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| import streamlit as a | |
| import cv2 as b | |
| import numpy as c | |
| import pandas as d | |
| from yolov8 import e | |
| f = d.read_csv("skin_recommendations.csv") | |
| html_style = """ | |
| <style> | |
| .container { padding: 20px; background-color: #f9f9f9; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);} | |
| .title { color: #ff69b4; font-size: 36px; text-align: center; margin-bottom: 30px;} | |
| .subheader { color: #ff69b4; font-size: 24px; margin-top: 20px;} | |
| .image-container { margin-top: 20px; text-align: center;} | |
| </style> | |
| """ | |
| a.markdown(html_style, unsafe_allow_html=True) | |
| a.markdown("<h1 class='title'>AI Skin Analyzer</h1>", unsafe_allow_html=True) | |
| # Skin Analysis Question | |
| g = a.text_input("Describe your skin concern:", | |
| value="Is there anything unusual I should be aware of?") | |
| # Additional questions | |
| h = a.text_input("Have you experienced any skin irritation recently?", | |
| value="Yes/No") | |
| i = a.text_input("Are you currently using any skincare products?", | |
| value="Yes/No") | |
| j = a.text_input("Do you have any known allergies?", | |
| value="Yes/No") | |
| k = a.text_input("Do you smoke or consume alcohol regularly?", | |
| value="Yes/No") | |
| l = a.text_input("How many hours of sleep do you get per night?", | |
| value="7") | |
| m = a.text_input("How many glasses of water do you drink per day?", | |
| value="8") | |
| # Image Upload | |
| n = a.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if n is not None: | |
| o = b.imdecode(c.fromstring(n.read(), c.uint8), 1) | |
| a.image(o, caption="Uploaded Image", use_column_width=True) | |
| a.markdown("<h2 class='subheader'>Model Predictions:</h2>", unsafe_allow_html=True) | |
| # Load YOLOv8 models | |
| p = [] | |
| p.append(e('yolov8n.pt')) | |
| p.append(e('yolov8s.pt')) | |
| p.append(e('yolov8m.pt')) | |
| p.append(e('yolov8l.pt')) | |
| # Perform object detection with each model | |
| for q, r in enumerate(p): | |
| a.markdown(f"<h3 class='subheader'>Model {q+1}</h3>", unsafe_allow_html=True) | |
| s = r(o) | |
| s.render() | |
| a.image(s.imgs[0], use_column_width=True) | |
| # Recommendations Based on Question | |
| t = u(g) | |
| a.markdown("<h2 class='subheader'>Recommendations:</h2>", unsafe_allow_html=True) | |
| for v in t: | |
| w = f[f['Condition'] == v] | |
| if not w.empty: | |
| a.image(w['Image'].values[0], caption=v, use_column_width=True) | |
| a.write(w['Advice'].values[0]) | |
| # Helper function (needs improvement for accurate matching) | |
| def u(x): | |
| y = ["acne", "spots", "dry"] # Example keywords | |
| z = [] | |
| for aa in y: | |
| if aa in x.lower(): | |
| z.append(aa.capitalize()) | |
| return z | |