@pranjal_kar827

Edulance is an open-source project that utilizes advanced technologies such as Unstructured, machine learning models, and APIs to transform text documents and PDFs into interactive educational resources. The software accepts user-uploaded files, applies optical character recognition (OCR) for text documents, or extracts valuable content from PDFs. It then generates lessons, quizzes, and lesson plans based on the content using its Lesson Graph model and agents like LessonGenerator, LessonPlanner, OCRAgent, PdfAgent, QuizAgent, and TogetherLLM. Edulance provides an immersive learning experience, enabling effective teaching and interactive knowledge acquisition. Overall this project incorporates the following: TogetherAI's LLM Models Unstructured Partition pdf for making PDFs LLM Ready Agentic AI with state management. Features Feature Description ⚙️ Architecture Edulance is a Python-based project using FastAPI as the web framework and Uvicorn for runtime serving. The application leverages containers with Docker for deployment, installing required dependencies from requirements.txt. It utilizes libraries like LangChain, PikePDF, PyTesseract for OCR, and TogetherAI's LLM models. 🔩 Code Quality The codebase follows a modular structure with clearly defined agents and graph files, ensuring high cohesion and low coupling. Python style guides are followed consistently, including PEP8 and PEP534. There is adequate usage of comments throughout the codebase.🔌 Integrations Key integrations include Docker for deployment, LangChain libraries, TogetherAI's LLM models, Vectara for Chat. 🧩 Modularity 📦 Dependencies Main dependencies include FastAPI, Docker, Python 3.10, requirements.txt, LangChain package, PikePDF, PyTesseract, and related tools.

The code aims to solve the challenge of generating attention-grabbing headlines for news videos by harnessing the power of natural language processing and the Falcon-40b language model. Crafting compelling news headlines is essential for engaging viewers and conveying the essence of a news story effectively. However, manually creating these headlines can be time-consuming and creatively demanding. This solution automates the headline generation process, making it more efficient and potentially improving the quality of headlines. Falcon-Generated Headlines: The system generates attention-grabbing headlines using Falcon, a cutting-edge language model. These headlines are tailored to the news topic, ensuring relevance and viewer interest. Video Frame Generation: Python's MoviePy library is employed to transform these headlines into dynamic video frames. This step enhances the visual appeal of the news video while maintaining concise and informative content. Background Music Integration: To further captivate the audience, background music is seamlessly integrated into the video. The choice of music complements the news content, enhancing emotional resonance and viewer engagement. Efficiency and Speed: By automating headline generation and video frame creation, this solution significantly reduces the time and effort required for news video production. News agencies can produce content faster and stay ahead of breaking news. Increased Engagement: Short, visually appealing news videos with relevant headlines are more likely to grab viewers' attention. This leads to higher engagement, increased views, and improved viewer retention, driving growth for news agencies.

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