Update app.py

app.py

@@ -1,18 +1,39 @@
 import gradio as gr
 from fastai.tabular.all import *
 import pandas as pd
+import torch
 
 model = load_learner('learn_model.pkl')
 
+def convert(vocab, data):
+    tensor =  torch.zeros(len(vocab), len(data))
+    for i, char in enumerate(vocab):
+        tensor[i][ord(char) - ord('a')] = i
+
+    return tensor
     
 def predict(age, hypertension, heart_disease, avg_glucose_level, bmi, gender, married, work_type, residence_type, smoking_status):
     
     columns_df = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi','smoking_status']
+    tensor = []
+    
+    data = [gender, age, hypertension, heart_disease, married, work_type, residence_type, avg_glucose_level, bmi, smoking_status]
+
     
-    data = [[gender, age, hypertension, heart_disease, married, work_type, residence_type, avg_glucose_level, bmi, smoking_status]]
-    df = pd.Dataframe(data, columns=columns_df)
+    # df = pd.Dataframe(data, columns=columns_df)
+    age_tensor = torch.tensor(age, dtype=torch.int64)
+    hypertension_tensor = convert(hypertension, data)
+    heart_disease_tensor = convert(heart_disease, data)
+    avg_glucose_level_tensor = torch.tensor(avg_glucose_level, dtype=torch.float)
+    bmi_tensor = torch.tensor(bmi, dtype=torch.float)
+    gender_tensor = convert(hypertension, data)
+    married_tensor = convert(married, data)
+    work_type_tensor = convert(work_type, data)
+    residence_type_tensor = convert(residence_type, data)
+    smoking_status_tensor = convert(smoking_status, data)
     
-    prediction = model.predict(df)
+    tensor = torch.cat([gender_tensor, married_tensor, work_type_tensor, residence_type_tensor, smoking_status_tensor, bmi_tensor, age_tensor, hypertension_tensor, heart_disease_tensor, avg_glucose_level_tensor])
+    prediction = model.predict(tensor)
     return "O paciente tem a seguinte possibilidade de infarto: " + str(predictions[0])
 
 gr.Interface(