Create app.py

app.py

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+import spaces
+import torch
+import re
+import gradio as gr
+from threading import Thread
+from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
+from PIL import ImageDraw
+from torchvision.transforms.v2 import Resize
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+model_id = "vikhyatk/moondream2"
+revision = "2024-08-26"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
+moondream = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True, revision=revision,
+    torch_dtype=torch.bfloat16, device_map={"": "cuda"},
+    attn_implementation="flash_attention_2"
+)
+moondream.eval()
+
+
+@spaces.GPU(duration=10)
+def answer_question(img, prompt):
+    image_embeds = moondream.encode_image(img)
+    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
+    thread = Thread(
+        target=moondream.answer_question,
+        kwargs={
+            "image_embeds": image_embeds,
+            "question": prompt,
+            "tokenizer": tokenizer,
+            "streamer": streamer,
+        },
+    )
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer.strip()
+
+def extract_floats(text):
+    # Regular expression to match an array of four floating point numbers
+    pattern = r"\[\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*,\s*(-?\d+\.\d+)\s*\]"
+    match = re.search(pattern, text)
+    if match:
+        # Extract the numbers and convert them to floats
+        return [float(num) for num in match.groups()]
+    return None  # Return None if no match is found
+
+
+def extract_bbox(text):
+    bbox = None
+    if extract_floats(text) is not None:
+        x1, y1, x2, y2 = extract_floats(text)
+        bbox = (x1, y1, x2, y2)
+    return bbox
+
+def process_answer(img, answer):
+    if extract_bbox(answer) is not None:
+        x1, y1, x2, y2 = extract_bbox(answer)
+        draw_image = Resize(768)(img)
+        width, height = draw_image.size
+        x1, x2 = int(x1 * width), int(x2 * width)
+        y1, y2 = int(y1 * height), int(y2 * height)
+        bbox = (x1, y1, x2, y2)
+        ImageDraw.Draw(draw_image).rectangle(bbox, outline="red", width=3)
+        return gr.update(visible=True, value=draw_image)
+
+    return gr.update(visible=False, value=None)
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🌔 moondream vl (new)
+        A tiny vision language model. [GitHub](https://github.com/vikhyat/moondream)
+        """
+    )
+    with gr.Row():
+        prompt = gr.Textbox(label="Input", value="Describe this image.", scale=4)
+        submit = gr.Button("Submit")
+    with gr.Row():
+        img = gr.Image(type="pil", label="Upload an Image")
+        with gr.Column():
+            output = gr.Markdown(label="Response")
+            ann = gr.Image(visible=False, label="Annotated Image")
+
+    submit.click(answer_question, [img, prompt], output)
+    prompt.submit(answer_question, [img, prompt], output)
+    output.change(process_answer, [img, output], ann, show_progress=False)
+
+demo.queue().launch()