A microservices-based platform featuring Deep Learning X-ray analysis, RAG-driven medical chatbots (FastAPI/LangChain), and OCR document processing. Engineered for scalability with Docker and a modern Streamlit interface. - Shrouk-Sharaf/Tammeny
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Maker
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I built Tammeny to solve a problem that is deeply personal to many in the MENA region. I’ve often seen people receive medical reports in English and feel immediate fear because they can’t understand the technical jargon in their own language.
What started as a simple bilingual chatbot evolved into a full multi-model architecture. During the process, I realized that just translating text wasn't enough; patients needed a way to 'see' what their X-rays meant and 'ask' questions about their specific files. By integrating models like Mistral, Qwen 2.5-VL, and DenseNet-121, I was able to build a tool that doesn't just process data it provides reassurance.
Tammeny (طمني) means 'reassure me'. My goal is to ensure that for 400 million Arabic speakers, health communication is no longer a source of confusion, but a source of clarity.
I’d love to hear your feedback on the bilingual flow and the medical image analysis features!