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Browse files- Dockerfile +14 -0
- app.py +90 -0
- main.yml +27 -0
- requirements.txt +14 -0
Dockerfile
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# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
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# you will also find guides on how best to write your Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY . .
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CMD ["gunicorn", "-b" ,"0.0.0.0:7860","app:app", ]
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app.py
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from flask import Flask, request, jsonify
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import os
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import nltk
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from langchain.llms import LlamaCpp
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from langchain.callbacks.manager import CallbackManager
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from langchain.prompts import PromptTemplate
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from langchain.schema.output_parser import StrOutputParser
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nltk.download('punkt')
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app = Flask(__name__)
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# Download model
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if not os.path.exists('phi-2.Q4_K_M.gguf'):
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os.system('wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q4_K_M.gguf')
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# Disable GPU usage
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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# Callback manager setup
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callback_manager = CallbackManager([])
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# Creating LlamaCpp instance
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llm = LlamaCpp(
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model_path="phi-2.Q4_K_M.gguf",
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temperature=0.1,
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n_gpu_layers=0,
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n_batch=1024,
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callback_manager=callback_manager,
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verbose=True,
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n_ctx=2048
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)
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# Define templates
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templates = {
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"work_experience": """Instruction:
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Extract and summarize the work experience mentioned in the CV provided below. Focus solely on the details related to work history, including job titles, companies, and duration.
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Text: {text}
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Question: {question}
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Output:""",
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"certification": """Instruction:
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Extract and summarize the certification history mentioned in the CV provided below. Include details such as degrees earned, institutions attended, and graduation years.
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Text: {text}
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Question: {question}
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Output:""",
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"contact_info": """Instruction:
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Extract and provide the contact information mentioned in the CV provided below. Include details such as phone number, email address, and any other relevant contact links.
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Text: {text}
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Question: {question}
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Output:""",
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"skills": """Instruction:
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Focus solely on extracting the skills mentioned in the text below, excluding any other details or context. Your answer should consist of concise skills.
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Text: {text}
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Question: {question}
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Output:"""
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}
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@app.route('/', methods=['POST'])
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def generate_text():
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data = request.get_json()
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question = data.get('question')
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text = data.get('text')
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if not question or not text:
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return jsonify({"error": "Both 'question' and 'text' fields are required."}), 400
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if question == "Please summarize the work experience mentioned in the CV.":
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template_key = "work_experience"
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elif question == "Please summarize the certification history mentioned in the CV without repeating the output only once.":
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template_key = "certification"
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elif question == "Please extract the contact information mentioned in the CV once.":
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template_key = "contact_info"
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elif question == "What are the 6 skills? Please provide a concise short answer of the only(skills) mentioned in the text without repeating the answer.":
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template_key = "skills"
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else:
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return jsonify({"error": "Invalid question provided."}), 400
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prompt = PromptTemplate(template=templates[template_key], input_variables=["question", "text"])
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chain = prompt | llm | StrOutputParser()
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response = chain.invoke({"question": question, "text": text})
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return jsonify({"generated_text": response})
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if __name__ == '__main__':
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port = int(os.environ.get("PORT", 8000))
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app.run( port= 8000)
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main.yml
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name: Python application
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on:
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push:
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branches: [ main ]
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pull_request:
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branches: [ main ]
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python 3.x
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uses: actions/setup-python@v2
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with:
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python-version: '3.x'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Run the app
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run: python app.py
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requirements.txt
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flask
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langchain
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matplotlib
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numpy
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nltk
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gensim
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scikit-learn
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llama-cpp-python
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huggingface-hub
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langchain
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langchain-experimental
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scipy==1.10.1
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gunicorn
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langchain-community
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