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import streamlit as st
import pdfplumber, re
from transformers import pipeline, AutoTokenizer
# ───────────────── Cached pipelines ────────────────────────────────────
@st.cache_resource(ttl=86400)
def load_pipes():
summarizer = pipeline("summarization", model=SUMM_MODEL)
tokenizer = AutoTokenizer.from_pretrained( SUMM_MODEL)
sentiment = pipeline("text-classification", model=SENT_MODEL)
ner = pipeline("token-classification", model=NER_MODEL,
aggregation_strategy="simple")
return summarizer, tokenizer, sentiment, ner
# ───────────────── Helper functions ────────────────────────────────────
def split_by_tokens(text, max_tokens):
words = re.split(r"(\s+)", text)
buf, n = "", 0
for w in words:
ln = len(TOK(w).input_ids)
if n + ln <= max_tokens:
buf, n = buf + w, n + ln
else:
yield buf.strip(); buf, n = w, ln
if buf.strip(): yield buf.strip()
def summarise(text):
parts = list(split_by_tokens(text, MAX_TOK))
per_len = max(25, min(80, TARGET_WORDS // max(1, len(parts))))
first = [SUMMAR(p, max_length=per_len,
min_length=per_len//2,
do_sample=False)[0]["summary_text"]
for p in parts]
joined = " ".join(first)
if len(joined.split()) > TARGET_WORDS:
joined = SUMMAR(joined, max_length=TARGET_WORDS,
min_length=TARGET_WORDS//2,
do_sample=False)[0]["summary_text"]
return joined
def shorten(summary, n):
s = summary.split(". ")
return (". ".join(s[:n]).rstrip(".") + ".") if len(s) > n else summary
def extract_pdf(file):
txt=""
with pdfplumber.open(file) as pdf:
for p in pdf.pages: txt += p.extract_text() or ""
return txt
def tag_entities(text):
tt = {"Organization":[], "Person":[], "Location":[], "Miscellaneous":[]}
for e in NER(text):
grp = {"ORG":"Organization","PER":"Person",
"LOC":"Location"}.get(e["entity_group"],"Miscellaneous")
tt[grp].append(e["word"])
return {k: sorted(set(v)) for k,v in tt.items() if v}
# ───────────────── Main Part ───────────────────────────────────────
st.set_page_config(page_title="Financial News Analyzer",
page_icon="📰",
layout="wide")
st.title("📰 Financial News Analyzer")
st.markdown("##### Instantly grasp news content, sentiment, and relevant entities")
# models and other constant variables
SUMM_MODEL = "sshleifer/distilbart-cnn-12-6"
SENT_MODEL = "nynn/Fintuned_Sentiment"
NER_MODEL = "Babelscape/wikineural-multilingual-ner"
SUMMAR, TOK, SENT_CLF, NER = load_pipes()
MAX_TOK = 1024
TARGET_WORDS = 225
LABEL_MAP = {"LABEL_0":"Negative","LABEL_1":"Positive","LABEL_2":"Neutral"}
COLOR_MAP = {"Positive":"green","Negative":"red","Neutral":"gray"}
# ───────────────── Sidebar input ───────────────────────────────────────
with st.sidebar:
st.header("Input News to Analyze:")
txt_input = st.text_area("Paste news article", height=150)
pdf_file = st.file_uploader("Or upload PDF", type=["pdf"])
sent_count = st.slider("Summary length (sentences)",
min_value=1, max_value=5, value=3, step=1)
run_btn = st.button("🔍 Analyze", use_container_width=True)
raw_text = extract_pdf(pdf_file) if pdf_file else txt_input.strip()
# ───────────────── Main pipeline ───────────────────────────────────────
if run_btn:
if not raw_text:
st.warning("Please provide text or a PDF first.")
st.stop()
with st.spinner("Analyzing"):
full_sum = summarise(raw_text)
summary = shorten(full_sum, sent_count)
cols = st.columns([2,1])
with cols[0]:
st.subheader("📝 Summary")
st.write(summary)
with cols[1]:
res = SENT_CLF(summary)[0]
label = LABEL_MAP.get(res["label"], res["label"])
colour= COLOR_MAP[label]
st.subheader("📊 Sentiment")
st.markdown(f"<h3 style='color:{colour};margin-bottom:0'>{label}</h3>"
f"{res['score']*100:.1f}% Confidence</p>",
unsafe_allow_html=True)
tags = tag_entities(summary)
st.subheader("🏷️ Relevant Tags")
if tags:
# CSS for the badge pills
pill_css = """
<style>
.tag-pill {
display: inline-block;
background: #f0f2f6;
color: #333;
padding: 4px 10px;
margin: 2px 4px 2px 0;
border-radius: 12px;
font-size: 0.9em;
}
.tag-cat {
font-weight: 600;
margin-top: 0;
margin-bottom: 4px;
}
</style>
"""
st.markdown(pill_css, unsafe_allow_html=True)
# Render each category as a header + pills
for category, vals in tags.items():
st.markdown(f"<div class='tag-cat'>{category}</div>", unsafe_allow_html=True)
pills = "".join(f"<span class='tag-pill'>{v}</span>" for v in vals)
st.markdown(pills, unsafe_allow_html=True)
else:
st.info("No entities detected.")
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