added genomic risks

app.py

@@ -43,17 +43,18 @@ def predict(file, age, parent, gp):
     quantity = pad(np.array([320, 450, 300, 120, 280]) / 500)
     min_normal = pad(np.array([250, 200, 210, 100, 250]) / 500)
     max_normal = pad(np.array([400, 300, 450, 150, 300]) / 500)
-    txt = 'Bacteria\t\t\t\t\t\tMin\t\t\tMax\n\n'
-    for i in range(len(product)):
-        txt += product[i]
-        txt += "\t\t\t"
-        txt += quantity[i] + "\t\t" + min_normal[i] + "\t\t" + max_normal[i] + "\t"
-        txt += '\n'
-    txt = str(txt)
+    variants = ["Y2TTB9", "9KKGH7", "ML0JH9"]
+    risks = ['Seborrheic eczema', 'Folliculitis', "Pityriasis amiantacea"]
+    percents = ['30', "30", '9']
+
+    geneticRisks = ""
+
+    for variant, risk, percent in zip(variants, risks, percents):
+        geneticRisks += f"Variant {variant} detected, risk of {risk} is {percent} higher.\n"
 
     useful_products = ("Hydrating Shampoo : https://www.headandshoulders.fr/fr-fr/\n"
                        "Light Hat : https://www.amazon.com/light-therapy-hat/s?k=light+therapy+hat")
-    return skio.imread("results.png"), 'Dermatitis\nDryness\nDandruff', evolution_plot(age), useful_products
+    return skio.imread("results.png"), 'Dermatitis\nDryness\nDandruff', geneticRisks, evolution_plot(age), useful_products
 # GUI
 title = 'Hair loss prediction'
 description = 'Metagenomics Scalp Analysis for Hair Loss Prediction'
@@ -70,6 +71,7 @@ iface = gr.Interface(
     outputs=[
         gr.Image(label='Scalp Metagenomics Analysis Results'),
         gr.Text(label='Current issues :'),
+        gr.Text(label='Genetic Risks :'),
         gr.Image(label='Future Evolution'),
         gr.Text(label='Useful Care Products')
     ],