Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -52,7 +52,7 @@ def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scal
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@spaces.GPU
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def run_example(image, text_input, model_id="Qwen/Qwen2-VL-7B-Instruct"):
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model = models[model_id].eval()
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processor = processors[model_id]
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@@ -61,7 +61,7 @@ def run_example(image, text_input, model_id="Qwen/Qwen2-VL-7B-Instruct"):
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"role": "user",
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"content": [
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{"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
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{"type": "text", "text":
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{"type": "text", "text": f"detect {text_input}"},
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],
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}
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@@ -109,6 +109,7 @@ with gr.Blocks(css=css) as demo:
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with gr.Column():
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input_img = gr.Image(label="Input Picture", type="pil")
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model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2-VL-7B-Instruct")
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text_input = gr.Textbox(label="Description of Localization Target")
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submit_btn = gr.Button(value="Submit")
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with gr.Column():
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@@ -116,6 +117,6 @@ with gr.Blocks(css=css) as demo:
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parsed_boxes = gr.Textbox(label="Parsed Boxes")
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annotated_image = gr.Image(label="Annotated Picture")
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submit_btn.click(run_example, [input_img, text_input, model_selector], [model_output_text, parsed_boxes, annotated_image])
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demo.launch(debug=True)
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@spaces.GPU
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def run_example(image, text_input, system_prompt, model_id="Qwen/Qwen2-VL-7B-Instruct"):
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model = models[model_id].eval()
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processor = processors[model_id]
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"role": "user",
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"content": [
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{"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
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{"type": "text", "text": system_prompt},
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{"type": "text", "text": f"detect {text_input}"},
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],
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}
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with gr.Column():
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input_img = gr.Image(label="Input Picture", type="pil")
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model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2-VL-7B-Instruct")
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system_prompt = gr.Textbox(label="System Prompt", value="You are a helpfull assistant to detect objects in images. When asked to detect elements based on a description you return bounding boxes for all elements in the form of [xmin, ymin, xmax, ymax] whith the values beeing scaled to 1000 by 1000 pixels. When there are more than one result answer with a list of bounding boxes in the form of [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ...].")
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text_input = gr.Textbox(label="Description of Localization Target")
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submit_btn = gr.Button(value="Submit")
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with gr.Column():
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parsed_boxes = gr.Textbox(label="Parsed Boxes")
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annotated_image = gr.Image(label="Annotated Picture")
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submit_btn.click(run_example, [input_img, text_input, system_prompt, model_selector], [model_output_text, parsed_boxes, annotated_image])
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demo.launch(debug=True)
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