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Update app.py
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app.py
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@@ -66,54 +66,54 @@ A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on R
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## alpaca-lora
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assert (
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), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
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from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
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BASE_MODEL = "decapoda-research/llama-7b-hf"
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LORA_WEIGHTS = "tloen/alpaca-lora-7b"
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if device == "cuda":
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elif device == "mps":
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else:
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if device != "cpu":
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model.eval()
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if torch.__version__ >= "2":
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## FLAN-UL2
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@@ -223,10 +223,17 @@ theme = gr.themes.Monochrome(
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with gr.Blocks(theme=theme) as demo:
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with gr.Column():
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gr.Markdown(
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"""<h1><center>DePlot+LLM: Multimodal chain-of-thought reasoning on plots</center></h1>
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<p>
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This is a demo of DePlot+LLM for QA and summarisation. <a href='https://arxiv.org/abs/2212.10505' target='_blank'>DePlot</a> is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are <a href='https://huggingface.co/
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</p>
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"""
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)
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input_image = gr.Image(label="Input Image", type="pil", interactive=True)
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#input_image.style(height=512, width=512)
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instruction = gr.Textbox(placeholder="Enter your instruction/question...", label="Question/Instruction")
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llm = gr.Dropdown(["alpaca-lora", "flan-ul2", "gpt-3.5-turbo"], label="LLM")
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openai_api_key_textbox = gr.Textbox(value='',
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placeholder="Paste your OpenAI API key (sk-...) and hit Enter (if using OpenAI models, otherwise leave empty)",
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show_label=False, lines=1, type='password')
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## alpaca-lora
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# assert (
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# "LlamaTokenizer" in transformers._import_structure["models.llama"]
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# ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
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# from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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# tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
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# BASE_MODEL = "decapoda-research/llama-7b-hf"
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# LORA_WEIGHTS = "tloen/alpaca-lora-7b"
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# if device == "cuda":
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL,
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# load_in_8bit=False,
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# torch_dtype=torch.float16,
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# device_map="auto",
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# )
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# model = PeftModel.from_pretrained(
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# model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
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# )
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# elif device == "mps":
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL,
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# device_map={"": device},
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# torch_dtype=torch.float16,
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# )
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# model = PeftModel.from_pretrained(
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# model,
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# LORA_WEIGHTS,
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# device_map={"": device},
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# torch_dtype=torch.float16,
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# )
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# else:
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
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# )
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# model = PeftModel.from_pretrained(
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# model,
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# LORA_WEIGHTS,
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# device_map={"": device},
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# )
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# if device != "cpu":
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# model.half()
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# model.eval()
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# if torch.__version__ >= "2":
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# model = torch.compile(model)
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## FLAN-UL2
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with gr.Blocks(theme=theme) as demo:
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with gr.Column():
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# gr.Markdown(
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# """<h1><center>DePlot+LLM: Multimodal chain-of-thought reasoning on plots</center></h1>
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# <p>
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# This is a demo of DePlot+LLM for QA and summarisation. <a href='https://arxiv.org/abs/2212.10505' target='_blank'>DePlot</a> is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are <a href='https://huggingface.co/spaces/tloen/alpaca-lora' target='_blank'>alpaca-lora</a>, <a href='https://huggingface.co/google/flan-ul2' target='_blank'>flan-ul2</a>, and <a href='https://openai.com/blog/chatgpt' target='_blank'>gpt-3.5-turbo</a>. To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. Read more at the links below.
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# </p>
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# """
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# )
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gr.Markdown(
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"""<h1><center>DePlot+LLM: Multimodal chain-of-thought reasoning on plots</center></h1>
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<p>
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This is a demo of DePlot+LLM for QA and summarisation. <a href='https://arxiv.org/abs/2212.10505' target='_blank'>DePlot</a> is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are <a href='https://huggingface.co/google/flan-ul2' target='_blank'>flan-ul2</a> and <a href='https://openai.com/blog/chatgpt' target='_blank'>gpt-3.5-turbo</a>. To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. Read more at the links below.
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</p>
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"""
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)
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input_image = gr.Image(label="Input Image", type="pil", interactive=True)
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#input_image.style(height=512, width=512)
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instruction = gr.Textbox(placeholder="Enter your instruction/question...", label="Question/Instruction")
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#llm = gr.Dropdown(["alpaca-lora", "flan-ul2", "gpt-3.5-turbo"], label="LLM")
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llm = gr.Dropdown(["flan-ul2", "gpt-3.5-turbo"], label="LLM")
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openai_api_key_textbox = gr.Textbox(value='',
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placeholder="Paste your OpenAI API key (sk-...) and hit Enter (if using OpenAI models, otherwise leave empty)",
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show_label=False, lines=1, type='password')
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