Delete originalapp.py

originalapp.py

@@ -1,59 +0,0 @@
-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()
-
-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.float32
-)
-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(
-        """
-        # 🌔 moondream2
-        A tiny vision language model. [GitHub](https://github.com/vikhyat/moondream)
-        """
-    )
-    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()