Update README.md

README.md

@@ -19,60 +19,60 @@ It achieves the following results on the evaluation set:
 - Loss: 0.2107
 
 ### Inference Code versions
-from transformers import AutoProcessor, AutoModelForCausalLM
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-model = AutoModelForCausalLM.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True, device_map='cuda') # Load the model on GPU if available
-processor = AutoProcessor.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True)
-
-def run_example(task_prompt, image, max_new_tokens=128):
-    prompt = task_prompt
-    inputs = processor(text=prompt, images=image, return_tensors="pt")
-    generated_ids = model.generate(
-      input_ids=inputs["input_ids"].cuda(),
-      pixel_values=inputs["pixel_values"].cuda(),
-      max_new_tokens=max_new_tokens,
-      early_stopping=False,
-      do_sample=False,
-      num_beams=3,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    parsed_answer = processor.post_process_generation(
-        generated_text,
-        task=task_prompt,
-        image_size=(image.width, image.height)
-    )
-    return parsed_answer
-
-def plot_bbox(image, data):
-   # Create a figure and axes
-    fig, ax = plt.subplots()
-
-    # Display the image
-    ax.imshow(image)
-
-    # Plot each bounding box
-    for bbox, label in zip(data['bboxes'], data['labels']):
-        # Unpack the bounding box coordinates
-        x1, y1, x2, y2 = bbox
-        # Create a Rectangle patch
-        rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
-        # Add the rectangle to the Axes
-        ax.add_patch(rect)
-        # Annotate the label
-        plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
-
-    # Remove the axis ticks and labels
-    ax.axis('off')
-
-    # Show the plot
-    plt.show()
-    
-image = Image.open('1.jpeg')
-parsed_answer = run_example("<OD>", image=image)
-print(parsed_answer)
-plot_bbox(image, parsed_answer["<OD>"])
+  from transformers import AutoProcessor, AutoModelForCausalLM
+  import matplotlib.pyplot as plt
+  import matplotlib.patches as patches
+  
+  model = AutoModelForCausalLM.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True, device_map='cuda') # Load the model on GPU if available
+  processor = AutoProcessor.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True)
+  
+  def run_example(task_prompt, image, max_new_tokens=128):
+      prompt = task_prompt
+      inputs = processor(text=prompt, images=image, return_tensors="pt")
+      generated_ids = model.generate(
+        input_ids=inputs["input_ids"].cuda(),
+        pixel_values=inputs["pixel_values"].cuda(),
+        max_new_tokens=max_new_tokens,
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
+      )
+      generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+      parsed_answer = processor.post_process_generation(
+          generated_text,
+          task=task_prompt,
+          image_size=(image.width, image.height)
+      )
+      return parsed_answer
+  
+  def plot_bbox(image, data):
+  
+      fig, ax = plt.subplots()
+  
+      # Display the image
+      ax.imshow(image)
+  
+      # Plot each bounding box
+      for bbox, label in zip(data['bboxes'], data['labels']):
+          # Unpack the bounding box coordinates
+          x1, y1, x2, y2 = bbox
+          # Create a Rectangle patch
+          rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
+          # Add the rectangle to the Axes
+          ax.add_patch(rect)
+          # Annotate the label
+          plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
+  
+      # Remove the axis ticks and labels
+      ax.axis('off')
+  
+      # Show the plot
+      plt.show()
+      
+  image = Image.open('1.jpeg')
+  parsed_answer = run_example("<OD>", image=image)
+  print(parsed_answer)
+  plot_bbox(image, parsed_answer["<OD>"])