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import streamlit as st
import fitz # PyMuPDF
from transformers import pipeline
import spacy
import json
# ---------- INITIAL SETUP ----------
st.set_page_config(page_title="Police Feedback Analyzer", layout="wide")
st.title("🚓 Smart Analytics: Police Feedback Analyzer")
@st.cache_resource
def load_models():
sentiment_model = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
topic_model = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
nlp = spacy.load("en_core_web_trf")
return sentiment_model, topic_model, nlp
sentiment_model, topic_model, nlp = load_models()
# ---------- UTILITY FUNCTIONS ----------
def extract_text_from_pdf(uploaded_file):
text = ""
try:
with fitz.open(stream=uploaded_file.read(), filetype="pdf") as doc:
for page in doc:
text += page.get_text()
except Exception as e:
st.error(f"PDF extraction error: {e}")
return text.strip()
def extract_entities(text):
doc = nlp(text)
officers = []
departments = []
for ent in doc.ents:
if ent.label_ == "PERSON":
officers.append(ent.text)
elif "Police" in ent.text or "Station" in ent.text:
departments.append(ent.text)
return list(set(officers)), list(set(departments))
def analyze_feedback(text):
sentiment_result = sentiment_model(text[:512])[0] # analyze only first 512 chars
sentiment_score = sentiment_result["score"] if sentiment_result["label"] == "POSITIVE" else -sentiment_result["score"]
topics = topic_model(
text,
candidate_labels=["bravery", "community service", "investigation", "rescue", "discipline", "training", "traffic management"],
)
top_topics = [t for t, s in zip(topics["labels"], topics["scores"]) if s > 0.3]
officers, departments = extract_entities(text)
return {
"officers": officers,
"departments": departments,
"sentiment_score": round(sentiment_score, 3),
"sentiment_label": sentiment_result["label"],
"topics": top_topics,
}
# ---------- STREAMLIT UI ----------
uploaded_file = st.file_uploader("📤 Upload a feedback file (PDF or text)", type=["pdf", "txt"])
if uploaded_file:
if uploaded_file.type == "application/pdf":
text = extract_text_from_pdf(uploaded_file)
else:
text = uploaded_file.read().decode("utf-8")
if text:
st.subheader("📝 Extracted Text Preview")
st.text_area("Text Content", text[:1000] + "...", height=200)
with st.spinner("Analyzing feedback..."):
results = analyze_feedback(text)
st.success("✅ Analysis Complete")
col1, col2 = st.columns(2)
with col1:
st.markdown("### 👮♂️ Detected Officers")
st.write(results["officers"] or "No names detected")
st.markdown("### 🏢 Departments")
st.write(results["departments"] or "No departments detected")
with col2:
st.markdown("### 💬 Sentiment Analysis")
st.metric("Sentiment", results["sentiment_label"], results["sentiment_score"])
st.markdown("### 🏷️ Topic Tags")
st.write(", ".join(results["topics"]) if results["topics"] else "No clear topic found")
# Optional: Download JSON
st.download_button(
"⬇️ Download Results JSON",
data=json.dumps(results, indent=2),
file_name="feedback_analysis.json",
mime="application/json",
)
else:
st.warning("No text extracted. Try another file.")
else:
st.info("Upload a feedback file to begin analysis.")
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