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app.py

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+import streamlit as st 
+import torch
+import torch.nn.functional as f
+import torch.nn as nn
+import joblib 
+
+html_temp = """
+    <div style="background-color:black;padding:10px">
+    <h2 style="color:white;text-align:center;"> Fetal Health App </h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+## model development 
+class ClassificationModel(nn.Module):
+    def __init__(self, input_dim):
+        super(ClassificationModel, self).__init__()
+        self.fc1 = nn.Linear(input_dim, 16)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.2)
+        self.fc2 = nn.Linear(16,8)
+        self.fc3 = nn.Linear(8, 4)
+        self.fc4 = nn.Linear(4, 2) # 2 outputs for binary classification
+
+    def forward(self, x):
+        x = f.relu(self.fc1(x))
+        x = f.relu(self.fc2(x))
+        x = f.relu(self.fc3(x))
+        x = self.fc4(x)  # No softmax, since CrossEntropyLoss applies it
+        return x
+    
+torch.manual_seed(42)    
+model=ClassificationModel(input_dim=11)
+##  loading the models
+
+scaler_x=joblib.load('x_sacaler_model.joblib')
+
+# Load the entire model
+
+model.load_state_dict(torch.load( "fetal_health_model.pth", map_location=torch.device("cpu")))
+model.eval()  
+
+## inputs 
+col1,col2 =st.columns(2)
+
+with col1:
+    #  Age
+    age = st.slider("Slide the age of Patient in date wise",min_value=10798,max_value=23713,value=1500)
+
+with col2:
+    # gender
+    gender_option =["MALE","Female"] 
+    gender=st.selectbox("Select the Gender of the Patient ",gender_option)
+    gender_value = gender_option.index(gender)
+with col1:
+    # height
+    height = st.slider("Slide Height of the Patient in cm's ",min_value=55,max_value=250,value=75)
+with col2:
+    #  Weight
+    weight = st.slider("Slide the Weight of the Patient in Kg's ",min_value=10.0,max_value=200.0,value=50.0)
+col1,col2=st.columns(2)
+with col1:
+    #  ap_hi
+    ap_hi =st.slider("Slide the ap high of the Patient ",min_value=-150,max_value=16020,value=15)
+with col2:
+    # ap_lo
+    ap_lo=st.slider("Slide the ap Low of the Patient ",min_value=-70,max_value=11000,value=200)
+col1,col2=st.columns(2)
+with col1:   
+    # cholesterol
+     chol_option =["Normal","Above Normal","Well"] 
+     chol=st.selectbox("Select the Gender of the Patient",chol_option)
+     chol_value=chol_option.index(chol)
+    
+with col2:
+     glue_option =["Normal","Above Normal","Well"] 
+     glue=st.selectbox("Select the Glue of the Patient",glue_option)
+     glue_value=glue_option.index(glue)
+col1,col2=st.columns(2)
+with col1:  
+     smoke_option =["NO","Yes"] 
+     smoke=st.selectbox("Are YOU smoker ",smoke_option)
+     smoke_value=smoke_option.index(smoke)
+with col2:
+     aclo_option =["NO","Yes"] 
+     aclo=st.selectbox(" Are you Alcholic ",aclo_option)
+     aclo_value=aclo_option.index(aclo)
+    
+active_option =["NO","Yes"] 
+active=st.selectbox("Are you  ",active_option)
+active_value=active_option.index(active)
+
+if st.button("Enter"):
+    # st.write(age,gender_value,height,weight,ap_hi,ap_lo,chol_value,
+                                # glue_value,smoke_value,aclo_value,active_value)
+    
+    values=scaler_x.transform([[age,gender_value,height,weight,ap_hi,ap_lo,chol_value,
+                                glue_value,smoke_value,aclo_value,active_value]])
+    
+    # st.write(values)
+    values_tensor=torch.tensor(values,dtype=torch.float32)
+
+    with torch.no_grad():  # Disable gradient calculation (faster inference)
+        output = model(values_tensor)
+    st.write(output.argmax(dim=1))
+    out_value=output.argmax(dim=1)
+    if out_value ==0:
+        st.write("The Patient is Healthy")
+    else :
+        st.write("The Patient is UN Healthy")

fetal_health_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec94ef15d340f169173f4744df9d948d7bb5b7f2cd62bd6a623528bfc0569bac
+size 4676

x_sacaler_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9619ec7120481a07f6d94a5b74917da409eb79d493257c440786bad4a0c88e7
+size 863