removed print

app.py

@@ -40,16 +40,12 @@ def load_model(model_choice):
     
     # Only load if different model is requested
     if current_model != selected_model:
-        print(f"Loading model: {selected_model}")
         try:
             loaded_generator = pipeline("text2text-generation", model=selected_model)
             current_model = selected_model
-            print(f"Model {selected_model} loaded successfully")
         except Exception as e:
-            print(f"Error loading model {selected_model}: {e}")
             # Fallback to base model
             if selected_model != "google/flan-t5-base":
-                print("Falling back to base model...")
                 loaded_generator = pipeline("text2text-generation", model="google/flan-t5-base")
                 current_model = "google/flan-t5-base"
             else:
@@ -74,11 +70,8 @@ def extract_dl_info(image, model_choice, manual_text, use_manual_text):
         # Determine text source
         if use_manual_text and manual_text and manual_text.strip():
             # Use manual text input
-            print("Using manual text input...")
             raw_text = manual_text.strip()
             cleaned_text = clean_ocr_text(raw_text)
-            print(f"Manual text: {raw_text}")
-            print(f"Cleaned text: {cleaned_text}")
         else:
             # Use OCR on image
             if image is None:
@@ -89,23 +82,18 @@ def extract_dl_info(image, model_choice, manual_text, use_manual_text):
                 image = image.convert("RGB")
             
             # Use Tesseract OCR to extract text
-            print("Extracting text with Tesseract OCR...")
             raw_text = pytesseract.image_to_string(image)
-            print(f"Raw OCR text: {raw_text}")
             
             # Clean the OCR text using regex (only for cleaning, not extraction)
             cleaned_text = clean_ocr_text(raw_text)
-            print(f"Cleaned OCR text: {cleaned_text}")
         
         # Use AI model to process cleaned OCR text and extract structured information
-        print("Processing cleaned OCR text with AI model...")
         
         # Load model only when needed
         try:
             generator = load_model(model_choice)
         except Exception as e:
             error_msg = f"Failed to load AI model: {str(e)}"
-            print(error_msg)
             return raw_text, cleaned_text, json.dumps({"error": error_msg}, indent=2)
         
         # Create specific prompts for the AI model to extract individual fields
@@ -156,7 +144,6 @@ def extract_dl_info(image, model_choice, manual_text, use_manual_text):
             }
             
         except Exception as e:
-            print(f"AI extraction failed: {e}")
             extracted = {
                 "name": "AI extraction failed",
                 "address": "AI extraction failed",