Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor
+from PIL import Image, ImageDraw
+import requests
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+import random
+
+# Load model and processor
+model_id = 'microsoft/Florence-2-large'
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+def run_example(task_prompt, image, text_input=None):
+    prompt = task_prompt if text_input is None else task_prompt + text_input
+    inputs = processor(text=prompt, images=image, return_tensors="pt")
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        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()
+    ax.imshow(image)
+    for bbox, label in zip(data['bboxes'], data['labels']):
+        x1, y1, x2, y2 = bbox
+        rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
+        ax.add_patch(rect)
+        plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
+    plt.axis('off')
+    plt.show()
+
+def draw_polygons(image, prediction, fill_mask=False):
+    draw = ImageDraw.Draw(image)
+    colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan', 'red']
+    for polygons, label in zip(prediction['polygons'], prediction['labels']):
+        color = random.choice(colormap)
+        fill_color = color if fill_mask else None
+        for polygon in polygons:
+            draw.polygon(polygon, outline=color, fill=fill_color)
+            draw.text((polygon[0][0], polygon[0][1]), label, fill=color)
+    image.show()
+
+def gradio_interface(image, task_prompt, text_input):
+    result = run_example(task_prompt, image, text_input)
+    if task_prompt in ['<OD>', '<OPEN_VOCABULARY_DETECTION>']:
+        plot_bbox(image, result)
+    elif task_prompt in ['<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>']:
+        draw_polygons(image, result, fill_mask=True)
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Florence Model Advanced Tasks")
+    with gr.Row():
+        image_input = gr.Image(type="pil")
+        task_input = gr.Dropdown(label="Select Task", choices=[
+            '<CAPTION>', '<DETAILED_CAPTION>', '<MORE_DETAILED_CAPTION>', 
+            '<OD>', '<DENSE_REGION_CAPTION>', '<REGION_PROPOSAL>', 
+            '<CAPTION_TO_PHRASE_GROUNDING>', '<REFERRING_EXPRESSION_SEGMENTATION>',
+            '<REGION_TO_SEGMENTATION>', '<OPEN_VOCABULARY_DETECTION>',
+            '<REGION_TO_CATEGORY>', '<REGION_TO_DESCRIPTION>', '<OCR>', '<OCR_WITH_REGION>'
+        ])
+        text_input = gr.Textbox(label="Optional Text Input", placeholder="Enter text here if required by the task")
+        submit_btn = gr.Button("Run Task")
+        output = gr.Textbox(label="Output")
+    
+    submit_btn.click(fn=gradio_interface, inputs=[image_input, task_input, text_input], outputs=output)
+
+demo.launch()