app file

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+from FlagEmbedding import BGEM3FlagModel
+from FlagEmbedding import FlagReranker
+import pandas as pd
+import numpy as np
+
+@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_embed(path):
+    embeddings_2 = np.load(path)
+    return embeddings_2
+
+model = load_model()
+reranker = load_reranker()
+
+embeddings_2 = load_embed('D:/AI_Builder/BGE_embeddings_2.npy')
+
+data = pd.DataFrame(pd.read_csv('D:/AI_Builder/ActualProject/DataCollection/TESTUNCLEANbookquestions.csv'))
+data2 = pd.DataFrame(pd.read_csv('D:/AI_Builder/ActualProject/DataCollection/TRAINbookquestions.csv'))
+data3 = pd.read_csv("D:/AI_Builder/ActualProject/DataCollection/booksummaries.txt",
+                              header=None,sep="\t",
+                              names=["ID", "Freebase ID", "Book Name", "Book Author", "Pub date", "Genres", "Summary"])
+df = pd.concat([data, data2])
+df = df.merge(data3, on='ID', how='left')
+df = df.rename(columns={'Book Name_x': 'Book Name'})
+df = df[['ID', 'Book Name', 'Book Author', 'Questions', 'Summary']]
+
+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}")