54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import re
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import gradio as gr
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from moondream import VisionEncoder, TextModel
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from PIL import Image
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from huggingface_hub import snapshot_download
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from threading import Thread
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from transformers import TextIteratorStreamer
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model_path = snapshot_download("vikhyatk/moondream1")
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vision_encoder = VisionEncoder(model_path)
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text_model = TextModel(model_path)
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# model inference
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def moondream(img, prompt):
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image_embeds = vision_encoder(img)
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streamer = TextIteratorStreamer(text_model.tokenizer, skip_special_tokens=True)
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generation_kwargs = dict(
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image_embeds=image_embeds, question=prompt, streamer=streamer
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)
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thread = Thread(target=text_model.answer_question, kwargs=generation_kwargs)
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thread.start()
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buffer = ""
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for new_text in streamer:
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# check for the end of generated text and yield the generated token
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if not new_text.endswith("<") and not new_text.endswith("END"):
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buffer += new_text
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yield buffer
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else:
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new_text = re.sub("<$", "", re.sub("END$", "", new_text))
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buffer += new_text
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yield buffer
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# Using Gradio Blocks API
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with gr.Blocks() as demo:
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gr.HTML("<h1><center>🌔MoonDream</center></h1>")
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gr.HTML("<h3><center>A tiny vision language model. <a href='https://github.com/vikhyat/moondream' target='blank_'>GitHub</a></center></h3>")
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(label='Input Prompt for the model',placeholder='Type whatever you want to ask about the image',scale=4 )
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submit = gr.Button('Submit', scale=1,)
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with gr.Row():
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img = gr.Image(type='pil', label='Upload or Drag an Image')
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output = gr.TextArea(label="Bot's response to the user query-", info='The response might take a few seconds..' )
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# handling events
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submit.click(moondream, [img, prompt], output)
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prompt.submit(moondream, [img, prompt], output)
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# launch gradio demo with debug mode on
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demo.queue().launch(debug=True) |