import os
import io
import streamlit as st
import docx
import docx2txt
import tempfile
import numpy as np
from scipy.spatial.distance import cosine
import time
import re
import concurrent.futures
from functools import lru_cache
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM

# Set page title and hide sidebar
st.set_page_config(
    page_title="Resume Analyzer and Company Suitability Checker",
    initial_sidebar_state="collapsed"
)

# Hide sidebar completely with custom CSS
st.markdown("""
<style>
    [data-testid="collapsedControl"] {display: none;}
    section[data-testid="stSidebar"] {display: none;}
</style>
""", unsafe_allow_html=True)

#####################################
# Preload Models - Optimized
#####################################
@st.cache_resource(show_spinner=True)
def load_models():
    """Load models at startup - using smaller/faster models"""
    with st.spinner("Loading AI models... This may take a minute on first run."):
        models = {}
        # Load smaller summarization model for speed
        models['summarizer'] = pipeline("summarization", model="facebook/bart-large-cnn", max_length=130)
        
        # Load smaller feature extraction model for speed
        models['feature_extractor'] = pipeline("feature-extraction", model="distilbert-base-uncased")
        
        return models

# Preload models immediately when app starts
models = load_models()

#####################################
# Function: Extract Text from File
#####################################
@st.cache_data(show_spinner=False)
def extract_text_from_file(file_obj):
    """
    Extract text from .docx and .doc files.
    Returns the extracted text or an error message if extraction fails.
    """
    filename = file_obj.name
    ext = os.path.splitext(filename)[1].lower()
    text = ""

    if ext == ".docx":
        try:
            document = docx.Document(file_obj)
            text = "\n".join(para.text for para in document.paragraphs if para.text.strip())
        except Exception as e:
            text = f"Error processing DOCX file: {e}"
    elif ext == ".doc":
        try:
            # For .doc files, we need to save to a temp file
            with tempfile.NamedTemporaryFile(delete=False, suffix='.doc') as temp_file:
                temp_file.write(file_obj.getvalue())
                temp_path = temp_file.name
        
            # Use docx2txt which is generally faster
            try:
                text = docx2txt.process(temp_path)
            except Exception:
                text = "Could not process .doc file. Please convert to .docx format."
        
            # Clean up temp file
            os.unlink(temp_path)
        except Exception as e:
            text = f"Error processing DOC file: {e}"
    elif ext == ".txt":
        try:
            text = file_obj.getvalue().decode("utf-8")
        except Exception as e:
            text = f"Error processing TXT file: {e}"
    else:
        text = "Unsupported file type. Please upload a .docx, .doc, or .txt file."
    return text

#####################################
# Functions for Information Extraction - Optimized
#####################################

# Cache the extraction functions to avoid reprocessing
@lru_cache(maxsize=32)
def extract_name(text_start):
    """Extract candidate name from the beginning of resume text"""
    # Only use the first 500 characters to speed up processing
    lines = text_start.split('\n')
    
    # Check first few non-empty lines for potential names
    potential_name_lines = [line.strip() for line in lines[:5] if line.strip()]
    
    if potential_name_lines:
        # First line is often the name if it's short and doesn't contain common headers
        first_line = potential_name_lines[0]
        if 5 <= len(first_line) <= 40 and not any(x in first_line.lower() for x in ["resume", "cv", "curriculum", "vitae", "profile"]):
            return first_line
    
    # Look for lines that might contain a name
    for line in potential_name_lines[:3]:
        if len(line.split()) <= 4 and not any(x in line.lower() for x in ["address", "phone", "email", "resume", "cv"]):
            return line

    return "Unknown (please extract from resume)"

def extract_age(text):
    """Extract candidate age from resume text"""
    # Simplified: just check a few common patterns
    age_patterns = [
        r'age:?\s*(\d{1,2})',
        r'(\d{1,2})\s*years\s*old',
    ]
    
    text_lower = text.lower()
    for pattern in age_patterns:
        matches = re.search(pattern, text_lower)
        if matches:
            return matches.group(1)
    
    return "Not specified"

def extract_industry(text, base_summary):
    """Extract expected job industry from resume"""
    # Simplified industry keywords focused on the most common ones
    industry_keywords = {
        "technology": ["software", "programming", "developer", "IT", "tech", "computer"],
        "finance": ["banking", "financial", "accounting", "finance", "analyst"],
        "healthcare": ["medical", "health", "hospital", "clinical", "nurse", "doctor"],
        "education": ["teaching", "teacher", "professor", "education", "university"],
        "marketing": ["marketing", "advertising", "digital marketing", "social media"],
        "engineering": ["engineer", "engineering"],
        "data science": ["data science", "machine learning", "AI", "analytics"],
        "information systems": ["information systems", "ERP", "systems management"]
    }
    
    # Count occurrences of industry keywords - using the summary to speed up
    combined_text = base_summary.lower()
    
    counts = {}
    for industry, keywords in industry_keywords.items():
        counts[industry] = sum(combined_text.count(keyword.lower()) for keyword in keywords)
    
    # Get the industry with the highest count
    if counts:
        likely_industry = max(counts.items(), key=lambda x: x[1])
        if likely_industry[1] > 0:
            return likely_industry[0].capitalize()
    
    # Check for educational background that might indicate industry
    degrees = ["computer science", "business", "engineering", "medicine", "education", "finance", "marketing"]
    
    for degree in degrees:
        if degree in combined_text:
            return f"{degree.capitalize()}-related field"
    
    return "Not clearly specified"

def extract_skills_and_work(text):
    """Extract both skills and work experience at once to save processing time"""
    # Common skill categories - reduced keyword list for speed
    skill_categories = {
        "Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#"],
        "Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch"],
        "Database": ["SQL", "MySQL", "MongoDB", "Database"],
        "Web Development": ["React", "Angular", "Node.js", "Frontend", "Backend"],
        "Software Development": ["Agile", "Scrum", "Git", "DevOps", "Docker"],
        "Cloud": ["AWS", "Azure", "Google Cloud", "Cloud"],
        "Business": ["Project Management", "Business Analysis", "Leadership"],
        "Tools": ["Excel", "PowerPoint", "Tableau", "Power BI", "JIRA"]
    }
    
    # Work experience extraction
    work_headers = [
        "work experience", "professional experience", "employment history", 
        "work history", "experience"
    ]
    
    next_section_headers = [
        "education", "skills", "certifications", "projects", "achievements"
    ]
    
    # Process everything at once
    lines = text.split('\n')
    text_lower = text.lower()
    
    # Skills extraction
    found_skills = []
    for category, skills in skill_categories.items():
        category_skills = []
        for skill in skills:
            if skill.lower() in text_lower:
                category_skills.append(skill)
        
        if category_skills:
            found_skills.append(f"{category}: {', '.join(category_skills)}")
    
    # Work experience extraction - simplified approach
    work_section = []
    in_work_section = False
    
    for idx, line in enumerate(lines):
        line_lower = line.lower().strip()
        
        # Start of work section
        if not in_work_section:
            if any(header in line_lower for header in work_headers):
                in_work_section = True
                continue
        # End of work section
        elif in_work_section:
            if any(header in line_lower for header in next_section_headers):
                break
            
            if line.strip():
                work_section.append(line.strip())
    
    # Simplified work formatting
    if not work_section:
        work_experience = "Work experience not clearly identified"
    else:
        # Just take the first 5-7 lines of the work section as a summary
        work_lines = []
        company_count = 0
        current_company = ""
        
        for line in work_section:
            # New company entry often has a date
            if re.search(r'(19|20)\d{2}', line):
                company_count += 1
                if company_count <= 3:  # Limit to 3 most recent positions
                    current_company = line
                    work_lines.append(f"**{line}**")
                else:
                    break
            elif company_count <= 3 and len(work_lines) < 10:  # Limit total lines
                work_lines.append(line)
        
        work_experience = "\n• " + "\n• ".join(work_lines[:7]) if work_lines else "Work experience not clearly structured"
    
    skills_formatted = "\n• " + "\n• ".join(found_skills) if found_skills else "No specific technical skills clearly identified"
    
    return skills_formatted, work_experience

#####################################
# Function: Summarize Resume Text - Optimized
#####################################
def summarize_resume_text(resume_text):
    """
    Generates a structured summary of the resume text - optimized for speed
    """
    start_time = time.time()
    
    # First, generate a quick summary using pre-loaded model
    max_input_length = 1024  # Model limit
    
    # Only summarize the first portion of text for speed
    text_to_summarize = resume_text[:min(len(resume_text), max_input_length)]
    base_summary = models['summarizer'](text_to_summarize)[0]['summary_text']
    
    # Extract information in parallel where possible
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # These can run in parallel
        name_future = executor.submit(extract_name, resume_text[:500])  # Only use start of text
        age_future = executor.submit(extract_age, resume_text)
        industry_future = executor.submit(extract_industry, resume_text, base_summary)
        skills_work_future = executor.submit(extract_skills_and_work, resume_text)
        
        # Get results
        name = name_future.result()
        age = age_future.result()
        industry = industry_future.result()
        skills, work_experience = skills_work_future.result()
    
    # Format the structured summary
    formatted_summary = f"Name: {name}\n"
    formatted_summary += f"Age: {age}\n"
    formatted_summary += f"Expected Job Industry: {industry}\n\n"
    formatted_summary += f"Previous Work Experience: {work_experience}\n\n"
    formatted_summary += f"Skills: {skills}"
    
    execution_time = time.time() - start_time
    
    return formatted_summary, execution_time

#####################################
# Function: Compare Candidate Summary to Company Prompt - Optimized
#####################################
# Fixed: Use underscore prefix for non-hashable arguments to tell Streamlit not to hash them
@st.cache_data(show_spinner=False)
def compute_suitability(candidate_summary, company_prompt, _feature_extractor=None):
    """
    Compute the similarity between candidate summary and company prompt.
    Returns a score in the range [0, 1] and execution time.
    """
    start_time = time.time()
    
    feature_extractor = _feature_extractor or models['feature_extractor']
    
    # Extract features (embeddings)
    candidate_features = feature_extractor(candidate_summary)
    company_features = feature_extractor(company_prompt)
    
    # Convert to numpy arrays and flatten if needed
    candidate_vec = np.mean(np.array(candidate_features[0]), axis=0)
    company_vec = np.mean(np.array(company_features[0]), axis=0)
    
    # Compute cosine similarity (1 - cosine distance)
    similarity = 1 - cosine(candidate_vec, company_vec)
    
    execution_time = time.time() - start_time
    
    return similarity, execution_time

#####################################
# Main Streamlit Interface - with Progress Reporting
#####################################
st.title("Resume Analyzer and Company Suitability Checker")
st.markdown(
    """
Upload your resume file in **.docx**, **.doc**, or **.txt** format. The app performs the following tasks:
1. Extracts text from the resume.
2. Uses AI to generate a structured candidate summary with name, age, expected job industry, previous work experience, and skills.
3. Compares the candidate summary with a company profile to produce a suitability score.
"""
)

# File uploader
uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])

# Company description text area
company_prompt = st.text_area(
    "Enter the company description or job requirements:",
    height=150,
    help="Enter a detailed description of the company culture, role requirements, and desired skills.",
)

# Process button with optimized flow
if uploaded_file is not None and company_prompt and st.button("Analyze Resume"):
    # Create a placeholder for the progress bar
    progress_bar = st.progress(0)
    status_text = st.empty()
    
    # Step 1: Extract text
    status_text.text("Step 1/3: Extracting text from resume...")
    resume_text = extract_text_from_file(uploaded_file)
    progress_bar.progress(25)
    
    if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
        st.error(resume_text)
    else:
        # Step 2: Generate summary
        status_text.text("Step 2/3: Analyzing resume and generating summary...")
        summary, summarization_time = summarize_resume_text(resume_text)
        progress_bar.progress(75)
        
        # Display summary
        st.subheader("Candidate Summary")
        st.markdown(summary)
        st.info(f"Summary generated in {summarization_time:.2f} seconds")
        
        # Step 3: Compute similarity
        status_text.text("Step 3/3: Calculating compatibility with company profile...")
        # Pass the feature extractor with an underscore prefix to avoid hashing issues
        similarity_score, similarity_time = compute_suitability(summary, company_prompt, _feature_extractor=models['feature_extractor'])
        progress_bar.progress(100)
        
        # Clear status messages
        status_text.empty()
        
        # Display similarity score
        st.subheader("Suitability Assessment")
        st.markdown(f"**Matching Score:** {similarity_score:.2%}")
        st.info(f"Compatibility assessment completed in {similarity_time:.2f} seconds")
        
        # Provide interpretation
        if similarity_score >= 0.85:
            st.success("Excellent match! This candidate's profile is strongly aligned with the company requirements.")
        elif similarity_score >= 0.70:
            st.success("Good match! This candidate shows strong potential for the position.")
        elif similarity_score >= 0.50:
            st.warning("Moderate match. The candidate meets some requirements but there may be gaps.")
        else:
            st.error("Low match. The candidate's profile may not align well with the requirements.")