Create app.py

app.py

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+import spaces
+import argparse
+import torch
+import re
+import gradio as gr
+from threading import Thread
+from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
+
+parser = argparse.ArgumentParser()
+
+if torch.cuda.is_available():
+    device, dtype = "cuda", torch.float16
+else:
+    device, dtype = "cpu", torch.float32
+
+model_id = "vikhyatk/moondream2"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision="2024-03-06")
+moondream = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True, revision="2024-03-06"
+).to(device=device, dtype=dtype)
+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:
+        clean_text = re.sub("<$|<END$", "", new_text)
+        buffer += clean_text
+        yield buffer
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Super Rapid Annotator - Multimodal vision tool to annotate videos with LLaVA framework
+        """
+    )
+    with gr.Row():
+        prompt = gr.Textbox(label="Input", placeholder="Type here...", 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()