Spaces:
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davidm
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Commit
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dfbe641
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Parent(s):
aa4e0dd
initial commit
Browse files- Dockerfile +9 -0
- __init__.py +0 -0
- app.py +41 -0
- requirements.txt +7 -0
- setup.py +24 -0
Dockerfile
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FROM python:3.9-slim-buster
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COPY . /app
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WORKDIR /app
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RUN pip install --upgrade pip
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RUN pip install numpy==1.24.3
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RUN pip install -r requirements.txt
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EXPOSE 8501
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ENTRYPOINT ["streamlit","run"]
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CMD ["app.py"]
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__init__.py
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app.py
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import numpy as np
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import requests
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import streamlit as st
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def main():
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st.title("Scientific Question Generation")
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API_URL = "https://api-inference.huggingface.co/models/dhmeltzer/bart-large_askscience-qg"
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headers = {"Authorization": "Bearer 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"}
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def query(payload):
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response = requests.post(API_URL,
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headers=headers,
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json=payload)
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return response.json()
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# User search
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user_input = st.text_area("Question Generator",
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"""Black holes are the most \
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gravitationally dense objects in the universe.""")
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# Filters
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st.sidebar.markdown("**Filters**")
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temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.0,.1)
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num_results = st.sidebar.slider("Number of search results", 1, 50, 1)
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vector = query([user_input])
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if user_input:
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output = query({
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"inputs": user_input,
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"temperature":temperature,
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"wait_for_model":True})
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st.write(output)
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if __name__ == "__main__":
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main()
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requirements.txt
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datasets
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torch==1.13
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transformers==4.29.0
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faiss-cpu
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folium
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streamlit==1.14.0
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-e .
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setup.py
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from setuptools import setup
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common_kwargs = dict(
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version="0.1.0",
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license="MIT",
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author="David Meltzer",
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author_email="[email protected]",
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classifiers=[
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"Intended Audience :: Developers",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: MIT License",
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"Natural Language :: English",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3.9",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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],
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python_requires=">=3.9",
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include_package_data=False,
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)
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setup(
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name="asks_qg",
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**common_kwargs
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)
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