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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") | |
| 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.") | |