Upload app3.py

app3.py

@@ -0,0 +1,267 @@
+import streamlit as st
+import numpy as np
+import time
+from sentence_transformers import SentenceTransformer
+import os
+import datetime
+import feedparser
+from huggingface_hub import hf_hub_download
+import faiss, pickle
+import aiohttp
+import asyncio
+
+# -------------------
+# Load prebuilt index
+# -------------------
+import sqlite3
+
+def init_cache_db():
+    conn = sqlite3.connect("query_cache.db")
+    c = conn.cursor()
+    c.execute("""
+        CREATE TABLE IF NOT EXISTS cache (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            query TEXT UNIQUE,
+            answer TEXT,
+            embedding BLOB,
+            frequency INTEGER DEFAULT 1
+        )
+    """)
+    conn.commit()
+    return conn
+
+cache_conn = init_cache_db()
+
+def store_in_cache(query, answer, embedding):
+    c = cache_conn.cursor()
+    c.execute("""
+        INSERT OR REPLACE INTO cache (query, answer, embedding, frequency)
+        VALUES (?, ?, ?, COALESCE(
+            (SELECT frequency FROM cache WHERE query=?), 0
+        ) + 1)
+        """,
+        (query, answer, embedding.tobytes(), query)
+    )
+    cache_conn.commit()
+
+
+def search_cache(query, embed_model, threshold=0.85):
+    q_emb = embed_model.encode([query], convert_to_numpy=True)[0]
+
+    c = cache_conn.cursor()
+    c.execute("SELECT query, answer, embedding, frequency FROM cache")
+    rows = c.fetchall()
+
+    best_sim = -1
+    best_row = None
+
+    for qry, ans, emb_blob, freq in rows:
+        emb = np.frombuffer(emb_blob, dtype=np.float32)
+        emb = emb.reshape(-1)
+
+        sim = np.dot(q_emb, emb) / (np.linalg.norm(q_emb) * np.linalg.norm(emb))
+
+        if sim > threshold and sim > best_sim:
+            best_sim = sim
+            best_row = (qry, ans, freq)
+
+    if best_row:
+        return best_row[1]  # return only answer
+
+    return None
+
+
+@st.cache_resource
+def load_index():
+    faiss_path = hf_hub_download(
+        repo_id="krishnasimha/health-chatbot-data",
+        filename="health_index.faiss",
+        repo_type="dataset"
+    )
+    pkl_path = hf_hub_download(
+        repo_id="krishnasimha/health-chatbot-data",
+        filename="health_metadata.pkl",
+        repo_type="dataset"
+    )
+
+    index = faiss.read_index(faiss_path)
+    with open(pkl_path, "rb") as f:
+        metadata = pickle.load(f)
+
+    embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+    return index, metadata, embed_model
+
+index, metadata, embed_model = load_index()
+
+# -------------------
+# FAISS Benchmark
+# -------------------
+def benchmark_faiss(n_queries=100, k=3):
+    queries = ["What is diabetes?", "How to prevent malaria?", "Symptoms of dengue?"]
+    query_embs = embed_model.encode(queries, convert_to_numpy=True)
+
+    times = []
+    for _ in range(n_queries):
+        q = query_embs[np.random.randint(0, len(query_embs))].reshape(1, -1)
+        start = time.time()
+        D, I = index.search(q, k)
+        times.append(time.time() - start)
+
+    avg_time = np.mean(times) * 1000
+    st.sidebar.write(f"⚡ FAISS Benchmark: {avg_time:.2f} ms/query over {n_queries} queries")
+
+# -------------------
+# Chat session management
+# -------------------
+if "chats" not in st.session_state:
+    st.session_state.chats = {}
+if "current_chat" not in st.session_state:
+    st.session_state.current_chat = "New Chat 1"
+    st.session_state.chats["New Chat 1"] = [
+        {"role": "system", "content": "You are a helpful public health awareness chatbot."}
+    ]
+
+st.sidebar.header("Chat Manager")
+
+if st.sidebar.button("➕ New Chat"):
+    chat_count = len(st.session_state.chats) + 1
+    new_chat_name = f"New Chat {chat_count}"
+    st.session_state.chats[new_chat_name] = [
+        {"role": "system", "content": "You are a helpful public health awareness chatbot."}
+    ]
+    st.session_state.current_chat = new_chat_name
+
+benchmark_faiss()
+
+chat_list = list(st.session_state.chats.keys())
+selected_chat = st.sidebar.selectbox("Your chats:", chat_list, index=chat_list.index(st.session_state.current_chat))
+st.session_state.current_chat = selected_chat
+
+new_name = st.sidebar.text_input("Rename Chat:", st.session_state.current_chat)
+if new_name and new_name != st.session_state.current_chat:
+    if new_name not in st.session_state.chats:
+        st.session_state.chats[new_name] = st.session_state.chats.pop(st.session_state.current_chat)
+        st.session_state.current_chat = new_name
+
+# -------------------
+# RSS News Fetcher (async)
+# -------------------
+RSS_URL = "https://news.google.com/rss/search?q=health+disease+awareness&hl=en-IN&gl=IN&ceid=IN:en"
+
+async def fetch_rss_url(url):
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as resp:
+            return await resp.text()
+
+def fetch_news():
+    raw_xml = asyncio.run(fetch_rss_url(RSS_URL))
+    feed = feedparser.parse(raw_xml)
+    articles = []
+    for entry in feed.entries[:5]:
+        articles.append({
+            "title": entry.title,
+            "link": entry.link,
+            "published": entry.published
+        })
+    return articles
+
+def update_news_hourly():
+    now = datetime.datetime.now()
+    if "last_news_update" not in st.session_state or (now - st.session_state.last_news_update).seconds > 3600:
+        st.session_state.last_news_update = now
+        st.session_state.news_articles = fetch_news()
+
+# -------------------
+# Async Together API
+# -------------------
+async def async_together_chat(messages):
+    url = "https://api.together.xyz/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {os.environ['TOGETHER_API_KEY']}",
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "model": "deepseek-ai/DeepSeek-V3",
+        "messages": messages,
+    }
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, headers=headers, json=payload) as resp:
+            result = await resp.json()
+            return result["choices"][0]["message"]["content"]
+
+# -------------------
+# Query function (async call inside)
+# -------------------
+def retrieve_answer(query, k=3):
+
+    # 1️⃣ Try fetch from cache
+    cached_answer = search_cache(query, embed_model)
+    if cached_answer:
+        st.sidebar.success("⚡ Retrieved from cache")
+        return cached_answer, []   # no FAISS sources
+
+    # 2️⃣ If no cache → normal FAISS pipeline
+    query_emb = embed_model.encode([query], convert_to_numpy=True)
+    D, I = index.search(query_emb, k)
+    retrieved = [metadata["texts"][i] for i in I[0]]
+    sources = [metadata["sources"][i] for i in I[0]]
+    context = "\n".join(retrieved)
+
+    user_message = {
+        "role": "user",
+        "content": f"Answer based on the context below:\n\n{context}\n\nQuestion: {query}"
+    }
+    st.session_state.chats[st.session_state.current_chat].append(user_message)
+
+    answer = asyncio.run(async_together_chat(st.session_state.chats[st.session_state.current_chat]))
+
+    # 3️⃣ Save the new query + embedding + answer into cache
+    store_in_cache(query, answer, query_emb[0])
+
+    st.session_state.chats[st.session_state.current_chat].append({"role": "assistant", "content": answer})
+    return answer, sources
+
+
+# -------------------
+# Background task for news refresh
+# -------------------
+async def background_news_updater():
+    while True:
+        st.session_state.news_articles = fetch_news()
+        await asyncio.sleep(3600)  # refresh every hour
+
+if "news_task" not in st.session_state:
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    st.session_state.news_task = loop.create_task(background_news_updater())
+
+# -------------------
+# Streamlit UI
+# -------------------
+st.title(st.session_state.current_chat)
+
+update_news_hourly()
+st.subheader("📰 Latest Health Updates")
+if "news_articles" in st.session_state:
+    for art in st.session_state.news_articles:
+        st.markdown(f"**{art['title']}**  \n[Read more]({art['link']})  \n*Published: {art['published']}*")
+        st.write("---")
+
+user_query = st.text_input("Ask me about health, prevention, or awareness:")
+
+if user_query:
+    with st.spinner("Searching knowledge base..."):
+        answer, sources = retrieve_answer(user_query)
+    st.write("### 💡 Answer")
+    st.write(answer)
+
+    st.write("### 📖 Sources")
+    for src in sources:
+        st.write(f"- {src}")
+
+for msg in st.session_state.chats[st.session_state.current_chat]:
+    if msg["role"] == "user":
+        st.write(f"🧑 **You:** {msg['content']}")
+    elif msg["role"] == "assistant":
+        st.write(f"🤖 **Bot:** {msg['content']}")