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")
 
 
 
@@ -36,77 +36,77 @@
 
 
 
-# #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("🕒 Time Spent Alone",min_value=0,max_value=11,step=1)
+stage_fear =  st.selectbox("🎤 Stage Fear",["Yes","No"])
+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)
+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)
 
 
 
-# # 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"
-# }
+prediction_labels = {
+    0: "Extrovert",
+    1: "Introvert"
+}
 
 
 
@@ -125,120 +125,130 @@
 #         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)
+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
+    )
+
+
+
+
+# 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]
+
+  
+
+#     # 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)