prompt text for image generation
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Returns:
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HTML img tag with the image or error message
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"""
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try:
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# Правильный импорт библиотеки
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import google.generativeai as genai
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from google.generativeai import types
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# Get API key from environment variable
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api_key = os.environ.get("GEMINI_API_KEY")
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if not api_key:
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logger.error("GEMINI_API_KEY not found in environment variables")
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return "<p>Error: Gemini API key not found. Please set the GEMINI_API_KEY environment variable.</p>"
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logger.info("Initializing Gemini client")
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genai.configure(api_key=api_key)
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client = genai.Client()
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logger.info("Generating image with Gemini")
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response = client.models.generate_content(
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model="gemini-2.0-flash-exp-image-generation",
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contents=caption,
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config=types.GenerateContentConfig(
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response_modalities=['Text', 'Image']
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)
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)
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# Process the response
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for part in response.candidates[0].content.parts:
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if part.text is not None:
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logger.info(f"Gemini response text: {part.text[:100]}...")
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elif hasattr(part, 'inline_data') and part.inline_data is not None:
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# Save the image to a temporary file
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# inline_data.data уже содержит данные в формате base64
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image_b64 = part.inline_data.data
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img_html = f'<img src="data:image/png;base64,{image_b64}" alt="Generated image based on song analysis" style="max-width:100%; border-radius:10px; box-shadow:0 4px 8px rgba(0,0,0,0.1);">'
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return img_html
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return "<p>Error: No image generated by Gemini API.</p>"
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except ImportError:
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logger.error("Google GenAI package not installed")
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return "<p>Error: Google GenAI package not installed. Install with 'pip install google-generativeai'</p>"
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except Exception as e:
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logger.error(f"Error generating image with Gemini: {str(e)}")
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return f"<p>Error generating image with Gemini: {str(e)}</p>"
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def forward(self, analysis_json: Dict, title: str, artist: str) -> str:
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"""
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Generates an image based on the analysis results using Gemini API.
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Args:
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analysis_json: Dictionary containing the analysis results
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title: Song title
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artist: Song artist (required)
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Returns:
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HTML img tag with the image or error message
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"""
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try:
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# Generate caption for the image
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caption = caption_gen_tool(analysis_json, title=title, artist=artist)
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logger.info("Caption generated successfully")
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logger.warning("OpenRouter failed, falling back to Gemini API")
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# Fall back to Gemini API
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result = self.generate_with_gemini(caption)
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return result
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except Exception as e:
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logger.error(f"Error in image generation: {str(e)}")
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return f"<p>Error in image generation: {str(e)}</p>"
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