Initial commit: IYmtg Master project

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

generate_report.py

@@ -0,0 +1,81 @@
+import os
+import datetime
+
+# Define paths relative to IYmtg_Automation/
+TRAINING_ROOT = "../IYmtg_Training"
+OUTPUT_FILE = "../IYmtg_App_iOS/app_description_update.txt"
+
+if not os.path.exists(TRAINING_ROOT) and os.path.exists("IYmtg_Training"):
+    TRAINING_ROOT = "IYmtg_Training"
+    OUTPUT_FILE = "IYmtg_App_iOS/app_description_update.txt"
+
+def count_images_in_dir(directory):
+    count = 0
+    if os.path.exists(directory):
+        for root, _, files in os.walk(directory):
+            count += len([f for f in files if f.lower().endswith(('.jpg', '.jpeg', '.png'))])
+    return count
+
+def get_training_status(count, basic_threshold, advanced_threshold):
+    if count == 0:
+        return "❌ Inactive (0 images)"
+    elif count < basic_threshold:
+        # Less than 10 per category is generally unstable
+        return f"⚠️ Experimental ({count} images - Needs {basic_threshold}+)"
+    elif count < advanced_threshold:
+        return f"✅ Basic ({count} images - Functional)"
+    else:
+        return f"🌟 Advanced ({count} images - High Accuracy)"
+
+def main():
+    print("Generating App Description Update...")
+    
+    # 1. Analyze Models
+    foil_count = count_images_in_dir(os.path.join(TRAINING_ROOT, "Foil_Data"))
+    stamp_count = count_images_in_dir(os.path.join(TRAINING_ROOT, "Stamp_Data"))
+    cond_count = count_images_in_dir(os.path.join(TRAINING_ROOT, "Condition_Data"))
+    
+    # Set Symbols might be in Automation folder or Training folder
+    set_sym_count = count_images_in_dir(os.path.join(TRAINING_ROOT, "Set_Symbol_Training"))
+    if set_sym_count == 0:
+        set_sym_count = count_images_in_dir("Set_Symbol_Training")
+
+    # 2. Build Report
+    lines = []
+    lines.append(f"IYmtg System Status - {datetime.date.today().strftime('%B %d, %Y')}")
+    lines.append("==================================================")
+    lines.append("")
+    lines.append("🧠 AI MODEL STATUS")
+    # Foil: 13 Classes. Min 130 (10/class), Adv 650 (50/class)
+    lines.append(f"• Foil Classification:   {get_training_status(foil_count, 130, 650)}")
+    # Stamp: 2 Classes. Min 20 (10/class), Adv 100 (50/class)
+    lines.append(f"• Promo Stamp Detection: {get_training_status(stamp_count, 20, 100)}")
+    # Condition: 13 Classes. Min 130, Adv 650
+    lines.append(f"• Condition Grading:     {get_training_status(cond_count, 130, 650)}")
+    # Sets: Hundreds. Min 500, Adv 2000
+    lines.append(f"• Set Symbol Recog:      {get_training_status(set_sym_count, 500, 2000)}")
+    lines.append("")
+    lines.append("👁️ IDENTIFICATION CAPABILITIES")
+    lines.append("• Standard Cards:        ✅ Active (Vector Fingerprinting)")
+    lines.append("• Alpha vs Beta:         ✅ Active (Corner Radius Detection)")
+    lines.append("• Unlimited vs Revised:  ✅ Active (Saturation Analysis)")
+    lines.append("• The List / Mystery:    ✅ Active (Symbol Detection)")
+    lines.append("• World Champ Decks:     ✅ Active (Gold Border Detection)")
+    lines.append("• Chronicles Reprints:   ✅ Active (Border Color Logic)")
+    lines.append("• Serialized Cards:      ✅ Active (OCR Number Pattern)")
+    lines.append("• Summer Magic (Edgar):  ✅ Active (OCR Copyright Date)")
+    lines.append("")
+    lines.append("📝 NOTE TO REVIEWER")
+    lines.append("This build includes updated ML models based on the dataset sizes listed above.")
+    lines.append("Features marked 'Inactive' will fallback to manual entry or basic heuristics.")
+    
+    # 3. Write to File
+    try:
+        with open(OUTPUT_FILE, "w", encoding="utf-8") as f:
+            f.write("\n".join(lines))
+        print(f"Success! Description saved to: {os.path.abspath(OUTPUT_FILE)}")
+    except Exception as e:
+        print(f"Error writing report: {e}")
+
+if __name__ == "__main__":
+    main()