update app.py

app.py

@@ -48,16 +48,11 @@ def display_chat_history():
                     message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs")
                     message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
 
-def create_conversational_chain(vector_store, text_chunks, embeddings):
+def create_conversational_chain(vector_store, text_chunks):
+    st.write("Text chunks lengths:", [len(chunk) for chunk in text_chunks])  # Add this line to print lengths
     replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl"  # Replace with your actual token
     os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
 
-    print("Length of text_chunks:", len(text_chunks))
-    print("Content of text_chunks:", text_chunks)
-
-    print("Length of embeddings:", len(embeddings))
-    print("Content of embeddings:", embeddings)
-
     llm = Replicate(
         streaming=True,
         model="replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
@@ -102,10 +97,12 @@ def main():
         text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
         text_chunks = text_splitter.split_documents(text)
 
+        # Create embeddings
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
         vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
 
-        chain = create_conversational_chain(vector_store, text_chunks, embeddings)
+        # Create the chain object
+        chain = create_conversational_chain(vector_store, text_chunks)
 
         display_chat_history()