import streamlit as st
import pickle
import sklearn
import pandas as pd

# Load trained model, encoders, and feature column order
with open("best_model.pkl", "rb") as f:
    model = pickle.load(f)

with open("encoders.pkl", "rb") as f:
    encoders = pickle.load(f)

with open("columns.pkl", "rb") as f:
    feature_columns = pickle.load(f)

st.title("🧠 Autism Prediction System")

st.markdown("### 📝 Please fill the following details:")

# A1 to A10 - Screening Scores
user_inputs = {
    "A1_Score": st.selectbox("A1 Score (Social interaction)", [0, 1]),
    "A2_Score": st.selectbox("A2 Score (Eye contact)", [0, 1]),
    "A3_Score": st.selectbox("A3 Score (Responds to name)", [0, 1]),
    "A4_Score": st.selectbox("A4 Score (Repetitive behavior)", [0, 1]),
    "A5_Score": st.selectbox("A5 Score (Preference for routine)", [0, 1]),
    "A6_Score": st.selectbox("A6 Score (Sensitive to sounds, lights, etc.)", [0, 1]),
    "A7_Score": st.selectbox("A7 Score (Speech or communication delay)", [0, 1]),
    "A8_Score": st.selectbox("A8 Score (Struggles recognizing emotions)", [0, 1]),
    "A9_Score": st.selectbox("A9 Score (Unusual interests or fixations)", [0, 1]),
    "A10_Score": st.selectbox("A10 Score (Strong attention to detail)", [0, 1]),

    # Demographic and medical history
    "age": st.number_input("Age (in years)", min_value=1, max_value=120),
    "gender": st.selectbox("Gender (Biological sex)", encoders["gender"].classes_),
    "ethnicity": st.selectbox("Ethnicity (Cultural background)", encoders["ethnicity"].classes_),
    "jaundice": st.selectbox("Jaundice at Birth (Medical history)", encoders["jaundice"].classes_),
    "contry_of_res": st.selectbox("Country of Residence (Current location)", encoders["contry_of_res"].classes_),
    "austim": st.selectbox("Family history of Autism?", encoders["austim"].classes_),
    "used_app_before": st.selectbox("Used Screening App Before?", encoders["used_app_before"].classes_),
    "result": st.number_input("Screening Score Result (Total score from A1-A10)", min_value=0, max_value=20),
    "relation": st.selectbox("Relation to Patient (Who is filling this form?)", encoders["relation"].classes_),
}

if st.button("🔍 Predict"):
    try:
        # Convert to DataFrame
        input_df = pd.DataFrame([user_inputs])

        # Encode categorical columns
        for col in encoders:
            input_df[col] = encoders[col].transform(input_df[col])

        # Reorder columns as used in training
        input_df = input_df[feature_columns]

        # Predict
        prediction = model.predict(input_df)[0]
        result = "🧠 ASD Positive" if prediction == 1 else "✅ ASD Negative"
        st.success(f"Prediction: {result}")

    except Exception as e:
        st.error(f"⚠️ Error during prediction: {e}")