Update app.py

app.py

@@ -83,6 +83,12 @@ attachments_knowledge = load_knowledge("knowledge/bartholomew_attachments_defini
 bigfive_knowledge = load_knowledge("knowledge/bigfive_definitions.txt")
 personalities_knowledge = load_knowledge("knowledge/personalities_definitions.txt")
 
+# Create vector stores
+embeddings = HuggingFaceEmbeddings()
+attachments_db = FAISS.from_texts([attachments_knowledge], embeddings)
+bigfive_db = FAISS.from_texts([bigfive_knowledge], embeddings)
+personalities_db = FAISS.from_texts([personalities_knowledge], embeddings)
+
 # Lazy initialization for retrieval chains
 class LazyChains:
     def __init__(self, lazy_llm):
@@ -95,9 +101,9 @@ class LazyChains:
     def get_chains(self):
         if self.attachments_chain is None:
             llm = self.lazy_llm.get_llm()
-            self.attachments_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=attachments_knowledge)
-            self.bigfive_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=bigfive_knowledge)
-            self.personalities_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=personalities_knowledge)
+            self.attachments_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=attachments_db.as_retriever())
+            self.bigfive_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=bigfive_db.as_retriever())
+            self.personalities_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=personalities_db.as_retriever())
         return self.attachments_chain, self.bigfive_chain, self.personalities_chain
 
 lazy_chains = LazyChains(lazy_llm)
@@ -159,7 +165,7 @@ def process_video(video_file):
 iface = gr.Interface(
     fn=process_video,
     inputs=gr.File(label="Upload Video File"),
-    outputs=gr.Textbox(label="Analysis Result"),
+    outputs=gr.Textbox(label="Results"),
     title="Video Analysis with Meta-Llama-3.1-8B-Instruct",
     description="Upload a video file to analyze using RAG techniques with Meta-Llama-3.1-8B-Instruct."
 )