Update app.py

app.py

@@ -1,32 +1,32 @@
-import streamlit as st
-import pickle
-import sklearn
-from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder
-from sklearn.neighbors import KNeighborsClassifier
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
+# import streamlit as st
+# import pickle
+# import sklearn
+# from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder
+# from sklearn.neighbors import KNeighborsClassifier
+# import pandas as pd
+# import numpy as np
+# import matplotlib.pyplot as plt
 
 
 
-# st.markdown("""
-#     <style>
-#         .stApp {
-#             background-image: url('https://huggingface.co/spaces/shubham680/DiabetesPrediction/resolve/main/bg.jpg');
-#             background-size: cover;
-#             background-repeat: no-repeat;
-#             background-attachment: fixed;
-#         }
-#         .stTitle {
-#             color: #ffffff;
-#             font-size: 36px;
-#             font-weight: bold;
-#             text-align: center;
-#         }
-#     </style>
-# """, unsafe_allow_html=True)
+# # st.markdown("""
+# #     <style>
+# #         .stApp {
+# #             background-image: url('https://huggingface.co/spaces/shubham680/DiabetesPrediction/resolve/main/bg.jpg');
+# #             background-size: cover;
+# #             background-repeat: no-repeat;
+# #             background-attachment: fixed;
+# #         }
+# #         .stTitle {
+# #             color: #ffffff;
+# #             font-size: 36px;
+# #             font-weight: bold;
+# #             text-align: center;
+# #         }
+# #     </style>
+# # """, unsafe_allow_html=True)
 
-st.title("Introvert/Extrovert Prediction App")
+# st.title("Introvert/Extrovert Prediction App")
 
 
 
@@ -36,118 +36,209 @@ st.title("Introvert/Extrovert Prediction App")
 
 
 
-#with st.sidebar:
-    #st.header("Patient Information")
-time_spent = st.number_input("Enter Time_spent_Alone:",min_value=0,max_value=11,step=1)
-stage_fear =  st.selectbox("Stage Fear:",["Yes","No"])
-social_event =  st.number_input("Enter Social Event Frequency:",min_value=0,max_value=10,step=1)
-going_outside = st.number_input("Enter Going Frequency:",min_value=0,max_value=7,step=1)
-drained =  st.selectbox("Drained After Socializing:",["Yes","No"])
-friends =  st.number_input("Enter Friend Circle Size:",min_value=0,max_value=15,step=1)
-post_frequency =  st.number_input("Enter Post Frequency:",min_value=0,max_value=10,step=1)
+# #with st.sidebar:
+#     #st.header("Patient Information")
+# time_spent = st.number_input("Enter Time_spent_Alone:",min_value=0,max_value=11,step=1)
+# stage_fear =  st.selectbox("Stage Fear:",["Yes","No"])
+# social_event =  st.number_input("Enter Social Event Frequency:",min_value=0,max_value=10,step=1)
+# going_outside = st.number_input("Enter Going Frequency:",min_value=0,max_value=7,step=1)
+# drained =  st.selectbox("Drained After Socializing:",["Yes","No"])
+# friends =  st.number_input("Enter Friend Circle Size:",min_value=0,max_value=15,step=1)
+# post_frequency =  st.number_input("Enter Post Frequency:",min_value=0,max_value=10,step=1)
 
 
 
-# def plot_health_metrics(inputs):
-#     labels = ["Age", "BMI", "Urea", "Creatinine", "HbA1c", "Cholesterol", "Triglycerides", "HDL", "LDL", "VLDL"]
-#     standard_ranges = [
-#         (20, 80), (18.5, 24.9), (2.3, 7.0), (0.6, 1.2), (4.0, 5.6), (125, 200), (40, 150), (40, 60), (50, 100), (5, 40)
-#     ]
-#     fig, ax = plt.subplots(figsize=(10, 6))
-#     ax.barh(labels, [inputs[i] for i in range(10)], color='skyblue')
-#     for i, (low, high) in enumerate(standard_ranges):
-#         ax.plot([low, high], [i, i], color='red', linewidth=2)
-#     ax.set_xlabel("Value")
-#     ax.set_title("Health Metrics vs Standard Ranges")
-#     st.pyplot(fig)
+# # def plot_health_metrics(inputs):
+# #     labels = ["Age", "BMI", "Urea", "Creatinine", "HbA1c", "Cholesterol", "Triglycerides", "HDL", "LDL", "VLDL"]
+# #     standard_ranges = [
+# #         (20, 80), (18.5, 24.9), (2.3, 7.0), (0.6, 1.2), (4.0, 5.6), (125, 200), (40, 150), (40, 60), (50, 100), (5, 40)
+# #     ]
+# #     fig, ax = plt.subplots(figsize=(10, 6))
+# #     ax.barh(labels, [inputs[i] for i in range(10)], color='skyblue')
+# #     for i, (low, high) in enumerate(standard_ranges):
+# #         ax.plot([low, high], [i, i], color='red', linewidth=2)
+# #     ax.set_xlabel("Value")
+# #     ax.set_title("Health Metrics vs Standard Ranges")
+# #     st.pyplot(fig)
 
-# plot_health_metrics([age, bmi, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl])
+# # plot_health_metrics([age, bmi, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl])
 
 
-with open("rs.pkl", "rb") as f:
-    rs = pickle.load(f)
+# with open("rs.pkl", "rb") as f:
+#     rs = pickle.load(f)
 
-with open("ohe_drain.pkl", "rb") as f:
-    ohe_drain = pickle.load(f)
+# with open("ohe_drain.pkl", "rb") as f:
+#     ohe_drain = pickle.load(f)
 
-with open("ohe_stage.pkl", "rb") as f:
-    ohe_stage = pickle.load(f)
+# with open("ohe_stage.pkl", "rb") as f:
+#     ohe_stage = pickle.load(f)
 
-with open("le.pkl", "rb") as f:
-    le = pickle.load(f)
+# with open("le.pkl", "rb") as f:
+#     le = pickle.load(f)
 
-with open("knn.pkl", "rb") as f:
-    knn = pickle.load(f)
+# with open("knn.pkl", "rb") as f:
+#     knn = pickle.load(f)
 
-stage_encoded = ohe_stage.transform([[stage_fear]])[0]  # gender encoded using one hot encoding
-drain_encoded = ohe_drain.transform([[drained]])[0]
+# stage_encoded = ohe_stage.transform([[stage_fear]])[0]  # gender encoded using one hot encoding
+# drain_encoded = ohe_drain.transform([[drained]])[0]
 
 
-numeric_features = np.array([[time_spent, social_event, going_outside, friends, post_frequency]])
-scaled_features = rs.transform(numeric_features)[0]
+# numeric_features = np.array([[time_spent, social_event, going_outside, friends, post_frequency]])
+# scaled_features = rs.transform(numeric_features)[0]
 
-st.write("Scaled Features:", scaled_features)
+# st.write("Scaled Features:", scaled_features)
 
 
-final_input = np.concatenate((
-    scaled_features[:1],        
-    stage_encoded,              
-    scaled_features[1:3],       
-    drain_encoded,               
-    scaled_features[3:]          
-)).reshape(1, -1)
+# final_input = np.concatenate((
+#     scaled_features[:1],        
+#     stage_encoded,              
+#     scaled_features[1:3],       
+#     drain_encoded,               
+#     scaled_features[3:]          
+# )).reshape(1, -1)
 
 
-#input_data = np.array([[gender_encoded, age, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl, bmi]])
+# #input_data = np.array([[gender_encoded, age, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl, bmi]])
 
 
 
 
-prediction_labels = {
-    0: "Extrovert",
-    1: "Introvert"
-}
-
-# if st.button("Predict"):
-#     prediction = knn.predict(final_input)[0]
-#     result_label = prediction_labels.get(prediction, "Unknown")
-    # st.success(f"Predicted Personality: {result_label}")
-    # if result_label == "Pre-Diabetic":
-    #     st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
-    # elif result_label == "Diabetic":
-    #     st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")
-
-
-
-if st.button("Predict"):
-    prediction = knn.predict(final_input)[0]
-    result_label = prediction_labels.get(prediction, "Unknown")
-    
-    # Custom result display
-    st.markdown(
-        f"""
-        <div style='background-color: #1f77b4; padding: 15px; border-radius: 10px;'>
-            <h4 style='color: white;'>Predicted Result: {result_label}</h4>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-
-    # # Message based on result
-    # if result_label == "Pre-Diabetic":
-    #     st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
-    # elif result_label == "Diabetic":
-    #     st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")
-
+# prediction_labels = {
+#     0: "Extrovert",
+#     1: "Introvert"
+# }
 
 
 
 
 # if st.button("Predict"):
 #     prediction = knn.predict(final_input)[0]
-#     result_label = prediction_labels.get(prediction,"Unknown")
-#     st.success(f"Predicted Result: {result_label}")
-#     #st.write("Predicted Diabetes Status :",prediction[0])
+#     result_label = prediction_labels.get(prediction, "Unknown")
     
+#     # Custom result display
+#     st.markdown(
+#         f"""
+#         <div style='background-color: #1f77b4; padding: 15px; border-radius: 10px;'>
+#             <h4 style='color: white;'>Predicted Result: {result_label}</h4>
+#         </div>
+#         """,
+#         unsafe_allow_html=True
+#     )
+
+ 
+
+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+
+# Inject custom CSS
+st.markdown("""
+    <style>
+        .stApp {
+            background-image: url('https://huggingface.co/spaces/shubham680/DiabetesPrediction/resolve/main/bg.jpg');
+            background-size: cover;
+            background-attachment: fixed;
+        }
+        .main-container {
+            background-color: rgba(255, 255, 255, 0.9);
+            padding: 2rem;
+            border-radius: 15px;
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            font-weight: bold;
+            padding: 0.5em 1em;
+            border-radius: 10px;
+        }
+        .prediction-box {
+            background-color: #1f77b4;
+            padding: 1em;
+            border-radius: 10px;
+            color: white;
+            font-size: 18px;
+            text-align: center;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Title
+st.markdown("<h1 style='text-align: center;'>Introvert vs Extrovert Predictor</h1>", unsafe_allow_html=True)
+
+with st.container():
+    st.markdown("<div class='main-container'>", unsafe_allow_html=True)
+
+    # Create input layout with columns
+    col1, col2 = st.columns(2)
+
+    with col1:
+        time_spent = st.number_input("🕒 Time Spent Alone", min_value=0, max_value=11, step=1)
+        social_event = st.number_input("🎉 Social Event Attendance", min_value=0, max_value=10, step=1)
+        going_outside = st.number_input("🚶‍♂️ Going Outside Frequency", min_value=0, max_value=7, step=1)
+
+    with col2:
+        stage_fear = st.selectbox("🎤 Stage Fear", ["Yes", "No"])
+        drained = st.selectbox("😓 Drained After Socializing", ["Yes", "No"])
+        friends = st.number_input("👥 Friend Circle Size", min_value=0, max_value=15, step=1)
+        post_frequency = st.number_input("📱 Post Frequency on Social Media", min_value=0, max_value=10, step=1)
+
+    # Load models and encoders
+    with open("rs.pkl", "rb") as f:
+        rs = pickle.load(f)
+
+    with open("ohe_drain.pkl", "rb") as f:
+        ohe_drain = pickle.load(f)
+
+    with open("ohe_stage.pkl", "rb") as f:
+        ohe_stage = pickle.load(f)
+
+    with open("le.pkl", "rb") as f:
+        le = pickle.load(f)
+
+    with open("knn.pkl", "rb") as f:
+        knn = pickle.load(f)
+
+    # Encode categorical values
+    stage_encoded = ohe_stage.transform([[stage_fear]])[0]  # shape (1,)
+    drain_encoded = ohe_drain.transform([[drained]])[0]
+
+    # Scale numeric input
+    numeric_features = np.array([[time_spent, social_event, going_outside, friends, post_frequency]])
+    scaled_features = rs.transform(numeric_features)[0]
+
+    # Debug: show scaled values
+    st.markdown("### 🧪 Scaled Feature Values")
+    feature_names = ["Time_spent_Alone", "Social_event", "Going_outside", "Friends", "Post_frequency"]
+    st.json({name: val for name, val in zip(feature_names, scaled_features)})
+
+    # Final input
+    final_input = np.concatenate((
+        scaled_features[:1],        # Time_spent_Alone
+        stage_encoded,              # Stage_fear (1 col)
+        scaled_features[1:3],       # Social_event, Going_outside
+        drain_encoded,              # Drained_after_socializing (1 col)
+        scaled_features[3:]         # Friends, Post_frequency
+    )).reshape(1, -1)
+
+    # Prediction labels
+    prediction_labels = {
+        0: "Extrovert",
+        1: "Introvert"
+    }
+
+    # Prediction trigger
+    if st.button("🔍 Predict"):
+        prediction = knn.predict(final_input)[0]
+        result_label = prediction_labels.get(prediction, "Unknown")
+
+        # Styled result box
+        st.markdown(
+            f"<div class='prediction-box'><strong>Predicted Personality:</strong> {result_label}</div>",
+            unsafe_allow_html=True
+        )
+
+    st.markdown("</div>", unsafe_allow_html=True)