Create app.py

app.py

@@ -0,0 +1,61 @@
+import pandas as pd
+import bm25s
+from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
+from langchain.docstore.document import Document
+import torch
+from langchain_ollama.llms import OllamaLLM
+from langchain.chains import LLMChain
+
+@st.cache_data
+def load_data():
+    retriever = bm25s.BM25.load("bm25s_very_big_index", mmap=True, load_corpus = True)
+    return retriever
+
+def load_model():
+    prompt = ChatPromptTemplate.from_messages([
+        HumanMessagePromptTemplate.from_template(
+        f"""
+        Extract the appropriate 8-digit HS Code base on the product description and retrieved document by thoroughly analyzing its details and utilizing a reliable and up-to-date HS Code database for accurate results.
+        Only return the HS Code as a 8-digit number .
+        Example: 1234567878
+        Context: {{context}}
+        Description: {{description}}
+        Answer:
+        """
+        )
+    ])
+    
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    llm = OllamaLLM(model="gemma2", temperature=0, device=device)
+
+    chain = prompt|llm
+    return chain
+
+def process_input(sentence):
+    docs, _ = retriever.retrieve(bm25s.tokenize(sentence), k=15)
+    documents =[]
+    for doc in docs[0]:
+        documents.append(Document(doc['text'])) 
+    return documents
+    
+if 'retriever' not in st.session_state:
+    st.session_state.retriever = None
+
+if 'chain' not in st.session_state:
+    st.session_state.chain = None
+    
+if st.session_state.retriever is None:
+    st.session_state.retriever = load_data()
+
+if st.session_state.chain is None:
+    st.session_state.chain = load_model()
+    
+sentence = st.text_input("please enter description:")
+
+if sentence !='':
+    documents = process_input(sentence)
+    hscode = chain.invoke({'context': documents,'description':sentence})
+    st.write("answer:",hscode)
+