import gradio as gr
import json
import PyPDF2
import docx


# ----------------------------
# Resume Text Extraction
# ----------------------------

def extract_text(file):

    if file is None:
        return ""

    filename = file.name
    text = ""

    if filename.endswith(".pdf"):
        reader = PyPDF2.PdfReader(file)
        for page in reader.pages:
            text += page.extract_text() or ""

    elif filename.endswith(".docx"):
        document = docx.Document(file)
        for para in document.paragraphs:
            text += para.text + "\n"

    elif filename.endswith(".txt"):
        text = file.read().decode("utf-8")

    return text


# ----------------------------
# Resume Analyzer
# ----------------------------

def analyze_resume(resume_text):

    if not resume_text.strip():
        return {
            "score": 0,
            "technical_skills": [],
            "soft_skills": [],
            "recommendation": "No text detected in resume"
        }

    text = resume_text.lower()

    tech_keywords = [
        "python","java","c++","sql","machine learning",
        "data analysis","tensorflow","pandas","numpy",
        "git","linux","ai"
    ]

    soft_keywords = [
        "communication","teamwork","leadership",
        "problem solving","adaptability"
    ]

    tech_found = [k for k in tech_keywords if k in text]
    soft_found = [k for k in soft_keywords if k in text]

    score = min(100, len(tech_found)*8 + len(soft_found)*5)

    recommendation = (
        "Add more technical skills and measurable achievements."
        if score < 50 else
        "Good resume. Minor improvements recommended."
    )

    return {
        "score": score,
        "technical_skills": tech_found,
        "soft_skills": soft_found,
        "recommendation": recommendation
    }


# ----------------------------
# Format analysis for UI
# ----------------------------

def format_analysis(result):

    return f"""
## Resume Score: {result['score']}/100

### Technical Skills Found
{', '.join(result['technical_skills']) if result['technical_skills'] else "None"}

### Soft Skills Found
{', '.join(result['soft_skills']) if result['soft_skills'] else "None"}

### Recommendation
{result['recommendation']}
"""


# ----------------------------
# Export Functions
# ----------------------------

def export_json(data):

    file_path = "analysis.json"

    with open(file_path,"w") as f:
        json.dump(data,f,indent=4)

    return file_path


def export_text(data):

    file_path = "analysis.txt"

    with open(file_path,"w") as f:
        f.write(str(data))

    return file_path


# ----------------------------
# Processing Pipeline
# ----------------------------

def process_resume(file):

    text = extract_text(file)

    analysis = analyze_resume(text)

    formatted = format_analysis(analysis)

    return text, formatted, analysis


# ----------------------------
# UI
# ----------------------------

with gr.Blocks(title="Resume Analyzer") as demo:

    gr.Markdown("# AI Resume Analyzer")
    gr.Markdown("Upload your resume and get instant feedback.")

    resume_file = gr.File(label="Upload Resume (PDF / DOCX / TXT)")

    analyze_btn = gr.Button("Analyze Resume")

    resume_text = gr.Textbox(
        label="Extracted Resume Text",
        lines=10
    )

    analysis_output = gr.Markdown(label="Analysis Result")

    analysis_state = gr.State()

    with gr.Row():

        export_json_btn = gr.Button("Export JSON")
        export_text_btn = gr.Button("Export Text")

    download_file = gr.File(label="Download Analysis")

    # ----------------------------
    # Button Actions
    # ----------------------------

    analyze_btn.click(
        process_resume,
        inputs=resume_file,
        outputs=[resume_text, analysis_output, analysis_state]
    )

    export_json_btn.click(
        export_json,
        inputs=analysis_state,
        outputs=download_file
    )

    export_text_btn.click(
        export_text,
        inputs=analysis_state,
        outputs=download_file
    )


# ----------------------------
# Launch
# ----------------------------

if __name__ == "__main__":
    demo.launch()