Update app.py

app.py

@@ -1,7 +1,58 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd
+import joblib
+import os
+import numpy as np
+
+model = joblib.load("nutriscore_model.pkl")
+
+labelencoder = joblib.load("labelencoder.pkl")
+
+std_scale = joblib.load("std_scale.pkl")
+
+cols = ['energy_kcal_100g', 'fat_100g', 'saturated_fat_100g',
+        'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g']
+
+temp = pd.DataFrame(columns=cols)
+
+def greet(energy_kcal_100g, fat_100g, saturated_fat_100g,
+       carbohydrates_100g, sugars_100g, proteins_100g, salt_100g):
+
+  total =  fat_100g  + carbohydrates_100g +  proteins_100g + salt_100g
+  if total > 100:
+      return "Erreur de saisie : total des informations nutritionnelles pour 100g > 100"
+
+  if saturated_fat_100g > fat_100g:
+      return 'Erreur de saisie : graisses saturées > graisses'
+
+  if sugars_100g > carbohydrates_100g:
+      return 'Erreur de saisie : sucres > glucides'
+
+  else:
+
+    temp['energy_kcal_100g'] = [energy_kcal_100g]
+    temp['fat_100g'] = [fat_100g]
+    temp['saturated_fat_100g'] = [saturated_fat_100g]
+    temp['carbohydrates_100g'] = [carbohydrates_100g]
+    temp['sugars_100g'] = [sugars_100g]
+    temp['proteins_100g'] = [proteins_100g]
+    temp['salt_100g'] = [salt_100g]
+
+    X = temp.values
+
+    temp_scaled = std_scale.transform(temp_scaled)
+
+      # Make prediction
+    y_pred = model.predict(temp)
+
+    # reverse label encoding
+    y_pred = labelencoder.inverse_transform(y_pred)
+
+    return y_pred[0].capitalize()
+
+iface = gr.Interface(fn=greet, inputs=["number", "number", "number", "number","number", "number", "number"],
+                     outputs="text")
 iface.launch()