Update app.py

app.py

@@ -1,42 +1,38 @@
 import streamlit as st
 import numpy as np
-import pickle
 import pandas as pd
+import pickle
 
-
+# Load model
 try:
     with open("final_model (3).pkl", "rb") as f:
         model = pickle.load(f)
     st.success("✅ Model loaded successfully!")
 except FileNotFoundError:
-    st.error("❌ Model file not found! Please upload `final_model.pkl`.")
+    st.error("❌ Model file not found! Please upload `final_model (3).pkl`.")
     model = None
 
-
+# Custom CSS for styling
 st.markdown(
     """
     <style>
-        body {
-            background-color: #121212;
-            color: #FFFFFF;
-        }
         .stApp {
             background-color: #121212;
             color: #FFFFFF;
         }
         .title {
             text-align: center;
-            font-size: 36px;
+            font-size: 38px;
             font-weight: bold;
             color: #BB86FC;
+            margin-top: 20px;
         }
         .stButton > button {
-            width: 100%;
             background-color: #6200EE;
             color: white;
             font-size: 18px;
             border-radius: 8px;
-            padding: 10px;
+            padding: 10px 20px;
             transition: 0.3s ease-in-out;
         }
         .stButton > button:hover {
@@ -45,17 +41,13 @@ st.markdown(
         }
         .result-box {
             text-align: center;
-            font-size: 20px;
+            font-size: 22px;
             font-weight: bold;
             color: white;
-            padding: 12px;
-            border-radius: 8px;
-            margin-top: 20px;
-            box-shadow: 0px 4px 8px rgba(255, 255, 255, 0.2);
-            transition: 0.3s ease-in-out;
-        }
-        .result-box:hover {
-            transform: scale(1.05);
+            padding: 16px;
+            border-radius: 10px;
+            margin-top: 30px;
+            box-shadow: 0px 4px 12px rgba(255, 255, 255, 0.3);
         }
         .high-risk {
             background-color: #D32F2F;
@@ -65,51 +57,64 @@ st.markdown(
         }
         .probability {
             background-color: #FFA726;
+            margin-top: 12px;
         }
     </style>
     """,
     unsafe_allow_html=True,
 )
 
+st.markdown("<h1 class='title'>🩺 PCOS Risk Predictor</h1>", unsafe_allow_html=True)
 
-st.markdown("<h1 class='title'> PCOS Risk Predictor</h1>", unsafe_allow_html=True)
-
-
-with st.expander("🔹 **Personal Information**", expanded=True):
-    Age = st.slider("Age", 18, 44, 30)
-
-with st.expander("🔹 **Medical Information**", expanded=True):
-    BMI = st.number_input("BMI", min_value=8, max_value=50, value=23)
-    Menstrual_Irregularity = st.selectbox("Menstrual_Irregularity", [0,1])
-    Testosterone_Level = st.number_input("Testosterone_Level(ng/dL)", min_value=20, max_value=135, value=53)
-    Antral_Follicle_Count = st.number_input("Antral_Follicle_Count", min_value=3, max_value=39, value=8)
+# Collect user inputs
+with st.expander("📄 Personal Information", expanded=True):
+    Age = st.slider("🎂 Age", 18, 44, 30)
 
+with st.expander("🏥 Medical Information", expanded=True):
+    BMI = st.number_input("⚖️ BMI", min_value=8, max_value=50, value=23)
+    Menstrual_Irregularity = st.selectbox("🩸 Menstrual Irregularity (0 = No, 1 = Yes)", [0, 1])
+    Testosterone_Level = st.number_input("🧬 Testosterone Level (ng/dL)", min_value=20, max_value=135, value=53)
+    Antral_Follicle_Count = st.number_input("🧪 Antral Follicle Count", min_value=3, max_value=39, value=8)
 
+# Predict
 if st.button("🔍 Predict Risk"):
-    try:
-        input_df = pd.DataFrame([[Age,BMI,Menstrual_Irregularity, Testosterone_Level,Antral_Follicle_Count]], columns=['Age','BMI','Menstrual_Irregularity','Testosterone_Level(ng/dL)','Antral_Follicle_Count'])
-        prediction = model.predict(input_df)[0]  
-        probability = model.predict_proba(input_df)[0][1] * 100  
+    if model is None:
+        st.error("⚠️ Model is not loaded. Please ensure the model file exists.")
+    else:
+        try:
+            input_df = pd.DataFrame([[
+                Age,
+                BMI,
+                Menstrual_Irregularity,
+                Testosterone_Level,
+                Antral_Follicle_Count
+            ]], columns=[
+                'Age',
+                'BMI',
+                'Menstrual_Irregularity',
+                'Testosterone Level (ng/dL)',
+                'Antral_Follicle_Count'
+            ])
+
+            prediction = model.predict(input_df)[0]
+            probability = model.predict_proba(input_df)[0][1] * 100
 
-        # Determine Risk Category
-        if prediction == 1:
-            result_text = "❌⚠️ High Risk of PCOS Disease!"
-            result_class = "high-risk"
-        else:
-            result_text = "✅ Low Risk of PCOS Disease!"
-            result_class = "low-risk"
+            if prediction == 1:
+                result_text = "⚠️❌ High Risk of PCOS Detected!"
+                result_class = "high-risk"
+            else:
+                result_text = "✅ Low Risk of PCOS — You're Good!"
+                result_class = "low-risk"
 
-        # Display Result
-        st.markdown(
-            f"<div class='result-box {result_class}'> {result_text} </div>",
-            unsafe_allow_html=True,
-        )
+            st.markdown(
+                f"<div class='result-box {result_class}'>{result_text}</div>",
+                unsafe_allow_html=True
+            )
 
-        # Display Probability Score
-        st.markdown(
-            f"<div class='result-box probability'> 📊 Risk Probability: {probability:.2f}% </div>",
-            unsafe_allow_html=True,
-        )
+            st.markdown(
+                f"<div class='result-box probability'>📊 Estimated Probability: {probability:.2f}%</div>",
+                unsafe_allow_html=True
+            )
 
-    except Exception as e:
-        st.error(f"⚠️ Prediction failed: {e}") 
+        except Exception as e:
+            st.error(f"🚫 Prediction failed: {e}")