uc

app.py

@@ -1,4 +1,63 @@
 import streamlit as st
+from FlagEmbedding import BGEM3FlagModel
+from FlagEmbedding import FlagReranker
+import pandas as pd
+import numpy as np
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+@st.cache_resource
+def load_model():
+    return BGEM3FlagModel('BAAI/bge-m3',
+                        use_fp16=True)
+@st.cache_resource
+def load_reranker():
+    return FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True)
+
+@st.cache_data
+def load_df(path):
+    df = pd.read_csv(path)
+    return df
+@st.cache_data
+def load_embed(path):
+    embeddings_2 = np.load(path)
+    return embeddings_2
+
+model = load_model()
+reranker = load_reranker()
+
+df = load_df('D:/AI_Builder/BookDataFrame.csv')
+embeddings_2 = load_embed('D:/AI_Builder/BGE_embeddings_2.npy')
+
+st.header(":books: Book Identifier")
+
+k = 10
+with st.form(key='my_form'):
+	sen1 = st.text_area("Book description:")
+	submit_button = st.form_submit_button(label='Submit')
+
+if submit_button:
+    embeddings_1 = model.encode(sen1,
+                                batch_size=12,
+                                max_length=8192,
+                                )['dense_vecs']
+    similarity = embeddings_1 @ embeddings_2.T
+
+    top_k_qs = []
+    topk = np.argsort(similarity)[-k:]
+
+    for t in topk:
+        pred_sum = df['Summary'].iloc[t]
+        pred_ques = sen1
+        pred = [pred_ques, pred_sum]
+        top_k_qs.append(pred)
+    rrscore = reranker.compute_score(top_k_qs, normalize=True)
+    rrscore_index = np.argsort(rrscore)
+
+    pred_book = []
+    for rr in rrscore_index:
+        pred_book.append(f"{df['Book Name'][topk[rr]]} by {df['Book Author'][topk[rr]]}")
+
+    finalpred = []
+    pred_book.reverse()
+    st.write("Here is your prediction")
+    for n, pred in enumerate(pred_book):
+        st.write(f"{n+1}: {pred}")

requirements.txt

@@ -0,0 +1,4 @@
+FlagEmbedding==1.2.10
+numpy==1.26.4
+pandas==2.2.2
+streamlit==1.35.0