Delete batchapp.py

batchapp.py

@@ -1,48 +0,0 @@
-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()