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
+
+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 = "2024-04-02"
+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()
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🌔 moondream2
+        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")
+        output = gr.TextArea(label="Response")
+    submit.click(answer_question, [img, prompt], output)
+    prompt.submit(answer_question, [img, prompt], output)
+
+demo.queue().launch()