import gradio as gr
import joblib
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder

# Load the trained model and scaler objects from file
REPO_ID = "Hemg/modelxxx"  # hugging face repo ID
MODEL_FILENAME = "predjob.joblib"  # model file name
SCALER_FILENAME = "scalejob.joblib"  # scaler file name

model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME))
scaler = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=SCALER_FILENAME))

def encode_categorical_columns(df):
    label_encoder = LabelEncoder()
    
    # Identify categorical columns
    ordinal_columns = df.select_dtypes(include=['object']).columns
    
    # Encode ordinal columns using LabelEncoder
    for col in ordinal_columns:
        df[col] = label_encoder.fit_transform(df[col])

    # Get nominal columns for one-hot encoding
    nominal_columns = df.select_dtypes(include=['object']).columns.difference(ordinal_columns)
    
    # Apply one-hot encoding to nominal columns (drop the first column to avoid multicollinearity)
    df = pd.get_dummies(df, columns=nominal_columns, drop_first=True)

    return df
def predict_performance(Location, Course,Faculty, College,Source, Event, Presenter, Visited_Parent, 
                       Visited_College_for_Inquiry, Attended_Any_Event, College_Fee, GPA, Year):
    input_data = [Location, Course, Faculty, Source, Event, Presenter, Visited_Parent,
                  Visited_College_for_Inquiry, Attended_Any_Event, College_Fee, GPA, Year]

    # Updated feature names to use spaces instead of underscores to match training data
    feature_names = [
        "Location", "Course", "Faculty", "College","Source", "Event", "Presenter", 
        "Visited Parent", "Visited College for Inquiry", "Attended Any Event", 
        "College Fee", "GPA", "Year"
    ]
    
    input_df = pd.DataFrame([input_data], columns=feature_names)

    
    # Debug print 2: Show DataFrame before encoding
    print("\nDataFrame before encoding:")
    print(input_df)
    
    # Encode categorical columns
    df = encode_categorical_columns(input_df)
    
    # Debug print 3: Show DataFrame after encoding
    print("\nDataFrame after encoding:")
    print(df)
    
    # Scale input data using the loaded scaler
    scaled_input = scaler.transform(df)
    
    # Make the prediction
    prediction = model.predict(scaled_input)[0]
    
    # Clip the prediction to be between 0 and 1
    prediction = np.clip(prediction, 0, 1)
    
    
    # Debug print
    print("\nPrediction details:")
    print(f"Raw prediction: {prediction}")
   
    
    return f"Chance of Admission: {prediction:.1f}"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_performance,
    inputs=[
        gr.Radio(["Kathmandu", "Bhaktapur", "Lalitpur", "Kritipur"], label="Location"),
        gr.Radio(["MSc IT & Applied Security", "BSc (Hons) Computing", 
                 "BSc (Hons) Computing with Artificial Intelligence", 
                 "BSc (Hons) Computer Networking & IT Security", 
                 "BSc (Hons) Multimedia Technologies", "MBA", 
                 "BA (Hons) Accounting & Finance", 
                 "BA (Hons) Business Administration"], label="Course"),
        gr.Radio(["Science", "Management", "Humanities"], label="Faculty"),
        gr.Radio(["Event", "Facebook", "Instagram", "Offline", "Recommendation"], label="Source"),
        gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", 
                 "Holi Festival", "Welcome Ceremony"], label="Event"),
        gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], 
                label="Presenter"),
        gr.Radio(["Yes", "No"], label="Visited Parent"),
        gr.Radio(["Trinity", "CCRC", "KMC", "SOS", "ISMT", "St. Xavier's", "Everest", "Prime"], label="College")
        gr.Radio(["Yes", "No"], label="Visited College for Inquiry"),
        gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"], 
                 label="Event"),
        gr.Radio(["Yes", "No"], label="Attended Any Event"),
        gr.Radio(["Yes", "No"], label="Visited Parent")
    ],
    outputs="text",
    title="Chance of Student Admission",
    description="Predict the chances of a student's admission based on various inputs."
)

# Run the app
if __name__ == "__main__":
    iface.launch(share=True)