import pickle
from ipywidgets import FileUpload, Button, Output, VBox
from IPython.display import display
from utils.parser import extract_text_from_pdf, extract_text_from_docx

# Constants for file paths
VECTORIZER_PATH = r'F:\New folder (2)\resume-analyzer\models\vectorizer.pkl'
MODEL_PATH = r'F:\New folder (2)\resume-analyzer\models\model.pkl'

# Load model and vectorizer
def load_pickle(file_path):
    with open(file_path, 'rb') as file:
        return pickle.load(file)

vectorizer = load_pickle(VECTORIZER_PATH)
model = load_pickle(MODEL_PATH)

# Function to predict resume category
def predict_resume_category(text):
    vectorized_text = vectorizer.transform([text])
    return model.predict(vectorized_text)[0]

# Function to handle file upload and prediction
def handle_file_upload(change):
    uploaded_file = list(file_upload.value.values())[0]
    file_name = uploaded_file['metadata']['name']
    file_content = uploaded_file['content']

    # Extract text based on file type
    if file_name.endswith(".pdf"):
        text = extract_text_from_pdf(file_content)
    elif file_name.endswith(".docx"):
        text = extract_text_from_docx(file_content)
    else:
        output.clear_output()
        with output:
            print("❌ Unsupported file format.")
        return

    if not text or not text.strip():
        output.clear_output()
        with output:
            print("❌ No text extracted from the resume.")
        return

    # Predict and display result
    category = predict_resume_category(text)
    output.clear_output()
    with output:
        print(f"✅ Predicted Resume Category: {category}")

# Widgets
file_upload = FileUpload(accept='.pdf,.docx', multiple=False)
file_upload.observe(handle_file_upload, names='value')

output = Output()

# Display widgets
display(VBox([file_upload, output]))