| |
| import os |
| import joblib |
| import pandas as pd |
| import streamlit as st |
|
|
| |
| BASE_PATH = "src" |
| ADMISSION_CSV = os.path.join(BASE_PATH, "admission_data.csv") |
| VISA_CSV = os.path.join(BASE_PATH, "visa_data.csv") |
|
|
| ADMISSION_MODEL_FILE = os.path.join(BASE_PATH, "admission_model_rf.pkl") |
| VISA_MODEL_FILE = os.path.join(BASE_PATH, "visa_model_tuned_gb.pkl") |
|
|
| |
| @st.cache_data(show_spinner=False) |
| def load_csv_safe(path): |
| if os.path.exists(path): |
| try: |
| return pd.read_csv(path) |
| except Exception as e: |
| st.warning(f"Could not read {path}: {e}") |
| return None |
| return None |
|
|
| @st.cache_resource(show_spinner=False) |
| def load_models_safe(): |
| admission_model = visa_model = None |
| if os.path.exists(ADMISSION_MODEL_FILE): |
| try: |
| admission_model = joblib.load(ADMISSION_MODEL_FILE) |
| except Exception as e: |
| st.warning(f"Failed to load admission model: {e}") |
| if os.path.exists(VISA_MODEL_FILE): |
| try: |
| visa_model = joblib.load(VISA_MODEL_FILE) |
| except Exception as e: |
| st.warning(f"Failed to load visa model: {e}") |
| return {"admission_model": admission_model, "visa_model": visa_model} |
|
|
| def prepare_and_predict(model, input_df): |
| """Runs prediction only (no probability)""" |
| try: |
| pred = model.predict(input_df) |
| pred_label = pred[0] if hasattr(pred, "__len__") else pred |
| except Exception as e: |
| st.error(f"Model prediction failed: {e}") |
| return None |
| return pred_label |
|
|
| |
| models = load_models_safe() |
| admission_df = load_csv_safe(ADMISSION_CSV) |
| visa_df = load_csv_safe(VISA_CSV) |
|
|
| |
| st.set_page_config(page_title="Admission & Visa Predictor", page_icon="🎓", layout="centered") |
| st.title("✨ Admission & Visa Predictor – Plan Your Future") |
|
|
| |
| st.subheader("Admission — Step 1") |
| gre_score = st.number_input("GRE Score", min_value=0, max_value=340, value=300) |
| toefl_score = st.number_input("TOEFL Score", min_value=0, max_value=120, value=100) |
| university_rating = st.selectbox("University Rating (1-5)", [1, 2, 3, 4, 5], index=2) |
| sop = st.slider("SOP Strength (1.0 - 5.0)", 1.0, 5.0, 3.0, step=0.5) |
| lor = st.slider("LOR Strength (1.0 - 5.0)", 1.0, 5.0, 3.0, step=0.5) |
| cgpa = st.number_input("CGPA (0.0 - 10.0)", min_value=0.0, max_value=10.0, value=8.0, format="%.2f") |
| research = st.selectbox("Research (0 = No, 1 = Yes)", [0, 1], index=0) |
|
|
| admission_input_df = pd.DataFrame([{ |
| "gre_score": gre_score, |
| "toefl_score": toefl_score, |
| "university_rating": university_rating, |
| "sop": sop, |
| "lor": lor, |
| "cgpa": cgpa, |
| "research": research |
| }]) |
|
|
| |
| if st.button("Check Admission", key="admission_btn"): |
| |
| st.session_state.pop("admitted", None) |
| st.session_state.pop("visa_ok", None) |
|
|
| if models["admission_model"] is None: |
| st.error("Admission model not found in `src/`.") |
| else: |
| admitted = prepare_and_predict(models["admission_model"], admission_input_df) |
| if admitted is None: |
| st.error("Could not predict admission result.") |
| else: |
| st.session_state["admitted"] = bool(admitted) |
|
|
| if not admitted: |
| st.error("Admission result: Not admitted. You cannot proceed to visa check.") |
| else: |
| st.success("Admission result: Admitted! Please scroll down to Visa check.") |
|
|
| |
| if st.session_state.get("admitted", False): |
| st.subheader("Visa — Step 2") |
|
|
| interview_location = st.selectbox( |
| "Interview Location", |
| visa_df["Interview Location"].dropna().unique().tolist() if visa_df is not None else ["Islamabad", "Karachi", "Other"] |
| ) |
| program = st.selectbox( |
| "Program", |
| visa_df["Program"].dropna().unique().tolist() if visa_df is not None else ["Masters", "PhD", "MBA", "Other"] |
| ) |
| degree_level = st.selectbox( |
| "Degree Level", |
| visa_df["Degree Level"].dropna().unique().tolist() if visa_df is not None else ["Bachelors", "Masters", "PhD"] |
| ) |
| university = st.selectbox( |
| "University", |
| visa_df["University"].dropna().unique().tolist() if visa_df is not None else ["Harvard", "MIT", "Other"] |
| ) |
| universities_applied = st.number_input("Number of Universities Applied", min_value=1, value=3) |
| sponsor = st.selectbox( |
| "Sponsor", |
| visa_df["Sponsor"].dropna().unique().tolist() if visa_df is not None else ["Self", "Parents", "Scholarship", "Other"] |
| ) |
| scholarship = st.selectbox( |
| "Scholarship", |
| visa_df["Scholarship"].dropna().unique().tolist() if visa_df is not None else ["Yes", "No"] |
| ) |
| relatives_us = st.selectbox( |
| "Relatives in US", |
| visa_df["Relatives in US"].dropna().unique().tolist() if visa_df is not None else ["Yes", "No"] |
| ) |
| visa_attempts = st.selectbox( |
| "Visa Attempts", |
| visa_df["Visa Attempts"].dropna().unique().tolist() if visa_df is not None else ["First", "Second", "More"] |
| ) |
|
|
| visa_input_df = pd.DataFrame([{ |
| "Interview Location": interview_location, |
| "Program": program, |
| "Degree Level": degree_level, |
| "University": university, |
| "Universities Applied": universities_applied, |
| "Sponsor": sponsor, |
| "Scholarship": scholarship, |
| "Relatives in US": relatives_us, |
| "Visa Attempts": visa_attempts |
| }]) |
|
|
| |
| if st.button("Check Visa Approval", key="visa_btn"): |
| |
| st.session_state.pop("visa_ok", None) |
|
|
| if models["visa_model"] is None: |
| st.error("Visa model not found in `src/`.") |
| else: |
| visa_ok = prepare_and_predict(models["visa_model"], visa_input_df) |
| if visa_ok is None: |
| st.error("Could not predict visa approval result.") |
| else: |
| st.session_state["visa_ok"] = bool(visa_ok) |
|
|
| |
| if "visa_ok" in st.session_state: |
| visa_ok = st.session_state["visa_ok"] |
| if visa_ok: |
| st.balloons() |
| st.success("🎉 Final Result: Admission + Visa likely approved.") |
| else: |
| st.warning("⚠️ Final Result: Admission accepted, but visa approval looks unlikely.") |
|
|
