Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,35 +1,52 @@
-import streamlit as st
 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_llm import analyze_text
-from utils import get_embeddings, build_index, search_similar
-
-st.set_page_config(page_title="Brand Insight Companion", layout="wide")
-st.title("🔍 Real-Time Brand Insight Companion")
-
-query = st.text_input("Enter a brand or keyword (e.g., 'Nvidia')")
-if query:
-    with st.spinner("🔎 Searching social media posts..."):
-        posts = google_search(query, num=7)
-    
-    if not posts:
-        st.warning("❌ No posts found.")
-    else:
-        st.success(f"✅ Fetched {len(posts)} posts.")
-        summaries = [analyze_text(p[:1024]) for p in posts]
-
-        for i, summary in enumerate(summaries):
-            st.subheader(f"Post {i+1}")
-            st.markdown(summary)
-
-        st.markdown("---")
-        sim_query = st.text_input("Find similar posts to this topic")
-        if sim_query:
-            embeddings = get_embeddings(posts)
-            index = build_index(embeddings)
-            query_embedding = get_embeddings([sim_query])
-            dists, idxs = search_similar(index, query_embedding)
-
-            st.subheader("🔁 Similar Posts")
-            for i in idxs[0]:
-                st.markdown(f"- {posts[i]}")
+from mistral_llm import summarize_texts, detect_fake_news, analyze_sentiment
+
+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", label_visibility="visible")
+
+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}**..."):
+        t1 = time.time()
+        articles = google_search(query, num_results=10)
+        fetch_time = round(time.time() - t1, 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]
+    contents = [a['snippet'] for a in articles]
+
+    with st.spinner("🧠 Summarizing, classifying, and detecting fake news..."):
+        t2 = time.time()
+        summaries = summarize_texts(contents)
+        sentiments = analyze_sentiment(contents)
+        fakeness = detect_fake_news(contents)
+        process_time = round(time.time() - t2, 2)
+
+    st.info(f"⏱️ AI analysis completed in {process_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("---")