Tentando aplicar a rede neural do zero

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 Diabetes.pkl filter=lfs diff=lfs merge=lfs -text
+Dianetes_Neural_Network.pkl filter=lfs diff=lfs merge=lfs -text

Dianetes_Neural_Network.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b017b04b57cb6ce8628b1d99f9fa9b7edd5ca864919686e1589e211ed014eb28
+size 2839

app.py

@@ -1,9 +1,23 @@
 import gradio as gr
 import pandas as pd
+import torch
 from fastai.tabular.all import load_learner
 
 learn = load_learner('Diabetes.pkl')
+coeffs = torch.load('modelo_rede_neural.pkl')
 
+import torch.nn.functional as F
+
+def coeff(coeffs, indeps):
+    layers,consts = coeffs
+    n = len(layers)
+    res = indeps
+    for i,l in enumerate(layers):
+        res = res@l + consts[i]
+        if i!=n-1: res = F.relu(res)
+    return torch.sigmoid(res)
+
+"""
 def predict(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
     inputs = {'Pregnancies': Pregnancies,
               'Glucose': Glucose,
@@ -16,7 +30,22 @@ def predict(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, Di
 
     df = pd.DataFrame([inputs])
 
-    pred, _, _ = learn.predict(df.iloc[8])
+    pred, _, _ = learn.predict(df.iloc[0])
+
+    return pred.item()
+
+iface = gr.Interface(fn=predict, 
+                     inputs=["number", "number", "number", "number", "number", "number", "number", "number"], 
+                     outputs="number",
+                     description="Insira os dados para prever o resultado de diabetes")
+
+iface.launch()
+"""
+
+def predict(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
+    inputs = torch.tensor([Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]).float()
+
+    pred = coeff(coeffs, inputs)
 
     return pred.item()
 
@@ -25,4 +54,4 @@ iface = gr.Interface(fn=predict,
                      outputs="number",
                      description="Insira os dados para prever o resultado de diabetes")
 
-iface.launch()
+iface.launch()