import gradio as gr
from PIL import Image
import pytesseract
import spacy
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as RLImage
from reportlab.lib.styles import getSampleStyleSheet
import os
from datetime import datetime

# Load NLP model for analysis
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    os.system("python -m spacy download en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")

def analyze_text(text):
    """Analyze the writing style and content"""
    doc = nlp(text)
    
    # Calculate word count and lengths
    words = [token for token in doc if not token.is_punct]
    word_count = len(words)
    word_lengths = sum(len(token) for token in words)
    
    analysis = {
        "word_count": word_count,
        "sentence_count": len(list(doc.sents)),
        "avg_word_length": word_lengths / max(1, word_count),
        "pos_tags": {tag: 0 for tag in ["NOUN", "VERB", "ADJ", "ADV"]},
        "readability": "Moderate",
        "sentiment": "Neutral"
    }
    
    # Count POS tags
    for token in doc:
        if token.pos_ in analysis["pos_tags"]:
            analysis["pos_tags"][token.pos_] += 1
    
    # Simple readability assessment
    if analysis["avg_word_length"] < 4:
        analysis["readability"] = "Easy"
    elif analysis["avg_word_length"] > 6:
        analysis["readability"] = "Difficult"
    
    return analysis

def create_pdf(text, analysis, image_path=None):
    """Generate PDF report"""
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    pdf_path = f"handwriting_report_{timestamp}.pdf"
    
    doc = SimpleDocTemplate(pdf_path, pagesize=letter)
    styles = getSampleStyleSheet()
    story = []
    
    # Title
    story.append(Paragraph("Handwriting Analysis Report", styles["Title"]))
    story.append(Spacer(1, 12))
    
    # Add original image if provided
    if image_path and os.path.exists(image_path):
        try:
            img = RLImage(image_path, width=400, height=300)
            story.append(img)
            story.append(Spacer(1, 12))
        except:
            pass
    
    # Extracted Text section
    story.append(Paragraph("Extracted Text:", styles["Heading2"]))
    story.append(Spacer(1, 12))
    story.append(Paragraph(text, styles["Normal"]))
    story.append(Spacer(1, 24))
    
    # Analysis section
    story.append(Paragraph("Writing Analysis:", styles["Heading2"]))
    story.append(Spacer(1, 12))
    
    analysis_text = f"""
    Word Count: {analysis['word_count']}<br/>
    Sentence Count: {analysis['sentence_count']}<br/>
    Average Word Length: {analysis['avg_word_length']:.2f} characters<br/>
    Readability: {analysis['readability']}<br/>
    <br/>
    Part-of-Speech Distribution:<br/>
    - Nouns: {analysis['pos_tags']['NOUN']}<br/>
    - Verbs: {analysis['pos_tags']['VERB']}<br/>
    - Adjectives: {analysis['pos_tags']['ADJ']}<br/>
    - Adverbs: {analysis['pos_tags']['ADV']}<br/>
    <br/>
    Overall Sentiment: {analysis['sentiment']}
    """
    
    story.append(Paragraph(analysis_text, styles["Normal"]))
    doc.build(story)
    return pdf_path

def process_handwriting(image):
    """Main processing function"""
    try:
        # Convert image to text
        text = pytesseract.image_to_string(image)
        
        if not text.strip():
            return "No text could be extracted from the image.", None
        
        # Analyze the text
        analysis = analyze_text(text)
        
        # Save image temporarily for PDF
        temp_image_path = "temp_input_image.jpg"
        image.save(temp_image_path)
        
        # Create PDF report
        pdf_path = create_pdf(text, analysis, temp_image_path)
        
        # Clean up
        if os.path.exists(temp_image_path):
            os.remove(temp_image_path)
        
        return text, pdf_path
    
    except Exception as e:
        return f"Error processing image: {str(e)}", None

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Handwriting Analysis Tool")
    gr.Markdown("Upload a handwritten image to extract text and get a writing analysis report.")
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(label="Upload Handwritten Image", type="pil")
            submit_btn = gr.Button("Analyze")
        
        with gr.Column():
            text_output = gr.Textbox(label="Extracted Text", lines=10)
            pdf_output = gr.File(label="Download Analysis Report")
    
    submit_btn.click(
        fn=process_handwriting,
        inputs=image_input,
        outputs=[text_output, pdf_output]
    )

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