python-flask-app-with-replicate-llava-image-captioning
Develop a Flask web application that serves an HTML interface and integrates with the Replicate API to generate image captions using the LLaVA model.
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Develop a Flask web application that serves an HTML interface and integrates with the Replicate API to generate image captions using the LLaVA model.
Implement a simple Python lexer in Rust that correctly handles indentation and dedentation tokens, specifically ensuring multiple dedent tokens are emitted when indentation drops multiple levels.
Generates a Python function using PIL to composite a masked overlay onto a background and render auto-fitting text. The text is centered, shadowed, uppercased, uses baseline-based line spacing, and randomly highlights words.
Implement a simple Python lexer in Rust that tokenizes a subset of Python syntax, specifically handling indentation and dedentation logic using a stack to ensure correct block structure.
Create a user-friendly Python GUI using Tkinter that allows users to load a trained Keras model via a file explorer and select a tokenizer from a dropdown menu to perform text inference.
Buat script Python untuk membuat file HTML artikel prediksi togel Hongkong dengan meta tag SEO lengkap (Open Graph, Twitter Card) dan encoding UTF-8.
使用Python Tkinter Treeview组件读取Excel文件,实现点击列头排序、添加动态行序号、双击单元格弹窗显示内容以及自定义列宽的功能。
使用Python和OpenCV的cv2.floodFill实现类似PS的魔术橡皮擦功能,封装为类,根据传入坐标点的颜色填充相似区域。
使用Python Tkinter库编写一个简单的加减法计算器。要求窗口可调整大小,所有控件(输入框、操作符、按钮)排列在同一行,结果显示在下一行,并包含中文注释及特定的样式要求。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
视频分镜拆解技能(自包含,无需外部后端)。使用 FFmpeg 预处理视频并生成分镜数据。(1) 视频 URL 直接处理 `python scripts/process_video.py "<video_url>"`;(2) 本地文件先上传 `python scripts/video_upload.py "<file_path>"` 获取 URL 后再处理。需要本机安装 FFmpeg。
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
漫剧制作总导演。接受用户故事创意,依次完成剧本创作、角色设计、分镜图生成、分镜视频生成、视频合成全流程,产物保存到 COMIC_DRAMA_OUTPUT_DIR(默认 ./output),最终交付完整漫剧视频和 TOS 链接。
Native Python execution via the local Python bridge. Use when the user asks about running Python locally, setting up the Python bridge, LibreOffice conversion, or troubleshooting Python connectivity.
在 ANP SDK 仓库里统一发布 Go、Python、Rust 三个 SDK。用于同时更新 pyproject.toml、uv.lock、rust/Cargo.toml、rust/Cargo.lock,执行 uv/cargo/go 校验,发布 PyPI 与 crates.io,并按 Go 子目录 module 规则推送 golang/vX.Y.Z tag。用户提到 Go/Python/Rust 一起发版、统一版本号、固定发布 0.7.2、下一版自动 +1、保持版本段为一位数时使用此 skill。
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, or implementing the dialectic chat endpoint for AI agents.
Provides comprehensive guidance for FastAPI framework including routing, request validation, dependency injection, async operations, OpenAPI documentation, and database integration. Use when the user asks about FastAPI, needs to create REST APIs, or build high-performance Python web services.