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name: Validate PR | ||
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on: | ||
pull_request: | ||
branches: [ master ] | ||
types: [opened, synchronize, reopened, ready_for_review] | ||
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jobs: | ||
build: | ||
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runs-on: ubuntu-latest | ||
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strategy: | ||
matrix: | ||
python-version: [3.12] | ||
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steps: | ||
- name: Checkout repository | ||
uses: actions/checkout@v3 | ||
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- name: Install Ubuntu packages | ||
run: | | ||
sudo apt-get update | ||
sudo xargs -a packages.txt apt-get install -y | ||
shell: bash | ||
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- name: Set up Python ${{ matrix.python-version }} | ||
uses: actions/setup-python@v4 | ||
with: | ||
python-version: ${{ matrix.python-version }} | ||
architecture: 'x64' | ||
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- name: Install Python dependencies | ||
run: | | ||
python -m pip install --upgrade pip | ||
pip install -r requirements.txt | ||
- name: Validate dependencies with pip-check | ||
run: | | ||
pip install pip-check | ||
pip-check | ||
continue-on-error: false # Fail the workflow if dependencies are invalid | ||
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- name: Test Streamlit App | ||
run: | | ||
pip install streamlit | ||
streamlit run App.py --server.headless true --browser.gatherUsageStats false & | ||
sleep 10 # Wait for the app to start | ||
curl --retry 5 --retry-delay 5 http://localhost:8501 | ||
env: | ||
STREAMLIT_SERVER_HEADLESS: true |
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__pycache__ | ||
venv | ||
*.log |
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# Development dependencies for the project. | ||
# Includes Jupyter and other tools used during development, but not required for the Streamlit app in production. | ||
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jupyterlab | ||
notebook | ||
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# jupyterlab==4.2.5 | ||
# jupyterlab_pygments==0.3.0 | ||
# jupyterlab_server==2.27.3 | ||
# jupyterlab_widgets==3.0.13 |
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from transformers import pipeline | ||
import streamlit as st | ||
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@st.cache_resource(show_spinner=True) # Cache the model loading for faster performance | ||
def load_summarizer(): | ||
"""Load and cache the text summarization pipeline model.""" | ||
return pipeline("summarization", model="t5-small") | ||
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def generate_summary(text: str) -> str: | ||
"""Generate a summary for the given input text.""" | ||
summarizer = load_summarizer() | ||
summary = summarizer(text, max_length=150, min_length=30, do_sample=False) | ||
return summary[0]["summary_text"] |
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import streamlit as st | ||
from models.text_sumarization.predict import generate_summary | ||
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st.title("Text Summarization Tool") | ||
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st.write("Enter the text you'd like to summarize (minimum 50 words).") | ||
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user_input = st.text_area("Input Text", height=250) | ||
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# A button to initiate the summarization process | ||
if st.button("Summarize"): | ||
if len(user_input.split()) < 50: | ||
st.warning("Please enter at least 50 words for summarization.") | ||
else: | ||
# Show a spinner while the summarization is being processed | ||
with st.spinner("Summarizing..."): | ||
summary = generate_summary(user_input) # Call the function from predict.py | ||
st.subheader("Summary:") | ||
st.code(summary, language="text", wrap_lines=True) |
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