Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from catboost import CatBoostRegressor
+
+# Load the saved CatBoost model.
+model = CatBoostRegressor()
+model.load_model("PlaceMe_CatBoost.cbm", format="cbm")
+
+st.title("Salary Prediction App")
+
+# 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: 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'