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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+life_expectancy_document.pdf filter=lfs diff=lfs merge=lfs -text

app (1).py

@@ -0,0 +1,173 @@
+import streamlit as st
+import pandas as pd
+import torch
+import torch.nn as nn
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+st.set_page_config(layout="wide")
+
+# Add custom CSS for background image and styling
+# Add custom CSS for background image and styling
+st.markdown("""
+    <style>
+        .stApp {
+            background-image: url("https://cdn.pixabay.com/photo/2020/01/28/11/14/galaxy-4799471_1280.jpg");
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+            height: auto;  /* Allows the page to expand for scrolling */
+            overflow: auto;  /* Enables scrolling if the page content overflows */
+        }
+
+        /* Adjust opacity of overlay to make content more visible */
+        .stApp::before {
+            content: "";
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(255, 255, 255, 0);  /* Slightly higher opacity */
+            z-index: 0;
+        }
+
+        /* Ensure content appears above the overlay */
+        .stApp > * {
+            position: relative;
+            z-index: 1;
+        }
+
+        /* Ensure the dataframe is visible */
+        .dataframe {
+            background-color: rgba(255, 255, 255, 0.9) !important;
+            z-index: 2;
+        }
+
+        /* Style text elements for better visibility */
+        h1, h3, span, div {
+            text-shadow: 1px 1px 2px rgba(255, 255, 255, 0.2);
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+
+# Custom title styling functions
+def colored_title(text, color):
+    st.markdown(f"<h1 style='color: {color};'>{text}</h1>", unsafe_allow_html=True)
+
+def colored_subheader(text, color):
+    st.markdown(f"<h3 style='color: {color};'>{text}</h3>", unsafe_allow_html=True)
+
+def colored_text(text, color):
+    st.markdown(f"<span style='color: {color};'>{text}</span>", unsafe_allow_html=True)
+
+class ANNModel(nn.Module):
+    def __init__(self, input_size):
+        super(ANNModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, 64)
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+@st.cache_resource
+def load_model():
+    _, X_scaled, _ = load_and_preprocess_data()
+    input_size = X_scaled.shape[1]
+    
+    model = ANNModel(input_size=input_size)
+    try:
+        state_dict = torch.load('model_weights.pth', map_location=torch.device('cpu'))
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None
+
+@st.cache_data
+def load_and_preprocess_data():
+    df = pd.read_csv('Life Expectancy Data.csv')
+    df.columns = df.columns.str.strip()
+
+    df_display = df.copy()
+
+    expected_features = [
+        'Adult Mortality', 'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B', 
+        'Measles', 'BMI', 'under-five deaths', 'Polio', 'Total expenditure', 
+        'Diphtheria', 'HIV/AIDS', 'GDP', 'Population', 'thinness  1-19 years', 
+        'thinness 5-9 years', 'Income composition of resources', 'Schooling',
+        'Country', 'Status', 'Year'
+    ]
+
+    for feature in expected_features:
+        if feature not in df.columns:
+            st.warning(f"Missing column '{feature}' - Creating with default values")
+            df[feature] = 0
+            df_display[feature] = 0
+
+    for column in df.columns:
+        if df[column].dtype == 'object':
+            fill_value = df[column].mode()[0]
+            df[column].fillna(fill_value, inplace=True)
+            df_display[column].fillna(fill_value, inplace=True)
+        else:
+            fill_value = df[column].median()
+            df[column].fillna(fill_value, inplace=True)
+            df_display[column].fillna(fill_value, inplace=True)
+
+    label_encoders = {}
+    categorical_cols = ['Country', 'Status']
+    for col in categorical_cols:
+        le = LabelEncoder()
+        df[col] = le.fit_transform(df[col].astype(str))
+        label_encoders[col] = le
+
+    X = df[expected_features]
+    y = df['Life expectancy']
+
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+
+    return df_display, X_scaled, y
+
+def main():
+    colored_title("Life Expectancy Estimation", "yellow")
+
+    df_display, X_scaled, y = load_and_preprocess_data()
+
+    colored_subheader("Dataset Preview", "yellow")
+    st.dataframe(df_display.head())
+
+    colored_subheader("Select a Row for Prediction:", "yellow")
+    colored_text("Select a Row Index", "red")
+    selected_row_index = st.selectbox("", options=range(len(df_display)), index=0, label_visibility="collapsed")
+
+    predict_button = st.button("Predict Life Expectancy")
+
+    if predict_button:
+        model = load_model()
+        if model is not None:
+            row_to_predict = X_scaled[selected_row_index].reshape(1, -1)
+            row_tensor = torch.tensor(row_to_predict, dtype=torch.float32)
+
+            try:
+                with torch.no_grad():
+                    prediction = model(row_tensor).item()
+                colored_subheader("Prediction Results:", "yellow")
+                colored_text(f"Predicted Life Expectancy: {prediction:.2f} years", "red")
+            except RuntimeError as e:
+                st.error(f"Prediction error: {str(e)}")
+                st.write("Input shape:", row_tensor.shape)
+                st.write("Expected shape: 1x21")
+
+            # Display the selected row with original categorical values
+            colored_subheader("Selected Row for Prediction:", "yellow")
+            st.write(df_display.iloc[selected_row_index])  
+            
+if __name__ == "__main__":
+    main()

life_expectancy_document.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7014b03b0a44d3dea99bb76e69b54cee285aab9c65db2c5cb462252f44a0646
+size 200352

model_weights.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee39a9e872a8063898ec8ebc23b1641a504754cc4d0c1ec7896c95b4c7ddc7f3
+size 16708

requirements (1).txt

@@ -0,0 +1,7 @@
+streamlit==1.25.0
+pandas==1.5.3
+numpy==1.24.3
+scikit-learn==1.2.2
+torch==2.0.1
+torchvision==0.15.2
+torchaudio==2.0.2