Update README.md

README.md

@@ -14,10 +14,68 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Florence-2-large-FormClassification-ft
 
-This model is a fine-tuned version of [microsoft/Florence-2-large-ft](https://huggingface.co/microsoft/Florence-2-large-ft) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/Florence-2-large-ft](https://huggingface.co/microsoft/Florence-2-large-ft) on an Musa07/Florence_ft dataset.
 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>"])
+
+
+
 ## Model description
 
 More information needed