Update app.py

app.py

@@ -78,29 +78,6 @@ def process_file(file: IO):
 ### On Chat Start (Session Start) Section ###
 @cl.on_chat_start
 async def on_chat_start():
-    
-    # Adding cache!
-    store = LocalFileStore("./cache/")
-    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
-        core_embeddings, store, namespace=core_embeddings.model
-    )
-
-    # Typical QDrant Vector Store Set-up
-    vectorstore = QdrantVectorStore(
-        client=client,
-        collection_name=collection_name,
-        embedding=cached_embedder)
-    vectorstore.add_documents(docs)
-    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
-
-    retrieval_augmented_qa_chain = (
-        {"context": itemgetter("question") | retriever, "question": itemgetter("question")} ## 
-        | RunnablePassthrough.assign(context=itemgetter("context"))
-        | chat_prompt | chat_model
-    )
-    
-    cl.user_session.set("midterm_chain", retrieval_augmented_qa_chain)
-
     files = None
 
     # Wait for the user to upload a file
@@ -123,6 +100,28 @@ async def on_chat_start():
 
     # load the file
     docs = process_file(file)
+    
+    # Adding cache!
+    store = LocalFileStore("./cache/")
+    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings, store, namespace=core_embeddings.model
+    )
+
+    # Typical QDrant Vector Store Set-up
+    vectorstore = QdrantVectorStore(
+        client=client,
+        collection_name=collection_name,
+        embedding=cached_embedder)
+    vectorstore.add_documents(docs)
+    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
+
+    retrieval_augmented_qa_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")} ## 
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | chat_prompt | chat_model
+    )
+    
+    cl.user_session.set("midterm_chain", retrieval_augmented_qa_chain)
 
 ### Rename Chains ###
 @cl.author_rename