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 LabelEncoder

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

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):
    # Create a copy of the DataFrame
    df_encoded = df.copy()
    
    # Convert Yes/No to 1/0 for binary columns
    binary_columns = ['Visited Parent', 'Visited College for Inquiry', 'Attended Any Event', 'College']
    for col in binary_columns:
        df_encoded[col] = df_encoded[col].map({'Yes': 1, 'No': 0}).astype(float)
    
    # Encode other categorical columns
    categorical_columns = ['Location', 'Course', 'Faculty', 'Source', 'Event', 'Presenter']
    label_encoder = LabelEncoder()
    for col in categorical_columns:
        df_encoded[col] = label_encoder.fit_transform(df_encoded[col]).astype(float)
    
    # Ensure numerical columns are float
    numerical_columns = ['College Fee', 'GPA', 'Year']
    for col in numerical_columns:
        df_encoded[col] = df_encoded[col].astype(float)
    
    return df_encoded

def predict_performance(Location, Course, College, Faculty, Source, Event, Presenter, Visited_Parent, Visited_College_for_Inquiry, Attended_Any_Event, College_Fee, GPA, Year):
    # Create initial DataFrame
    input_data = {
        'Location': [Location],
        'Course': [Course],
        'College': [College],
        'Faculty': [Faculty],
        'Source': [Source],
        'Event': [Event],
        'Presenter': [Presenter],
        'Visited Parent': [Visited_Parent],
        'Visited College for Inquiry': [Visited_College_for_Inquiry],
        'Attended Any Event': [Attended_Any_Event],
        'College Fee': [float(College_Fee)],
        'GPA': [float(GPA)],
        'Year': [float(Year)]
    }
    
    input_df = pd.DataFrame(input_data)
    
    print("\nInput DataFrame:")
    print(input_df)
    
    # Encode all categorical variables
    encoded_df = encode_categorical_columns(input_df)
    
    print("\nEncoded DataFrame:")
    print(encoded_df)
    
    # Normalize numerical features
    # College Fee normalization
    encoded_df['College Fee'] = (encoded_df['College Fee'] - 1000000) / (1700000 - 1000000)
    
    # Year normalization
    encoded_df['Year'] = (encoded_df['Year'] - 2019) / (2025 - 2019)
    
    # GPA normalization
    encoded_df['GPA'] = (encoded_df['GPA'] - 2.0) / (3.0 - 2.0)
    
    print("\nNormalized DataFrame:")
    print(encoded_df)
    
    # Make prediction
    prediction = model.predict(encoded_df.astype(float))[0]
    
    # Clip prediction between 0 and 1
    prediction = np.clip(prediction, 0, 1)
    
    print("\nPrediction:", 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(["Yes", "No"], label="College"),
        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(["Yes", "No"], label="Visited College for Inquiry"),
        gr.Radio(["Yes", "No"], label="Attended Any Event"),
        gr.Slider(minimum=1000000, maximum=1700000, step=1000, label="College Fee"),
        gr.Slider(minimum=2.0, maximum=3.0, step=0.1, label="GPA"),
        gr.Slider(minimum=2019, maximum=2025, step=1, label="Year")
    ],
    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)