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148
client.py
Executable file
148
client.py
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import json
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import time
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import sys
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import os
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from gradio_client import Client, handle_file
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# Replace with the actual server URL if different
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ip = "127.0.0.1"
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port = "8080"
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# Define the user prompt (caption)
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user_prompt = "Thoroughly and carefully describe this image."
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files = []
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output_file = "output.json"
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# Hyperparameters
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temperature = 0.6
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top_k = 50
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top_p = 0.9
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max_tokens = 100
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startAt = 0
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argumentStart = 1
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if len(sys.argv) > 1:
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for i in range(0, len(sys.argv)):
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if sys.argv[i] == "--ip":
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ip = sys.argv[i + 1]
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argumentStart += 2
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if sys.argv[i] == "--directory":
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directory = sys.argv[i + 1]
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argumentStart += 2
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# Populate files with image (.jpg, .png) contents of directory
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if os.path.isdir(directory):
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directoryList = os.listdir(directory)
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directoryList.sort()
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for file in directoryList:
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if file.lower().endswith(('.jpg', '.png', '.jpeg', '.txt')):
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files.append(os.path.join(directory, file))
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else:
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print(f"Error: Directory '{directory}' does not exist.")
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sys.exit(1)
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elif sys.argv[i] == "--start":
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startAt = int(sys.argv[i + 1])
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argumentStart += 2
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elif sys.argv[i] == "--port":
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port = sys.argv[i + 1]
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argumentStart += 2
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elif sys.argv[i] == "--prompt":
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user_prompt = sys.argv[i + 1]
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argumentStart += 2
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elif sys.argv[i] == "--temperature":
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temperature = float(sys.argv[i + 1])
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argumentStart += 2
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elif sys.argv[i] == "--top_k":
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top_k = int(sys.argv[i + 1])
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argumentStart += 2
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elif sys.argv[i] == "--top_p":
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top_p = float(sys.argv[i + 1])
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argumentStart += 2
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elif sys.argv[i] == "--max_tokens":
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max_tokens = int(sys.argv[i + 1])
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argumentStart += 2
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elif sys.argv[i] in ("--output", "-o"):
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output_file = sys.argv[i + 1]
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argumentStart += 2
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# Initialize the Gradio client with the server URL
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client = Client(f"http://{ip}:{port}")
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results = {"prompt": user_prompt}
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for i in range(argumentStart, len(sys.argv)):
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files.append(sys.argv[i])
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# Make sure the list is sorted
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files.sort()
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# Possibly start at a specific index
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for i in range(startAt, len(files)):
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# Grab the next image path
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image_path = files[i]
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# Count start time
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start = time.time()
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# Make query to VLLM
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try:
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imageFile = None
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this_user_prompt = user_prompt
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if image_path.endswith('.txt'):
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with open(image_path, 'r') as txt_file:
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this_user_prompt = txt_file.read().strip()
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else:
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imageFile = handle_file(image_path)
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# Reset state
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result = client.predict(api_name="/reset_state" )
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# Send the image file path and the prompt to the Gradio app for processing
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result = client.predict(
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input_images=[imageFile], # Provide the file path directly
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input_text=this_user_prompt, # Adapted prompt parameter
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api_name="/transfer_input"
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)
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result = client.predict(
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chatbot=[],
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temperature=temperature,
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#top_k=top_k,
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top_p=top_p,
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max_length_tokens=max_tokens, # Adapted max_tokens parameter
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repetition_penalty=1.1,
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max_context_length_tokens=4096,
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#model_select_dropdown="deepseek-ai/deepseek-vl2-tiny",
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api_name="/predict"
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)
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except Exception as e:
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print(f"Failed to complete job at index {i}: {e}")
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output_file = f"partial_until_{i}_{output_file}"
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break
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# Calculate elapsed time
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seconds = time.time() - start
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remaining = (len(files) - i) * seconds
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hz = 1 / (seconds + 0.0001)
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# Output the result
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#print("result[0][0][1] ",result[0][0][1])
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question = this_user_prompt #Don't try to recover it from the list..
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response = result[0][0][1]
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# Print on screen
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print(f"Processing {1 + i}/{len(files)} | {hz:.2f} Hz / remaining {remaining / 60:.2f} minutes")
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print(f"Image: {image_path}\nResponse: {response}")
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# Store each path as the key pointing to each description
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results[image_path] = response
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# Save results to JSON
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print(f"\n\n\nStoring results in JSON file {output_file}")
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with open(output_file, "w") as outfile:
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json.dump(results, outfile, indent=4)
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68
scripts/linux_setup.sh
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68
scripts/linux_setup.sh
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#!/bin/bash
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd "$DIR"
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cd ..
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if [ -d venv/ ]
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then
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echo "Found a virtual environment"
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source venv/bin/activate
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else
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echo "Creating a virtual environment"
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#Simple dependency checker that will apt-get stuff if something is missing
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# sudo apt-get install python3-venv python3-pip
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SYSTEM_DEPENDENCIES="python3-venv python3-pip zip libhdf5-dev"
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for REQUIRED_PKG in $SYSTEM_DEPENDENCIES
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do
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PKG_OK=$(dpkg-query -W --showformat='${Status}\n' $REQUIRED_PKG|grep "install ok installed")
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echo "Checking for $REQUIRED_PKG: $PKG_OK"
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if [ "" = "$PKG_OK" ]; then
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echo "No $REQUIRED_PKG. Setting up $REQUIRED_PKG."
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#If this is uncommented then only packages that are missing will get prompted..
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#sudo apt-get --yes install $REQUIRED_PKG
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#if this is uncommented then if one package is missing then all missing packages are immediately installed..
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sudo apt-get install $SYSTEM_DEPENDENCIES
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break
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fi
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done
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#------------------------------------------------------------------------------
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python3 -m venv venv
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source venv/bin/activate
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fi
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#git clone https://github.com/deepseek-ai/DeepSeek-VL2
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#cd DeepSeek-VL2
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#python3 -m venv venv
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#source venv/bin/activate
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#Make sure pip is up to date
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python3 -m pip install --upgrade pip
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python3 -m pip install -e .
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python3 -m pip install -e .[gradio]
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python3 -m pip install joblib wheel
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python3 -m pip install flash-attn --no-build-isolation
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python3 -m pip install xformers
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python3 -m pip install --upgrade gradio
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#You can now run using :
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#CUDA_VISIBLE_DEVICES=2 python3 web_demo.py --model_name "deepseek-ai/deepseek-vl2-tiny" --port 8080
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echo "From now on you can run the web demo using: "
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DEMO_DIR=`pwd`
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echo "cd $DEMO_DIR"
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echo "source venv/bin/activate"
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echo "python3 web_demo.py --model_name \"deepseek-ai/deepseek-vl2-tiny\" --port 8080"
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exit 0
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@ -663,7 +663,8 @@ if __name__ == "__main__":
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demo.title = "DeepSeek-VL2 Chatbot"
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reload_javascript()
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demo.queue(concurrency_count=CONCURRENT_COUNT, max_size=MAX_EVENTS).launch(
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demo.queue(#concurrency_count=CONCURRENT_COUNT, #<- for some reason this emmits an error!
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max_size=MAX_EVENTS).launch(
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# share=False,
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share=True,
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favicon_path="deepseek_vl2/serve/assets/favicon.ico",
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