src/streamlit_app.py

import os
import time
import streamlit as st
from datetime import datetime
from dotenv import load_dotenv

from google_search import google_search
from mistral_ll import summarize_texts, detect_fake_news, analyze_sentiment

# Load environment variables
load_dotenv(dotenv_path=os.path.join(os.path.dirname(__file__), '.env'))

st.set_page_config(page_title="🔥 Brand Crisis Detector", layout="wide", page_icon="🔥")
st.title("🔥 Real-Time Brand Crisis Detector")
st.markdown("Analyze web content about your brand in real-time using AI ⚡")
st.caption(f"🕒 Last refreshed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")

query = st.text_input("🔍 Enter a brand or keyword", placeholder="e.g., Nvidia, Nestle, Jio")

if st.button("Start Analysis 🚀") or (query and st.session_state.get("last_query") != query):
    st.session_state.last_query = query

    with st.spinner(f"🔎 Searching Google for **{query}**..."):
        start_time = time.time()
        articles = google_search(query, num_results=10)
        fetch_time = round(time.time() - start_time, 2)

    if not articles:
        st.warning("❌ No results found. Try a different query.")
        st.stop()

    st.success(f"✅ Fetched {len(articles)} articles in {fetch_time} seconds.")

    titles = [a['title'] for a in articles]
    links = [a['link'] for a in articles]
    snippets = [a['snippet'] for a in articles]

    with st.spinner("🧠 Summarizing, classifying, and detecting fake news..."):
        start_ai = time.time()
        summaries = summarize_texts(snippets)
        sentiments = analyze_sentiment(snippets)
        fakeness = detect_fake_news(snippets)
        ai_time = round(time.time() - start_ai, 2)

    st.info(f"⏱️ AI analysis completed in {ai_time} seconds.")

    for i in range(len(articles)):
        with st.container():
            st.subheader(f"{i+1}. {titles[i]}")
            st.markdown(f"**🔗 Link:** [{links[i]}]({links[i]})")
            st.markdown(f"**💬 Sentiment:** `{sentiments[i]}`")
            st.markdown(f"**🕵️ Fake News Score:** `{fakeness[i]}`")
            st.markdown(f"**📝 Summary:** {summaries[i]}")
            st.markdown("---")
else:
    st.info("Enter a brand or keyword and click 'Start Analysis 🚀' to begin.")