Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,37 +2,38 @@
 import streamlit as st
 import pandas as pd
 import os
-import json
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-from datetime import datetime, timedelta
-import numpy as np
-import time
-import re
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-from alerts import compute_dynamic_risk,assign_dynamic_risk_level,trigger_alerts
-# Detect if running in Hugging Face Space
-IS_HF = os.getenv("SPACE_ID") or os.getenv("HF_SPACE_ID")
+import io
+import logging
+from contextlib import redirect_stdout
+
+from alerts import compute_dynamic_risk, assign_dynamic_risk_level, trigger_alerts
 
-# Optional: you can also check simulation flag if you imported from alerts.py
+IS_HF = os.getenv("SPACE_ID") or os.getenv("HF_SPACE_ID")
 if IS_HF:
     st.warning("🚨 Email alerts are simulated in this Hugging Face Space (no real emails sent).")
 else:
     st.info("📩 Real email alerts enabled (local environment).")
-    
+
+# Capture stdout and logs
+log_stream = io.StringIO()
+logging.basicConfig(stream=log_stream, level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
 from evaluation import evaluate_model
 
-# Run evaluation on the scraped CSV folder
-evaluate_model("drug_analysis_data_3months")
+# Redirect print statements to log_stream
+with redirect_stdout(log_stream):
+    evaluate_model("drug_analysis_data_3months")
 
 st.set_page_config(
     page_title="Twitter Drug Crime Monitoring",
     layout="wide",
     initial_sidebar_state="expanded"
 )
+
+st.subheader("📊 Scraper Evaluation Logs")
+st.text(log_stream.getvalue())
+
+
 # Import NLTK with error handling
 import nltk
 from nltk.corpus import stopwords