Created this so that i have a backup in case my friend crashes my other site

app.py

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+import os
+import streamlit as st
+import faiss
+import pandas as pd
+import numpy as np
+import requests
+import torch
+import re
+import time
+from sentence_transformers import SentenceTransformer, CrossEncoder
+from huggingface_hub import hf_hub_download
+from langdetect import detect
+
+HF_REPO = "AKKI-AFK/deepshelf-data"
+
+books_file = hf_hub_download(repo_id=HF_REPO, filename="booksummaries.txt", repo_type="dataset")
+faiss_file = hf_hub_download(repo_id=HF_REPO, filename="faiss_index.bin", repo_type="dataset")
+
+df = pd.read_csv(books_file, delimiter="\t")
+index = faiss.read_index(faiss_file)
+
+encoder = SentenceTransformer("sentence-transformers/paraphrase-mpnet-base-v2")
+cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2")
+
+request_times = []  # Track request timestamps
+
+@st.cache_data
+def recommend_books(query):
+    query = sanitize_input(query)
+    if len(query) > 200:
+        st.warning("⚠️ Query is too long. Please keep it under 200 characters.")
+        return []
+    
+    if len(query) < 3:
+        st.warning("⚠️ Query is too short. Please provide more details.")
+        return []
+    
+    try:
+        lang = detect(query)
+        if lang != "en":
+            st.warning("⚠️ Non-English query detected. Results may not be accurate.")
+    except:
+        st.warning("⚠️ Could not detect language. Ensure proper input.")
+    
+    search_vector = encoder.encode(query)
+    search_vector = np.array([search_vector])
+    faiss.normalize_L2(search_vector)
+
+    distances, ann = index.search(search_vector, k=50)
+    results = pd.DataFrame({'distances': distances[0], 'ann': ann[0]})
+    merge = pd.merge(results, df, left_on='ann', right_index=True)
+    merge['Query'] = query
+
+    pairs = list(zip(merge['Query'], merge['summary']))
+    scores = cross_encoder.predict(pairs)  # Running synchronously
+    merge['score'] = scores
+
+    df_sorted = merge.iloc[merge["score"].argsort()][::-1]
+    return df_sorted[["title", "summary"]][:5].to_dict(orient="records")
+
+def sanitize_input(text):
+    """Sanitize input by removing special characters and excessive spaces."""
+    text = re.sub(r'[^\w\s]', '', text)  # Remove special characters
+    text = re.sub(r'\s+', ' ', text).strip()  # Normalize spaces
+    return text
+
+def rate_limit():
+    """Rate-limiting function to prevent excessive queries."""
+    global request_times
+    current_time = time.time()
+    request_times = [t for t in request_times if current_time - t < 10]  # Keep only recent requests within 10 seconds
+    if len(request_times) >= 5:
+        st.error("⚠️ Too many requests. Please wait a few seconds before trying again.")
+        return False
+    request_times.append(current_time)
+    return True
+
+st.set_page_config(page_title="DeepShelf", page_icon="📚", layout="wide")
+
+st.markdown("""
+    <style>
+        body {background-color: #1E1E1E; color: white;}
+        .title {text-align: center; font-size: 3em; font-weight: bold; color: #E6A400;}
+        .subtext {text-align: center; font-size: 1.2em; color: #AAAAAA;}
+        .recommend-btn {text-align: center;}
+        .book-container {border-radius: 10px; padding: 20px; margin: 10px; background: #2E2E2E; box-shadow: 2px 2px 10px #00000050;}
+        .book-title {font-size: 1.5em; font-weight: bold; color: #FFD700;}
+        .book-summary {font-size: 1em; color: #CCCCCC;}
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown('<div class="title">📖 DeepShelf</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtext">Find the best books based on your preferences!</div>', unsafe_allow_html=True)
+
+query = st.text_input("🔍 Enter a book description (e.g., 'A dark fantasy with drama')", max_chars=200, help="Use keywords to describe your ideal book!")
+button_disabled = not (3 <= len(query) <= 200)  # Disable button if query length is invalid
+
+button_placeholder = st.empty()
+
+if button_placeholder.button("✨ Recommend Books", disabled=button_disabled, help="Click to get personalized book recommendations!"):
+    if rate_limit():
+        button_placeholder.button("✨ Processing...", disabled=True)  # Disable button while processing
+        with st.spinner("🔍 Searching for the best books..."):
+            recommendations = recommend_books(query)
+        
+        button_placeholder.button("✨ Recommend Books", disabled=False)  # Re-enable button after processing
+        
+        st.markdown("## 📚 Recommended Books:")
+        for rec in recommendations:
+            st.markdown(f"""
+                <div class="book-container">
+                    <div class="book-title">📖 {rec["title"]}</div>
+                    <div class="book-summary">{rec["summary"]}</div>
+                </div>
+            """, unsafe_allow_html=True)