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  1. README.md +5 -5
  2. app.py +100 -0
  3. requirements.txt +10 -0
README.md CHANGED
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  ---
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  title: DeepSeek VL 1.3B Chat
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- emoji: 🏃
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- colorFrom: red
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- colorTo: gray
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  sdk: gradio
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- sdk_version: 5.14.0
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  app_file: app.py
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  pinned: false
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- license: mit
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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: DeepSeek VL 1.3B Chat
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+ emoji: 🐬 ⚡️ 🐬
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+ colorFrom: pink
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+ colorTo: blue
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  sdk: gradio
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+ sdk_version: 5.1.0
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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
app.py ADDED
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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 deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
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+ from deepseek_vl.utils.io import load_pil_images
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+ from io import BytesIO
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+ from PIL import Image
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+ import spaces # Import spaces for ZeroGPU support
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+
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+ # Load the model and processor
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+ model_path = "deepseek-ai/deepseek-vl-1.3b-chat"
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+ vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
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+ tokenizer = vl_chat_processor.tokenizer
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+
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+ # Define the function for image description with ZeroGPU support
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+ @spaces.GPU # Ensures GPU allocation for this function
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+ def describe_image(image, user_question="Describe this image in great detail."):
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+ try:
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+ # Convert the PIL Image to a BytesIO object for compatibility
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+ image_byte_arr = BytesIO()
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+ image.save(image_byte_arr, format="PNG") # Save image in PNG format
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+ image_byte_arr.seek(0) # Move pointer to the start
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+
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+ # Define the conversation, using the user's question
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+ conversation = [
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+ {
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+ "role": "User",
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+ "content": f"<image_placeholder>{user_question}",
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+ "images": [image_byte_arr] # Pass the image byte array instead of an object
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+ },
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+ {
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+ "role": "Assistant",
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+ "content": ""
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+ }
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+ ]
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+
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+ # Convert image byte array back to a PIL image for processing
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+ pil_images = [Image.open(BytesIO(image_byte_arr.read()))] # Convert byte back to PIL Image
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+ image_byte_arr.seek(0) # Reset the byte stream again for reuse
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+
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+ # Load images and prepare the inputs
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+ prepare_inputs = vl_chat_processor(
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+ conversations=conversation,
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+ images=pil_images,
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+ force_batchify=True
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+ ).to('cuda')
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+
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+ # Load and prepare the model
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+ vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda().eval()
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+
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+ # Generate embeddings from the image input
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+ inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
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+
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+ # Generate the model's response
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+ outputs = vl_gpt.language_model.generate(
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+ inputs_embeds=inputs_embeds,
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+ attention_mask=prepare_inputs.attention_mask,
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+ pad_token_id=tokenizer.eos_token_id,
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+ bos_token_id=tokenizer.bos_token_id,
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+ eos_token_id=tokenizer.eos_token_id,
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+ max_new_tokens=512,
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+ do_sample=False,
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+ use_cache=True
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+ )
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+
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+ # Decode the generated tokens into text
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+ answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
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+ return answer
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+
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+ except Exception as e:
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+ # Provide detailed error information
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+ return f"Error: {str(e)}"
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+
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+ # Gradio interface
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+ def gradio_app():
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+ with gr.Blocks() as demo:
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+ gr.Markdown("# Image Description with DeepSeek VL 1.3b 🐬\n### Upload an image and ask a question about it.")
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+
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+ with gr.Row():
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+ image_input = gr.Image(type="pil", label="Upload an Image")
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+ question_input = gr.Textbox(
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+ label="Question (optional)",
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+ placeholder="Ask a question about the image (e.g., 'What is happening in this image?')",
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+ lines=2
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+ )
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+
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+ output_text = gr.Textbox(label="Image Description", interactive=False)
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+
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+ submit_btn = gr.Button("Generate Description")
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+
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+ submit_btn.click(
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+ fn=describe_image,
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+ inputs=[image_input, question_input], # Pass both image and question as inputs
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+ outputs=output_text
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+ )
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+
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+ demo.launch()
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+
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+ # Launch the Gradio app
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+ gradio_app()
requirements.txt ADDED
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+ # Core requirements
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+ bitsandbytes
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+ transformers
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+ huggingface_hub
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+ accelerate
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+ gradio
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+ git+https://github.com/deepseek-ai/DeepSeek-VL
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+ spaces
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+ Pillow
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+ torch