🔍🔒The "Auto-Generation of Large Language Model Prompts from Oncology Data Model Specification" project aims to develop a system that automatically generates prompts for large language models (LLMs) using oncology data model specifications. Oncology data models are structured representations of data elements and their relationships in the field of oncology, encompassing various aspects such as patient demographics, diagnoses, treatments, and outcomes. 🛡👀
- [Automatic Prompt Generation 🕵♂🔍](#Automatic Prompt Generation)
- Context-Awareness 🔊🔐
- [Integration with LLMs 📷🔐](#Integration with LLMs)
- Summary 📷🔐
Automatically generate human-readable prompts from oncology data model specifications. The system analyzes structured representations of oncology data elements, attributes, and relationships to create prompts suitable for large language models (LLMs). By automating prompt generation, users can effortlessly formulate queries and tasks without manual intervention, enhancing efficiency and productivity in data analysis workflows. ⚙📹🚀
Ensure context-aware prompt generation by considering domain-specific conventions and contextual information inherent in oncology data models. The system leverages advanced algorithms and natural language processing techniques to generate prompts that are not only accurate but also contextually relevant. By incorporating domain knowledge and context, the prompts facilitate better communication with LLMs, resulting in more meaningful and accurate responses. 🎶🔊🤖💪
Integrate seamlessly with existing large language models, such as Gemini, to enable prompt-response generation. The system provides robust interfaces for interacting with LLMs, allowing users to input generated prompts and obtain relevant responses. Leveraging state-of-the-art language models enhances the system's ability to interpret and respond to complex queries, ultimately improving the quality of insights derived from oncology data. 🌐🖼🚀💡
Our project automates the generation of human-readable prompts from oncology data model specifications. Integrated with large language models like Gemini, it enhances data analysis in oncology by providing context-aware prompts tailored to specific queries. With user-friendly interfaces and rigorous validation, our solution promises to streamline oncology data analysis workflows, driving advancements in cancer research and treatment.
Accelerated Model Training: Leveraging Intel Developer Cloud's high-performance CPU and XPU capabilities significantly accelerates the training of machine learning models used in the project. This expedites the development process and enables faster iteration cycles. 💻⚡
Optimized Hardware Resources: Intel Developer Cloud provides optimized hardware resources specifically tailored for AI and machine learning workloads. This ensures efficient utilization of resources and maximizes performance during model training and inference. 🚀🔧
Integration with oneDNN: The integration of oneDNN (oneAPI Deep Neural Network Library) further enhances performance by optimizing computational tasks involved in model training. This results in faster training times and improved efficiency. 🚀⚒
Reduced Processing Time: By harnessing the computational power of Intel Developer Cloud, processing times for tasks such as data preprocessing, model training, and inference are significantly reduced. This enables quicker generation of prompts from oncology data model specifications. 🚀🔧
Enhanced Model Performance: The advanced hardware capabilities provided by Intel Developer Cloud contribute to improved model performance and accuracy. This ensures that the generated prompts are of high quality and effectively capture the nuances of oncology data. 🏋♂🧑💻
In summary, Intel Developer Cloud's advanced CPU and XPU technologies provided us with the computational power necessary to expedite model training and inference processes, ultimately accelerating our project development and deployment timelines. 🚀🕒
The flow diagram illustrates the sequential steps and interactions within our system. Each stage in the process contributes to the overall functionality, ensuring a smooth and efficient workflow. Let's delve into the key components:
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User Input 🤖🗣:
- Users initiate the process by providing input, whether through text prompts.
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Preprocessing Report📝:
- The oncology data report undergoes preprocessing to ensure consistency and compatibility with the prompt generation algorithm.
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Analyze Report Content and Structure 👤🚀:
- The preprocessed report is analyzed to understand its content and structure. This involves identifying key information, relationships, and context within the report.
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Contextual Data Understanding and Analysis 🛍💡:
- The algorithm interprets the contextual data extracted from the report to gain insights into the underlying oncological concepts, terminology, and domain-specific conventions.
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Generate Prompt from Analysis 🌐👀:
- Based on the analysis of the report content and contextual understanding, the algorithm generates a human-readable prompt tailored to the specific query or task at hand.
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Output Prompt for LLM 💬🤖:
- The generated prompt is provided as input to a large language model (LLM), such as Gemini, to produce a relevant and informative response.
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Frontend - HTML/CSS: Our project's frontend user interface is built using HTML for structure, CSS for styling, and JavaScript for interactivity. By leveraging these fundamental web technologies, we ensure compatibility across various browsers and devices. The use of HTML provides the structural foundation, CSS enhances the visual appeal with styling elements, and JavaScript adds dynamic behavior to create a seamless user experience. 💻🌐
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*Backend - Flask:*The backend of our application is powered by Flask, a lightweight and flexible web framework for Python. Flask enables us to develop RESTful APIs and handle server-side logic efficiently. Its simplicity and ease of use allow for rapid development of backend functionalities, including data processing, routing, and interaction with databases. With Flask, we ensure scalability and maintainability while delivering robust backend services for our project. 🐍🚀
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Machine Learning Models: Our application integrates various machine learning models to provide intelligent features and recommendations. These models were developed using state-of-the-art libraries and frameworks, including TensorFlow, PyTorch, and Hugging Face Transformers. Leveraging the power of machine learning, we implemented functionalities such as outfit recommendation, virtual try-on, fashion chatbot, and human detection. 🤖⚙
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Other Technologies: In addition to React, Flask, and machine learning models, our application utilizes a range of other technologies to enhance performance, security, and user experience. These include:
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Bootstrap: We utilize Bootstrap, a popular front-end framework, to streamline the design process and create responsive, mobile-first layouts. Bootstrap's pre-designed components and grid system expedite frontend development, ensuring consistency and compatibility across devices. 📊🔍
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Intel Developer Cloud: Leveraging Intel's high-performance CPU and XPU capabilities, we accelerate certain computational tasks within our application, such as data processing and model inference. By harnessing Intel Developer Cloud's optimized hardware resources, we enhance performance and efficiency, delivering a seamless user experience. ⚡💻
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- Developed frontend using HTML,CSS for a modular and reusable UI. 💻🔧
- Implemented backend with Python 🐍🚀
- Integrated various machine learning models for seamless communication and chatbot functionalities. 🤖⚙
- Implemented OCR feature with complex image processing and machine learning techniques. 📷🔄
- Integrated a chatbot leveraging natural language processing (NLP) capabilities. 💬🤖
In the realm of healthcare, we introduce a groundbreaking solution designed to empower patients and their caretakers with comprehensive health insights while also serving as an educational tool for students and healthcare professionals. Our platform facilitates seamless access to patient reports, enabling caretakers to monitor patient health effectively and fostering a deeper understanding of medical conditions for students.
By bridging the gap between patient care and education, our platform revolutionizes the way healthcare is delivered and medical knowledge is disseminated. Whether you are a patient seeking to take control of your health or a student eager to deepen your understanding of medical diagnostics, our platform offers a transformative experience that empowers individuals and enhances the quality of care. Join us in shaping the future of healthcare and education through innovation and collaboration. 🩺📚🚀