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Hair Loss Diagnosis with Stage Detection
Hair Loss Diagnosis Project: Leverages deep learning and image processing to diagnose hair conditions from varied angles (front, left, right, top views).
Privacy Measures: Blurs facial features for user anonymity before storing images in S3, ensuring confidentiality.
UI Design: Offers gender selection, image blurring for privacy, and multi-angle image capture for analysis.
Disease Identification: Utilizes CNN and ResNet 152 models, achieving 97% accuracy for no hair loss cases.
Result Interpretation: Delivered detailed reports on hair condition, potential diseases, and probabilities of hair issues.
Model Performance: Trains with 92% accuracy in training and 87% in testing, ensuring reliability.
Deployment: Live project uses trained models for accurate analysis, prioritizing user privacy.
Cloud Storage: Stores analyzed images with timestamps in S3 for organized data access.Computer Vision, Deep Learning, Machine Learning, Data Science, Python, AWS S3
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AI Blog Generator
Overview: Developed an AI-powered blog content generator leveraging OpenAI's GPT-4 models.
Objective: The project aimed to automate the creation of blogs by generating blog content.
Technologies Used: Streamlit, OpenAI API, Python, PIL, Requests.
Key Features:
Natural Language Generation: Implemented GPT-4 model to generate blog content in Markdown format based on user-provided topics.
Image Generation: Utilized OpenAI's image generation API to fetch relevant images corresponding to the blog topics.
Interactive Interface: Developed a user-friendly interface using Streamlit for easy interaction and blog post generation.■ OpenAI
■ Machine Learning
■ Data Science
■ Python
■ Data Analysis