few changes for logging

app.py

@@ -8,6 +8,9 @@ import torch
 from ast import literal_eval
 from PIL import Image
 
+import logging
+logging.basicConfig(level=logging.INFO)
+
 # Load the model on the available device(s)
 model = Qwen2VLForConditionalGeneration.from_pretrained(
     "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
@@ -56,14 +59,14 @@ def demo(image_path, prompt):
             ],
         }
     ]
-    print("1")
+    logging.info("Step 1: Preparing inference")
     # Preparation for inference
     text = processor.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
     )
-    print("2")
+    logging.info("2")
     image_inputs, video_inputs = process_vision_info(messages)
-    print("3")
+    logging.info("3")
     inputs = processor(
         text=[text],
         images=image_inputs,
@@ -71,22 +74,23 @@ def demo(image_path, prompt):
         padding=True,
         return_tensors="pt",
     )
-    print("4")
+    logging.info("4")
     inputs = inputs.to("cuda")
-    print("5")
+    logging.info("5")
     # Inference: Generation of the output
     generated_ids = model.generate(**inputs, max_new_tokens=1500)
-    print("6")
+    logging.info("6")
     generated_ids_trimmed = [
         out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
     ]
-    print("7")
+    logging.info("7")
     output_text = processor.batch_decode(
         generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
     )
-    print("8")
+    logging.info("8", output_text)
 
     # Handle output text to convert it into JSON
+    json = str()
     try:
         almost_json = output_text[0].split('\n')[-1].split('\n')[0]
         json = literal_eval(almost_json)
@@ -100,9 +104,10 @@ def process_document(image):
         image = Image.fromarray(image)  # Convert NumPy array to PIL Image
         image.save(tmp_file.name)  # Save the image to the temporary file
         image_path = tmp_file.name  # Get the path of the saved file
-
+    logging.info(image_path)
     # Process the image with your model
     one = demo(image_path, other_benifits)
+    logging.info("kjf")
     two = demo(image_path, tax_deductions)
     json_op = {
         "tax_deductions": one,