Create app.py

app.py

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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