import streamlit as st
import pickle
import re

#loading models
clf = pickle.load(open('clf.pkl','rb'))
tfidf = pickle.load(open('tfidf.pkl','rb'))

def clean_resume(resume_text):
    """
    Clean the text in the resume i.e. remove unwanted chars in the text. For e.g. 
    1 URLs,
    2 Hashtags,
    3 Mentions,
    4 Special Chars,
    5 Punctuations

    Parameters:
        resume_text (str): The input resume text to be cleaned.

    Returns:
        clean_text (str): Clean Resume.
    """
    clean_text = re.sub('http\S+\s*', ' ', resume_text)
    clean_text = re.sub('RT|cc', ' ', clean_text)
    clean_text = re.sub('#\S+', '', clean_text)
    clean_text = re.sub('@\S+', '  ', clean_text)
    clean_text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', clean_text)
    clean_text = re.sub(r'[^\x00-\x7f]', r' ', clean_text)
    clean_text = re.sub('\s+', ' ', clean_text)
    return clean_text

def remove_stopwords(text, language='english'):
    """
    Remove stopwords from a given text.

    Parameters:
        text (str): The input text from which to remove stopwords.
        language (str): The language of the stopwords. Default is 'english'.

    Returns:
        filtered_text (str): Text without stopwords.
    """
    stop_words = set([
    "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", 
    "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", 
    "her", "hers", "herself", "it", "its", "itself", "they", "them", "their", 
    "theirs", "themselves", "what", "which", "who", "whom", "this", "that", 
    "these", "those", "am", "is", "are", "was", "were", "be", "been", "being", 
    "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", 
    "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", 
    "at", "by", "for", "with", "about", "against", "between", "into", "through", 
    "during", "before", "after", "above", "below", "to", "from", "up", "down", 
    "in", "out", "on", "off", "over", "under", "again", "further", "then", 
    "once", "here", "there", "when", "where", "why", "how", "all", "any", 
    "both", "each", "few", "more", "most", "other", "some", "such", "no", 
    "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", 
    "t", "can", "will", "just", "don", "should", "now"
    ])
    words = text.split()  
    filtered_words = [word for word in words if word.lower() not in stop_words]
    filtered_text = ' '.join(filtered_words)
    return filtered_text
    
# web app
def main():
    st.title("Resume Screening App")
    uploaded_file = st.file_uploader('Upload Resume', type=['txt','pdf'])

    if uploaded_file is not None:
        try:
            resume_bytes = uploaded_file.read()
            resume_text = resume_bytes.decode('utf-8')
        except UnicodeDecodeError:
            # If UTF-8 decoding fails, try decoding with 'latin-1'
            resume_text = resume_bytes.decode('latin-1')

        cleaned_resume = clean_resume(resume_text)
        cleaned_resume = remove_stopwords(cleaned_resume)
        input_features = tfidf.transform([cleaned_resume])
        prediction_id = clf.predict(input_features)[0]
        st.write(prediction_id)

        # Map category ID to category name
        category_mapping = {
            15: "Java Developer",
            23: "Testing",
            8: "DevOps Engineer",
            20: "Python Developer",
            24: "Web Designing",
            12: "HR",
            13: "Hadoop",
            3: "Blockchain",
            10: "ETL Developer",
            18: "Operations Manager",
            6: "Data Science",
            22: "Sales",
            16: "Mechanical Engineer",
            1: "Arts",
            7: "Database",
            11: "Electrical Engineering",
            14: "Health and fitness",
            19: "PMO",
            4: "Business Analyst",
            9: "DotNet Developer",
            2: "Automation Testing",
            17: "Network Security Engineer",
            21: "SAP Developer",
            5: "Civil Engineer",
            0: "Advocate",
        }

        category_name = category_mapping.get(prediction_id, "Unknown")

        st.write("Predicted Category:", category_name)



# python main
if __name__ == "__main__":
    main()