add app

src/app.py

@@ -0,0 +1,138 @@
+import streamlit as st
+import torch
+import torch.nn as nn
+import numpy as np
+import pickle
+import json
+from PIL import Image
+
+st.set_page_config(layout="wide")
+
+st.markdown("""
+    <style>
+    body, html, .css-18e3th9 {
+        font-size: 20px !important;
+    }
+    h1 {
+        font-size: 3rem !important;
+    }
+    h2 {
+        font-size: 2.5rem !important;
+    }
+    label, .stSlider label, .stSelectbox label, .stButton button, .stTextInput label {
+        font-size: 22px !important;
+    }
+    div[data-baseweb="select"] > div {
+        font-size: 20px !important;
+    }
+    .stNumberInput input {
+        font-size: 20px !important;
+    }
+    div[data-testid="stImage"] > figcaption {
+        font-size: 1.2rem !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+
+with open('embeddings.pkl', 'rb') as f:
+    embeddings = pickle.load(f)
+
+with open('label_mappings.json', 'r') as f:
+    label_mappings = json.load(f)
+
+
+class MLPRegressor(nn.Module):
+    def __init__(self, input_dim, hidden_dims=[512, 128], dropout=0.2):
+        super().__init__()
+        layers = []
+        prev = input_dim
+        for h in hidden_dims:
+            layers += [
+                nn.Linear(prev, h),
+                nn.ReLU(),
+                nn.Dropout(dropout)
+            ]
+            prev = h
+        layers.append(nn.Linear(prev, 1))
+        layers.append(nn.Tanh())  # output in [-1, 1]
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.net(x)
+
+
+num_text_features = 3 * 384
+num_numeric_features = 1
+num_categorical_features = 4
+input_dim = num_text_features + num_numeric_features + num_categorical_features
+
+model = MLPRegressor(input_dim=input_dim)
+model.load_state_dict(torch.load('mlp_model.pth', map_location='cpu'))
+model.eval()
+
+
+st.title("Investor Risk Score Estimator")
+st.markdown("---")
+st.subheader("Select 3 images in order of your preference:")
+
+image_ids = list(range(1, 17))
+cols = st.columns(4)
+selection = {}
+
+for i, img_id in enumerate(image_ids):
+    col = cols[i % 4]
+    image_path = f"../images/{img_id}.png"
+    image = Image.open(image_path)
+    col.image(image, caption=f"ID {img_id}", use_container_width=True)
+    selection[img_id] = col.checkbox(f"Select ID {img_id}")
+
+selected = [img_id for img_id, chosen in selection.items() if chosen]
+
+if len(selected) < 3:
+    st.info(f"Please select {3 - len(selected)} more image(s).")
+    st.stop()
+elif len(selected) > 3:
+    st.error("You have selected more than 3 images. Please select exactly 3.")
+    st.stop()
+else:
+    st.success("✅ 3 images selected. You can proceed!")
+
+st.subheader("Set preference order for selected images:")
+pref_1 = st.selectbox("Preference 1", selected, key="p1")
+remaining_2 = [x for x in selected if x != pref_1]
+pref_2 = st.selectbox("Preference 2", remaining_2, key="p2")
+pref_3 = [x for x in remaining_2 if x != pref_2][0]
+st.write(f"Preference 3: {pref_3}")
+
+st.header("Sociodemographic Information:")
+
+age = st.slider("Age", 18, 100, 30)
+
+gender = st.selectbox("Gender", list(label_mappings['gender'].keys()))
+education = st.selectbox("Education", list(label_mappings['education'].keys()))
+income = st.selectbox("Income", list(label_mappings['income'].keys()))
+marital_status = st.selectbox("Marital Status", list(label_mappings['marital_status'].keys()))
+
+emb1 = embeddings[pref_1]
+emb2 = embeddings[pref_2]
+emb3 = embeddings[pref_3]
+
+emb_features = np.hstack([emb1, emb2, emb3])
+
+numeric_features = np.array([age])
+
+categorical_features = np.array([
+    label_mappings['gender'][gender],
+    label_mappings['education'][education],
+    label_mappings['income'][income],
+    label_mappings['marital_status'][marital_status]
+])
+
+final_input = np.hstack([emb_features, numeric_features, categorical_features]).astype(np.float32)
+final_tensor = torch.tensor(final_input).unsqueeze(0)
+
+if st.button("Calculate Risk Score"):
+    with torch.no_grad():
+        prediction = model(final_tensor).item()
+    st.success(f"Predicted Risk Score: {prediction:.3f}")

src/embeddings.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29cf048353616c559c496fb870ab8d8d298e46e45f1cf8c2a300af666cb31e42
+size 26846

src/label_mappings.json

@@ -0,0 +1 @@
+{"gender": {"female": 0, "male": 1, "other": 2}, "education": {"PhD": 0, "bachelor": 1, "high school": 2, "master": 3}, "income": {"high": 0, "low": 1, "medium": 2}, "marital_status": {"divorced": 0, "married": 1, "single": 2, "widowed": 3}}

src/mlp_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cbca730a7ae9e0ef71bab0eb968efe2ca81eeebd8b8bfeb0d49c1a981226066
+size 2637933