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Browse files- README.md +13 -13
- app.py +166 -0
- requirements.txt +3 -0
README.md
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---
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title: AIDC-AI Ovis1.6-Gemma2-9B
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colorFrom: gray
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: AIDC-AI Ovis1.6-Gemma2-9B
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emoji: π¨
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colorFrom: gray
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colorTo: red
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import spaces
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import os
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import re
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import time
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM
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from transformers import TextIteratorStreamer
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from threading import Thread
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model_name = 'AIDC-AI/Ovis1.6-Gemma2-9B'
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# load model
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model = AutoModelForCausalLM.from_pretrained(model_name,
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torch_dtype=torch.bfloat16,
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multimodal_max_length=8192,
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trust_remote_code=True).to(device='cpu')
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text_tokenizer = model.get_text_tokenizer()
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visual_tokenizer = model.get_visual_tokenizer()
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streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True)
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image_placeholder = '<image>'
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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def submit_chat(chatbot, text_input):
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response = ''
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chatbot.append((text_input, response))
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return chatbot ,''
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# @spaces.GPU <-- Remove this line
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def ovis_chat(chatbot, image_input):
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# preprocess inputs
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conversations = []
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response = ""
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text_input = chatbot[-1][0]
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for query, response in chatbot[:-1]:
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conversations.append({
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"from": "human",
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"value": query
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})
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conversations.append({
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"from": "gpt",
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"value": response
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})
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text_input = text_input.replace(image_placeholder, '')
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conversations.append({
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"from": "human",
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"value": text_input
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})
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if image_input is not None:
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conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"]
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prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input])
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attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
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input_ids = input_ids.unsqueeze(0).to(device='cpu')
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attention_mask = attention_mask.unsqueeze(0).to(device='cpu')
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if image_input is None:
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pixel_values = [None]
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else:
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pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device='cpu')]
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with torch.inference_mode():
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gen_kwargs = dict(
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max_new_tokens=512,
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do_sample=False,
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top_p=None,
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top_k=None,
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temperature=None,
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repetition_penalty=None,
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eos_token_id=model.generation_config.eos_token_id,
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pad_token_id=text_tokenizer.pad_token_id,
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use_cache=True
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)
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response = ""
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thread = Thread(target=model.generate,
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kwargs={"inputs": input_ids,
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"pixel_values": pixel_values,
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"attention_mask": attention_mask,
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"streamer": streamer,
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**gen_kwargs})
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thread.start()
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for new_text in streamer:
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response += new_text
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chatbot[-1][1] = response
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yield chatbot
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thread.join()
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# debug
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print('*'*60)
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print('*'*60)
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print('OVIS_CONV_START')
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for i, (request, answer) in enumerate(chatbot[:-1], 1):
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print(f'Q{i}:\n {request}')
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print(f'A{i}:\n {answer}')
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print('New_Q:\n', text_input)
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print('New_A:\n', response)
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print('OVIS_CONV_END')
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def clear_chat():
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return [], None, ""
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with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file:
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svg_content = svg_file.read()
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font_size = "2.5em"
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svg_content = re.sub(r'(<svg[^>]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content)
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html = f"""
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<p align="center" style="font-size: {font_size}; line-height: 1;">
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<span style="display: inline-block; vertical-align: middle;">{svg_content}</span>
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<span style="display: inline-block; vertical-align: middle;">{model_name.split('/')[-1]}</span>
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</p>
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<center><font size=3><b>Ovis</b> has been open-sourced on <a href='https://huggingface.co/{model_name}'>π Huggingface</a> and <a href='https://github.com/AIDC-AI/Ovis'>π GitHub</a>. If you find Ovis useful, a likeβ€οΈ or a starπ would be appreciated.</font></center>
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"""
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latex_delimiters_set = [{
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"left": "\\(",
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"right": "\\)",
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"display": False
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}, {
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"left": "\\begin{equation}",
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"right": "\\end{equation}",
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"display": True
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}, {
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"left": "\\begin{align}",
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"right": "\\end{align}",
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"display": True
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}, {
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"left": "\\begin{alignat}",
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"right": "\\end{alignat}",
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"display": True
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}, {
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"left": "\\begin{gather}",
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"right": "\\end{gather}",
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"display": True
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}, {
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"left": "\\begin{CD}",
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"right": "\\end{CD}",
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"display": True
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}, {
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"left": "\\[",
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"right": "\\]",
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"display": True
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}]
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text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False)
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with gr.Blocks(title=model_name.split('/')[-1]) as demo:
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gr.HTML(html)
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with gr.Row():
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with gr.Column(scale=3):
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image_input = gr.Image(label="image", height=350, type="pil")
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gr.Examples(
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examples=[
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[f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."],
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[f"{cur_dir}/examples/case1.png", "explain this model to me."],
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[f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"],
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],
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inputs=[image_input, text_input]
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)
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with gr.Column(scale=7):
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chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set)
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text_input.render()
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with gr.Row():
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send_btn = gr.Button("Send", variant="primary")
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clear_btn = gr.Button("Clear", variant="secondary")
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send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
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submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
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clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input])
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demo.launch()
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requirements.txt
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numpy==1.24.3
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torch==2.2.0
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transformers==4.44.2
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