Update app.py

app.py

@@ -8,17 +8,16 @@ import torch.optim as optim
 from torchvision import transforms
 from PIL import Image
 import pandas as pd
-import joblib
-import os
+import joblib,os
 
 from sklearn.preprocessing import MinMaxScaler,LabelEncoder
 
-path = os.getcwd()
 n_tab_features = 8
 num_classes = 11
 n_pain_cls = 4
 n_hidden = 128
 
+project = os.getcwd()
 scaler = MinMaxScaler(feature_range=(0,1))
 encoder = LabelEncoder()
 
@@ -89,11 +88,15 @@ dense_features = ['Weight_in_Kgs','Heart_rate','pulse_rate','Systolic_BP','Diast
 sparse_features = ['Pain_Position']
 
 # Define a function to get the medicine name from the model output
-def get_medicine_name(pred_medicine_class):
-    med_encoder = joblib.load(path+'/cat_encoder.joblib')
+def get_medicine_name(pred_medicine_class, pred_pain_cls):
+    med_encoder = joblib.load(project+'/cat_encoder.joblib')
     pred_medicine_name = med_encoder.inverse_transform(pred_medicine_class)
+    
+    pain_level_enc= joblib.load(project+'/cat_dos_level_encoder.joblib')
+    pred_dos_level_nm = pain_level_enc.inverse_transform(pred_pain_cls)
+    
     # Return the corresponding medicine name
-    return pred_medicine_name
+    return pred_medicine_name, pred_dos_level_nm
 
 # Define a function to get the model input from the user input
 def get_model_input(Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP, Diastolic_BP, BIS_Value, SPO2, Pain_Position):
@@ -103,17 +106,17 @@ def get_model_input(Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP, Diastoli
     temp = pd.DataFrame(all_values, columns= dense_features + sparse_features)
 #     print(temp.dtypes)
     # Normalize the dense feature values to be between 0 and 1
-    scaler = joblib.load(path+'/scaler.joblib')
+    scaler = joblib.load(project+'/scaler.joblib')
     temp[dense_features] = scaler.transform( temp[dense_features] )
-    print(temp)
+    #print(temp)
 
     # Encode the categorical features 0, 1 etc.
-    painpos_encoder = joblib.load(path+'/cat_pain_level_encoder.joblib')
+    painpos_encoder = joblib.load(project+'/cat_pain_level_encoder.joblib')
     temp[sparse_features] = painpos_encoder.transform( cat_values[0] )
 
     # Create a tensor from the normalized values
     input = torch.tensor(temp.to_numpy()).float()
-    print(input)
+    #print(input)
     # Return the input tensor
     return input
 
@@ -121,13 +124,13 @@ def get_model_input(Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP, Diastoli
 def predict(Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP, Diastolic_BP, BIS_Value, SPO2, Pain_Position):
     # Get the model input from the user input
     X = get_model_input(Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP, Diastolic_BP, BIS_Value, SPO2, Pain_Position)
-    print(X.dtype)
+    #print(X.dtype)
     # Get the model output from the model input
     _, _, _, _, pred_dosage, pred_medicine_class, pred_pain_cls = model(X)
     # Get the medicine name from the model output
-    medicine = get_medicine_name(pred_medicine_class)
+    medicine, dosage_level_nm = get_medicine_name(pred_medicine_class, pred_pain_cls)
     # Return the predicted medicine name as a string
-    return "The predicted medicine for you is: " + medicine
+    return "The predicted medicine for you is: " + medicine[0], dosage_level_nm[0]
 
 Weight_in_Kgs, Heart_rate, pulse_rate, Systolic_BP = 75.3, 85.7, 84, 141
 Diastolic_BP, BIS_Value, SPO2, Pain_Position       = 92, 80, 90, 'Lower Back'
@@ -144,8 +147,7 @@ interface = gr.Interface(
             gr.inputs.Slider(minimum=71,maximum=100,step=1,label="SPO2"), 
             gr.inputs.Dropdown(['Lower Back', 'Shoulder ', 'Abdomine', 'Muscle', 'Neck',
        'Fybromialgia', 'Knee Joint', 'Joint pain', 'Shoulder', 'No Pain'], label="Pain Position", default="Lower Back") ],
-    outputs=gr.outputs.Textbox(label="Medicine") )
+    outputs=[gr.outputs.Textbox(label="Medicine"),gr.outputs.Textbox(label="Dosage Level")] )
 
 # Launch the interface on Hugging Face Spaces (assuming you have an account)
-interface.launch()
-
+interface.launch()