Create model.py

model.py

@@ -0,0 +1,167 @@
+# model.py
+from PIL import Image, ImageEnhance
+import tempfile
+import os
+import cv2
+import numpy as np
+from PyPDF2 import PdfMerger, PdfReader, PdfWriter
+import csv
+import subprocess
+import shutil
+import pandas as pd
+from typing import List, Tuple
+
+# ---------------- IMAGE ENHANCEMENT & SCALING ---------------- #
+def enhance_image(image: Image.Image, quality_level: str) -> Image.Image:
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    
+    quality_map = {
+        "Low": (1.0, 1.0),
+        "Medium": (1.1, 1.05),
+        "High": (1.25, 1.1),
+        "Maximum": (1.4, 1.2)
+    }
+    sharp, contrast = quality_map.get(quality_level, (1.2, 1.1))
+    
+    image = ImageEnhance.Sharpness(image).enhance(sharp)
+    image = ImageEnhance.Contrast(image).enhance(contrast)
+    return image
+
+def compress_with_opencv(pil_img, quality=40):
+    img = np.array(pil_img)[:, :, ::-1]  # PIL → BGR
+    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+    _, enc = cv2.imencode('.jpg', img, encode_param)
+    dec = cv2.imdecode(enc, cv2.IMREAD_COLOR)
+    return Image.fromarray(cv2.cvtColor(dec, cv2.COLOR_BGR2RGB))
+
+# ---------------- IMAGE → PDF ---------------- #
+def images_to_pdf(images, page_size: str, orientation: str, quality_level: str, compression: bool = True):
+    if not images:
+        return None, "⚠️ Please upload at least one image."
+    
+    page_sizes = {
+        "A4": (2480, 3508),
+        "Letter": (2550, 3300),
+        "Legal": (2550, 4200),
+        "Original": None
+    }
+    
+    processed = []
+    target_size = page_sizes.get(page_size)
+    
+    for img_file in images:
+        if isinstance(img_file, str):
+            img = Image.open(img_file)
+        else:
+            img = Image.open(img_file.name) if hasattr(img_file, 'name') else img_file
+        
+        img = enhance_image(img, quality_level)
+        
+        if target_size:
+            if orientation == "Landscape":
+                target_size = (target_size[1], target_size[0])
+            
+            bg = Image.new("RGB", target_size, (255, 255, 255))
+            img.thumbnail(target_size, Image.Resampling.LANCZOS)
+            offset = ((target_size[0] - img.width) // 2, (target_size[1] - img.height) // 2)
+            bg.paste(img, offset)
+            img = bg
+        
+        if compression:
+            img = compress_with_opencv(img, quality=40)
+        
+        processed.append(img)
+    
+    output_path = os.path.join(tempfile.gettempdir(), "Nadish_Converted_File.pdf")
+    processed[0].save(output_path, "PDF", save_all=True, append_images=processed[1:], resolution=100)
+    
+    size_mb = os.path.getsize(output_path) / (1024 * 1024)
+    return output_path, f"✅ PDF Created | Size: {size_mb:.2f} MB"
+
+# ---------------- PDF MERGE ---------------- #
+def merge_pdfs(pdf_files):
+    if not pdf_files:
+        return None, "⚠️ Please upload PDFs."
+    
+    merger = PdfMerger()
+    for pdf in pdf_files:
+        merger.append(pdf.name if hasattr(pdf, 'name') else pdf)
+    
+    output_path = os.path.join(tempfile.gettempdir(), "Nadish_Merged_File.pdf")
+    merger.write(output_path)
+    merger.close()
+    
+    size_mb = os.path.getsize(output_path) / (1024 * 1024)
+    return output_path, f"✅ Merged {len(pdf_files)} PDFs | Size: {size_mb:.2f} MB"
+
+# ---------------- PDF COMPRESSION ---------------- #
+compression_map = {
+    "Maximum Compression (Smallest Size)": "screen",
+    "Balanced (Good Quality + Small Size)": "ebook",
+    "High Quality (Larger Size)": "printer",
+    "Original Quality (Least Compression)": "prepress"
+}
+
+def compress_pdf(pdf_file, compression_level: str):
+    if not pdf_file:
+        return None, "⚠️ Upload a PDF.", None
+    
+    gs_level = compression_map[compression_level]
+    
+    original_copy = os.path.join(tempfile.gettempdir(), "Nadish_Original.pdf")
+    shutil.copy(pdf_file.name if hasattr(pdf_file, 'name') else pdf_file, original_copy)
+    
+    output_path = os.path.join(tempfile.gettempdir(), f"Nadish_Compressed_{gs_level}.pdf")
+    
+    original_size = os.path.getsize(original_copy) / (1024 * 1024)
+    
+    gs_command = [
+        "gs", "-sDEVICE=pdfwrite", "-dCompatibilityLevel=1.4",
+        f"-dPDFSETTINGS=/{gs_level}",
+        "-dNOPAUSE", "-dQUIET", "-dBATCH",
+        "-dDetectDuplicateImages=true",
+        "-dCompressFonts=true",
+        "-dSubsetFonts=true",
+        f"-sOutputFile={output_path}", original_copy
+    ]
+    
+    try:
+        subprocess.run(gs_command, check=True, capture_output=True)
+        new_size = os.path.getsize(output_path) / (1024 * 1024)
+        reduction = ((original_size - new_size) / original_size) * 100 if original_size > 0 else 0
+        
+        table = f"""
+| Metric                | Value          |
+|-----------------------|----------------|
+| Original Size (MB)    | {original_size:.2f} |
+| Compressed Size (MB)  | {new_size:.2f}     |
+| Reduction (%)         | {reduction:.1f}%   |
+"""
+        return output_path, f"✅ Compression Successful!", table
+    except Exception as e:
+        return None, f"❌ Ghostscript Error: {str(e)}", None
+
+# ---------------- FEEDBACK ---------------- #
+def save_feedback(name_or_email: str, feedback_text: str, rating: str):
+    if not rating:
+        return "⚠️ Please select a rating."
+    
+    file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "feedback.csv")
+    file_exists = os.path.isfile(file_path)
+    
+    with open(file_path, mode="a", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        if not file_exists:
+            writer.writerow(["Name", "Feedback", "Rating"])
+        writer.writerow([name_or_email or "Anonymous", feedback_text or "N/A", rating])
+    
+    return "✅ Feedback saved! Thank you."
+
+def show_feedback():
+    file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "feedback.csv")
+    if not os.path.isfile(file_path):
+        return "⚠️ No feedback yet."
+    
+    df = pd.read_csv(file_path)
+    return df.to_markdown(index=False)