Create app.py

app.py

@@ -0,0 +1,146 @@
+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.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=0.5)
+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)
+
+# 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("ohe_drain.pkl", "rb") as f:
+    ohe_drain = pickle.load(f)
+
+with open("ohe_stage.pkl", "rb") as f:
+    ohe_drain = 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)
+
+stage_encoded = ohe.transform([[stage_fear]])[0][0]  # gender encoded using one hot encoding
+drain_encoded = ohe.transform([[drained]])[0][0]
+
+
+numeric_features = np.array([[time_spent, social_event, going_outside, friends, post_frequency]])
+scaled_features = rs.transform(numeric_features)
+
+
+final_input = np.concatenate(([gender_encoded], scaled_features[0])).reshape(1, -1)
+
+
+#input_data = np.array([[gender_encoded, age, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl, bmi]])
+
+
+
+
+prediction_labels = {
+    0: "Non-Diabetic",
+    1: "Diabetic",
+    2: "Pre-Diabetic"
+}
+
+# if st.button("Predict"):
+#     prediction = knn.predict(final_input)[0]
+#     result_label = prediction_labels.get(prediction, "Unknown")
+#     st.success(f"Predicted Result: {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.")
+
+
+
+
+
+# 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])
+    
+
+