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
+from PIL import Image
+
+parser = argparse.ArgumentParser()
+
+model_id = "vikhyat/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.float32
+)
+moondream.eval()
+
+@spaces.GPU(duration=10)
+def answer_question(images, prompts):
+    image_embeds = [moondream.encode_image(img) for img in images]
+    image_embeds = torch.cat(image_embeds, dim=0)
+    answers = moondream.batch_answer(
+        images=image_embeds,
+        prompts=prompts,
+        tokenizer=tokenizer
+    )
+    return [answer for answer in answers]
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🌔 moondream2
+        A tiny vision language model. [GitHub](https://github.com/vikhyat/moondream)
+        """
+    )
+    with gr.Row():
+        prompts = gr.Textbox(label="Input", placeholder="Type here...", scale=4)
+        submit = gr.Button("Submit")
+    with gr.Row():
+        images = gr.Image(type="pil", label="Upload Images", multiple=True)
+        output = gr.Textbox(label="Response", multiple=True)
+    submit.click(answer_question, [images, prompts], output)
+    prompts.submit(answer_question, [images, prompts], output)
+
+demo.queue().launch()