Marcelo Fernando Condori Mendoza@mcondori

“MedicQuick AI” is a Streamlit framework website application that works as a medical assistant allowing users to track symptoms and get analysis using an artificial intelligence (AI) model, to upload medical images for lung disease analysis, generate PDF reports based on symptoms and analysis, to visualize symptom and pain trends and to chat with a medical assistant powered by the Falcon 180B Model. A symptom tracker is established so users get described symptoms and receive analysis from an AI model (Falcon 180B). It generates a downloadable PDF report with the analysis and recommendations. Medical image analysis is addressed to upload X-rays or similar images for simulated analysis of lung diseases (cancer, pneumonia, COVID-19). In addition, users can visualize trends in their symptoms and pain levels over time, along with a pie chart showing symptom distribution. Finally, a medical chatbot lets the users chat with a medical assistant powered by another AI model, asking questions and receiving responses. This is a prototype, and AI analysis should not replace professional medical advice. The team members participated in different roles such as front and back-end development, AI engineering, business deployment, and project management, making the idea into a simple app. Modern healthcare challenges include diagnostic delays, error rates, resource constraints, and patient communication. As a result, MedicQuick AI leverages the Falcon 180B Model to enhance healthcare with advanced symptom analysis, report generation, and real-time patient support. Why MediQuick AI? Advanced AI Models analyze patient symptoms to suggest potential diagnosis. Furthermore, AI-powered X-ray image scanning lets the users determine whether they have COVID-19, Pneumonia, or Lung Cancer. Important prototype key features include the Falcon 180B AI model for accurate symptom analysis and PDF reports generation for diagnostic data comprehension.

This code creates a graphical user interface (GUI) application for detecting faces in images. Here's a breakdown of its functionalities. Lets the user select an image file using a file dialog. It then reads the image with OpenCV, converts it to grayscale, and displays a thumbnail of the loaded image in the GUI window. Takes an image and resizes it to a smaller thumbnail size (200x200 pixels). It then converts the image from BGR (OpenCV format) to RGB (used by Python's Image library) and displays it on a designated label in the GUI. Checks if an image has been loaded first. If not, it displays a warning message. Otherwise, it uses the pre-trained face cascade classifier (face_cascade) to detect faces in the grayscale version of the image. It allows for some adjustments in detection parameters like scaling factor (scaleFactor) and minimum number of neighboring detections (minNeighbors) to improve accuracy. If no faces are found, it displays an info message. If faces are found, it creates a directory named assets/output to store the detected faces. It iterates through each detected face and saves it as a separate JPG image. Finally, it displays a success message with the number of detected faces and the output directory path. The code creates a main window (root) with the title "Detector de Rostros" (Face Detector) and sets its size. It defines a label (panel) in the window to display the image thumbnail.