remove unused parts

app.py

@@ -26,12 +26,6 @@ DESCRIPTION = "# [Florence-2-DocVQA Demo](https://huggingface.co/HuggingFaceM4/F
 colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
             'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
 
-def fig_to_pil(fig):
-    buf = io.BytesIO()
-    fig.savefig(buf, format='png')
-    buf.seek(0)
-    return Image.open(buf)
-
 @spaces.GPU
 def run_example(task_prompt, image, text_input=None):
     if text_input is None:
@@ -53,138 +47,14 @@ def run_example(task_prompt, image, text_input=None):
         task=task_prompt,
         image_size=(image.width, image.height)
     )
-    return parsed_answer[task_prompt]#.replace("<pad>", "")
-
-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))
-    ax.axis('off')
-    return fig
-
-def draw_polygons(image, prediction, fill_mask=False):
-
-    draw = ImageDraw.Draw(image)
-    scale = 1
-    for polygons, label in zip(prediction['polygons'], prediction['labels']):
-        color = random.choice(colormap)
-        fill_color = random.choice(colormap) if fill_mask else None
-        for _polygon in polygons:
-            _polygon = np.array(_polygon).reshape(-1, 2)
-            if len(_polygon) < 3:
-                print('Invalid polygon:', _polygon)
-                continue
-            _polygon = (_polygon * scale).reshape(-1).tolist()
-            if fill_mask:
-                draw.polygon(_polygon, outline=color, fill=fill_color)
-            else:
-                draw.polygon(_polygon, outline=color)
-            draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
-    return image
-
-def convert_to_od_format(data):
-    bboxes = data.get('bboxes', [])
-    labels = data.get('bboxes_labels', [])
-    od_results = {
-        'bboxes': bboxes,
-        'labels': labels
-    }
-    return od_results
-
-def draw_ocr_bboxes(image, prediction):
-    scale = 1
-    draw = ImageDraw.Draw(image)
-    bboxes, labels = prediction['quad_boxes'], prediction['labels']
-    for box, label in zip(bboxes, labels):
-        color = random.choice(colormap)
-        new_box = (np.array(box) * scale).tolist()
-        draw.polygon(new_box, width=3, outline=color)
-        draw.text((new_box[0]+8, new_box[1]+2),
-                  "{}".format(label),
-                  align="right",
-                  fill=color)
-    return image
-
-def process_image(image, task_prompt, text_input=None):
+    return parsed_answer
+
+def process_image(image, text_input=None):
     image = Image.fromarray(image)  # Convert NumPy array to PIL Image
-    if task_prompt == 'Document Visual Question Answering':
-        task_prompt = '<DocVQA>'
-        results = run_example(task_prompt, image, text_input)
-        return results, None
-    elif task_prompt == 'Caption':
-        task_prompt = '<CAPTION>'
-        results = run_example(task_prompt, image)
-        return results, None
-    elif task_prompt == 'Detailed Caption':
-        task_prompt = '<DETAILED_CAPTION>'
-        results = run_example(task_prompt, image)
-        return results, None
-    elif task_prompt == 'More Detailed Caption':
-        task_prompt = '<MORE_DETAILED_CAPTION>'
-        results = run_example(task_prompt, image)
-        return results, None
-    elif task_prompt == 'Object Detection':
-        task_prompt = '<OD>'
-        results = run_example(task_prompt, image)
-        fig = plot_bbox(image, results['<OD>'])
-        return results, fig_to_pil(fig)
-    elif task_prompt == 'Dense Region Caption':
-        task_prompt = '<DENSE_REGION_CAPTION>'
-        results = run_example(task_prompt, image)
-        fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
-        return results, fig_to_pil(fig)
-    elif task_prompt == 'Region Proposal':
-        task_prompt = '<REGION_PROPOSAL>'
-        results = run_example(task_prompt, image)
-        fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
-        return results, fig_to_pil(fig)
-    elif task_prompt == 'Caption to Phrase Grounding':
-        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
-        results = run_example(task_prompt, image, text_input)
-        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
-        return results, fig_to_pil(fig)
-    elif task_prompt == 'Referring Expression Segmentation':
-        task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
-        results = run_example(task_prompt, image, text_input)
-        output_image = copy.deepcopy(image)
-        output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
-        return results, output_image
-    elif task_prompt == 'Region to Segmentation':
-        task_prompt = '<REGION_TO_SEGMENTATION>'
-        results = run_example(task_prompt, image, text_input)
-        output_image = copy.deepcopy(image)
-        output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
-        return results, output_image
-    elif task_prompt == 'Open Vocabulary Detection':
-        task_prompt = '<OPEN_VOCABULARY_DETECTION>'
-        results = run_example(task_prompt, image, text_input)
-        bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
-        fig = plot_bbox(image, bbox_results)
-        return results, fig_to_pil(fig)
-    elif task_prompt == 'Region to Category':
-        task_prompt = '<REGION_TO_CATEGORY>'
-        results = run_example(task_prompt, image, text_input)
-        return results, None
-    elif task_prompt == 'Region to Description':
-        task_prompt = '<REGION_TO_DESCRIPTION>'
-        results = run_example(task_prompt, image, text_input)
-        return results, None
-    elif task_prompt == 'OCR':
-        task_prompt = '<OCR>'
-        results = run_example(task_prompt, image)
-        return results, None
-    elif task_prompt == 'OCR with Region':
-        task_prompt = '<OCR_WITH_REGION>'
-        results = run_example(task_prompt, image)
-        output_image = copy.deepcopy(image)
-        output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
-        return results, output_image
-    else:
-        return "", None  # Return empty string and None for unknown task prompts
+    task_prompt = '<DocVQA>'
+    results = run_example(task_prompt, image, text_input)[task_prompt].replace("<pad>", "")
+    return results, None
+
 
 css = """
   #output {
@@ -200,14 +70,6 @@ with gr.Blocks(css=css) as demo:
         with gr.Row():
             with gr.Column():
                 input_img = gr.Image(label="Input Picture")
-                task_prompt = gr.Dropdown(choices=[
-                    'Document Visual Question Answering',
-                    'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
-                    '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'
-                ], label="Task Prompt", value= 'Document Visual Question Answering')
                 text_input = gr.Textbox(label="Text Input (optional)")
                 submit_btn = gr.Button(value="Submit")
             with gr.Column():
@@ -219,13 +81,13 @@ with gr.Blocks(css=css) as demo:
                 ["image1.jpg", 'Object Detection'],
                 ["image2.jpg", 'OCR with Region']
             ],
-            inputs=[input_img, task_prompt],
+            inputs=[input_img],
             outputs=[output_text, output_img],
             fn=process_image,
             cache_examples=True,
             label='Try examples'
         )
 
-        submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img])
+        submit_btn.click(process_image, [input_img, text_input], [output_text, output_img])
 
 demo.launch(debug=True)