Update app.py

app.py

@@ -39,7 +39,7 @@ repo = Repository(
 repo.git_pull()  # Pull the latest changes (if any)
 
 # Step 2: Load the PDF File
-pdf_path = "Private_Book/181223_mitLG.pdf"  # Replace with your PDF file path
+pdf_path = "Private_Book/09012024_Kombi_2.pdf"  # Replace with your PDF file path
 
 # Step 2: Load the PDF File
 pdf_path2 = "Private_Book/Deutsche_Kodierrichtlinien_23.pdf"  # Replace with your PDF file path
@@ -53,29 +53,32 @@ api_key = os.getenv("OPENAI_API_KEY")
 # Updated caching mechanism using st.cache_data
 @st.cache_data(persist="disk")  # Using persist="disk" to save cache across sessions
 def load_vector_store(file_path, store_name, force_reload=False):
+    vector_store_path = f"{store_name}.pkl"
+
+    # Check if vector store already exists and force_reload is False
+    if not force_reload and os.path.exists(vector_store_path):
+        with open(vector_store_path, "rb") as f:
+            VectorStore = pickle.load(f)
+    else:
+        # Load and process the PDF, then create the vector store
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000, chunk_overlap=200, length_function=len)
+        text = load_pdf_text(file_path)
+        chunks = text_splitter.split_text(text=text)
+        embeddings = OpenAIEmbeddings()
+        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+
+        # Serialize the vector store
+        with open(vector_store_path, "wb") as f:
+            pickle.dump(VectorStore, f)
+
+        # Commit and push changes to the repository
+        repo.git_add(vector_store_path)
+        repo.git_commit(f"Update vector store: {store_name}")
+        repo.git_push()
+
+    return VectorStore
 
-        # Check if we need to force reload the vector store (e.g., when the PDF changes)
-        if force_reload or not os.path.exists(f"{store_name}.pkl"):
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=1000,
-                chunk_overlap=200,
-                length_function=len
-            )
-            
-            text = load_pdf_text(file_path)
-            chunks = text_splitter.split_text(text=text)
-            
-            embeddings = OpenAIEmbeddings()
-            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-            VectorStore.save_local("faiss_store")
-            FAISS.load_local("faiss_store", OpenAIEmbeddings())
-            with open(f"{store_name}.pkl", "wb") as f:
-                pickle.dump(VectorStore, f)
-        else:
-            with open(f"{store_name}.pkl", "rb") as f:
-                VectorStore = pickle.load(f)
-    
-        return VectorStore
 
 # Utility function to load text from a PDF
 def load_pdf_text(file_path):
@@ -119,6 +122,17 @@ def handle_no_answer(response):
         "i cannot answer that",
         "unable to provide an answer",
         "not enough context",
+        "Sorry, I do not have enough information",
+        "I do not have enough information",
+        "I don't have enough information",
+        "Sorry, I don't have enough context to answer that question.",
+        "I don't have enough context to answer that question.",
+        "to answer that question.",
+        "Sorry",
+        "I'm sorry",
+        "I don't understand the question",
+        "I don't understand"
+
     ]
 
     alternative_responses = [
@@ -184,12 +198,12 @@ def page1():
             col1, col2 = st.columns(2)
             
             with col1:
-                if st.button("Was kann ich mit dem Prognose-Analyse-Tool machen?"):
-                    query = "Was kann ich mit dem Prognose-Analyse-Tool machen?"
-                if st.button("Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"):
-                    query = "Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"
-                if st.button("Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"):
-                    query = "Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"
+                if st.button("Welche Geräte müssen für die LG Geriatrie vorgehalten werden? "):
+                    query = "Welche Geräte müssen für die LG Geriatrie vorgehalten werden? "
+                if st.button("Welche ärztlichen Vorgaben gibt es für die LG Palliativmedizin?"):
+                    query = "Welche ärztlichen Vorgaben gibt es für die LG Palliativmedizin?"
+                if st.button("Wie haben sich die DiGA in den letzten Jahren entwickelt? Kannst du mir Daten nennen?"):
+                    query = "Wie haben sich die DiGA in den letzten Jahren entwickelt? Kannst du mir Daten nennen?"
 
             
             with col2: