Spaces:
Runtime error
Runtime error
File size: 2,575 Bytes
685ba0e d6a25c5 30ad188 d6a25c5 685ba0e d6a25c5 685ba0e d6a25c5 685ba0e 9eebfee 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 685ba0e 30ad188 d6a25c5 685ba0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
from typing import Dict, List, Tuple, Union
import streamlit as st
from annotated_text import annotated_text
from analyzer import NewsAnalyzer
def run() -> None:
analyzer = NewsAnalyzer(
category_model_name="elozano/news-category",
fake_model_name="elozano/news-fake",
clickbait_model_name="elozano/news-clickbait",
ner_model_name="dslim/bert-base-NER",
)
st.title("📰 News Analyzer")
headline = st.text_input("Headline:")
content = st.text_input("Content:")
if headline == "":
st.error("Please, provide a headline.")
else:
if content == "":
st.warning(
"Please, provide both headline and content to achieve better results."
)
button = st.button("Analyze")
if button:
predictions = analyzer(headline=headline, content=content)
col1, _, col2 = st.columns([2, 1, 5])
with col1:
st.subheader("Analysis:")
category_prediction = predictions["category"]
st.markdown(
f"{category_prediction['emoji']} **Category**: {category_prediction['label']}"
)
clickbait_prediction = predictions["clickbait"]
st.markdown(
f"{clickbait_prediction['emoji']} **Clickbait**: {'Yes' if clickbait_prediction['label'] == 'Clickbait' else 'No'}"
)
fake_prediction = predictions["fake"]
st.markdown(
f"{fake_prediction['emoji']} **Fake**: {'Yes' if fake_prediction['label'] == 'Fake' else 'No'}"
)
with col2:
st.subheader("Headline:")
annotated_text(
*parse_entities(headline, predictions["ner"]["headline"])
)
st.subheader("Content:")
if content:
annotated_text(
*parse_entities(content, predictions["ner"]["content"])
)
else:
st.error("Content not provided.")
def parse_entities(
text: str, entities: Dict[str, Union[str, int]]
) -> List[Union[str, Tuple[str, str]]]:
start = 0
parsed_text = []
for entity in entities:
parsed_text.append(text[start : entity["start"]])
parsed_text.append((entity["word"], entity["entity_group"]))
start = entity["end"]
parsed_text.append(text[start:])
return parsed_text
if __name__ == "__main__":
run()
|