Update app.py

app.py

@@ -1,77 +1,114 @@
 import streamlit as st
 import pandas as pd
+import sqlite3
 from catboost import CatBoostRegressor
 
-# Load the saved CatBoost model.
-model = CatBoostRegressor()
-model.load_model("PlaceMe_CatBoost.cbm", format="cbm")
+def predict_mode():
+    st.title("📊 Predict Mode: Salary Prediction")
+    
+    # Input fields
+    gender = st.selectbox("Gender", ["m", "f"])
+    perc_10 = st.number_input("10th Percentage", min_value=0.0, max_value=100.0, value=80.0)
+    perc_12 = st.number_input("12th Percentage", min_value=0.0, max_value=100.0, value=80.0)
+    
+    tier = st.selectbox("College Tier (Only 1 or 2 accepted)", [1, 2])
+    
+    specializations = [
+        'electronics & instrumentation',
+        'computer science',
+        'electronics & communication',
+        'biotechnology',
+        'mechanical',
+        'information technology',
+        'electrical engineering',
+        'electronics and electrical engineering',
+        'information science engineering',
+        'chemical engineering',
+        'ceramic engineering',
+        'metallurgical engineering',
+        'aeronautical engineering',
+        'electronics engineering',
+        'civil engineering',
+        'industrial & production engineering',
+        'other',
+        'electronics and computer engineering',
+        'industrial & management engineering',
+        'biomedical engineering',
+        'computer application',
+        'electrical and power engineering',
+        'industrial engineering',
+        'mechatronics',
+        'information & communication technology'
+    ]
+    specialization = st.selectbox("Specialization", specializations)
+    
+    cgpa_input = st.number_input("College GPA (0-10 scale)", min_value=0.0, max_value=10.0, value=7.5)
+    college_gpa = cgpa_input * 10  # Scale up to 100
+    
+    locations = [
+        "Delhi", "Uttar Pradesh", "Maharashtra", "Tamil Nadu", "West Bengal", "Telangana",
+        "Andhra Pradesh", "Haryana", "Karnataka", "Orissa", "Chhattisgarh", "Rajasthan", 
+        "Punjab", "Madhya Pradesh", "Uttarakhand", "Gujarat", "Jharkhand", "Himachal Pradesh", 
+        "Bihar", "Kerala", "Assam", "Jammu and Kashmir", "Sikkim", "Meghalaya", "Goa"
+    ]
+    location = st.selectbox("Location", locations)
+    
+    # Prepare input DataFrame for prediction
+    input_df = pd.DataFrame({
+        "Gender": [gender],
+        "10percentage": [perc_10],
+        "12percentage": [perc_12],
+        "CollegeTier": [tier],
+        "Specialization": [specialization],
+        "collegeGPA": [college_gpa],
+        "CollegeState": [location]
+    })
+    
+    if st.button("Predict Salary"):
+        model = CatBoostRegressor()
+        model.load_model("PlaceMe_CatBoost.cbm", format="cbm")
+        prediction = model.predict(input_df)
+        st.success(f"Predicted Salary: {prediction[0]:.2f}")
 
-st.title("Salary Prediction App")
+def db_mode():
+    st.title("🗄️ DB Mode: Database Viewer")
+    
+    # Connect to SQLite database.
+    conn = sqlite3.connect("PlaceMeNot.db")
+    
+    # Get table names from the database
+    table_query = "SELECT name FROM sqlite_master WHERE type='table';"
+    tables = pd.read_sql(table_query, conn)
+    
+    if tables.empty:
+        st.error("No tables found in the database.")
+        return
+    else:
+        # Assuming we use the first available table.
+        table_name = tables.iloc[0, 0]
+        st.write(f"Using table: **{table_name}**")
+    
+    # Allow user to sort data
+    cols = ["Gender", "10percentage", "12percentage", "CollegeTier", 
+            "Specialization", "collegeGPA", "CollegeState", "Salary"]
+    sort_col = st.selectbox("Sort by", cols)
+    order = st.radio("Order", ("Ascending", "Descending"))
+    order_str = "ASC" if order == "Ascending" else "DESC"
+    
+    query = f"SELECT * FROM {table_name} ORDER BY {sort_col} {order_str}"
+    df = pd.read_sql(query, conn)
+    st.dataframe(df)
+    
+    conn.close()
 
-# Input fields
-gender = st.selectbox("Gender", ["m", "f"])
-perc_10 = st.number_input("10th Percentage", min_value=0.0, max_value=100.0, value=80.0)
-perc_12 = st.number_input("12th Percentage", min_value=0.0, max_value=100.0, value=80.0)
+def main():
+    st.sidebar.title("Mode Selection")
+    mode = st.sidebar.radio("Choose Mode", ["Predict Mode", "DB Mode"])
+    
+    if mode == "Predict Mode":
+        predict_mode()
+    else:
+        db_mode()
 
-# Tier: only 1 and 2 accepted.
-tier = st.selectbox("College Tier", [1, 2])
-
-# Specialization: restricted choices.
-specializations = [
-    'electronics & instrumentation',
-    'computer science',
-    'electronics & communication',
-    'biotechnology',
-    'mechanical',
-    'information technology',
-    'electrical engineering',
-    'electronics and electrical engineering',
-    'information science engineering',
-    'chemical engineering',
-    'ceramic engineering',
-    'metallurgical engineering',
-    'aeronautical engineering',
-    'electronics engineering',
-    'civil engineering',
-    'industrial & production engineering',
-    'other',
-    'electronics and computer engineering',
-    'industrial & management engineering',
-    'biomedical engineering',
-    'computer application',
-    'electrical and power engineering',
-    'industrial engineering',
-    'mechatronics',
-    'information & communication technology'
-]
-specialization = st.selectbox("Specialization", specializations)
-
-# CGPA: user inputs on 0-10 scale, then scale up to 100.
-cgpa_input = st.number_input("College GPA (0-10 scale)", min_value=0.0, max_value=10.0, value=7.5)
-college_gpa = cgpa_input * 10
-
-# Location: accepted values.
-locations = [
-    "Delhi", "Uttar Pradesh", "Maharashtra", "Tamil Nadu", "West Bengal", "Telangana",
-    "Andhra Pradesh", "Haryana", "Karnataka", "Orissa", "Chhattisgarh", "Rajasthan", 
-    "Punjab", "Madhya Pradesh", "Uttarakhand", "Gujarat", "Jharkhand", "Himachal Pradesh", 
-    "Bihar", "Kerala", "Assam", "Jammu and Kashmir", "Sikkim", "Meghalaya", "Goa"
-]
-location = st.selectbox("Location", locations)
-
-# Build the input DataFrame with columns expected by the model.
-input_df = pd.DataFrame({
-    "Gender": [gender],
-    "10percentage": [perc_10],
-    "12percentage": [perc_12],
-    "CollegeTier": [tier],
-    "Specialization": [specialization],
-    "collegeGPA": [college_gpa],
-    "CollegeState": [location]  # mapping 'Location' to model's 'CollegeState'
-})
-
-if st.button("Predict Salary"):
-    prediction = model.predict(input_df)
-    st.success(f"Predicted Salary: {prediction[0]:.2f}")
-
-# To run, save this file (e.g., app.py) and execute 'streamlit run app.py'
+if __name__ == '__main__':
+    main()