version 3 working

Code/app.py

@@ -1,44 +1 @@
-import random
-
-# Sample dataset and transition matrix
-dataset = [
-    "Once upon a time",
-    "there was a cat",
-    "The cat loved to",
-    "explore the forest",
-    "One day, it found",
-    "a hidden treasure",
-    "The treasure was",
-    "guarded by a dragon",
-]
-
-transition_matrix = {
-    "Once upon a time": {"there was a cat": 1.0},
-    "there was a cat": {"The cat loved to": 1.0},
-    "The cat loved to": {"explore the forest": 1.0},
-    "explore the forest": {"One day, it found": 1.0},
-    "One day, it found": {"a hidden treasure": 1.0},
-    "a hidden treasure": {"The treasure was": 1.0},
-    "The treasure was": {"guarded by a dragon": 1.0},
-    "guarded by a dragon": {"The end": 1.0},
-}
-
-# Generate a story
-def generate_story(transition_matrix, initial_state, length=5):
-    current_state = initial_state
-    story = [current_state]
-
-    for _ in range(length):
-        next_state_options = transition_matrix.get(current_state, {})
-        if not next_state_options:
-            break
-        next_state = random.choices(list(next_state_options.keys()), list(next_state_options.values()))[0]
-        story.append(next_state)
-        current_state = next_state
-
-    return " ".join(story)
-
-# Generate a story starting from a specific state
-initial_state = "Once upon a time"
-generated_story = generate_story(transition_matrix, initial_state)
-print(generated_story)
+print("Hello world!")

app.py

@@ -37,7 +37,6 @@ if btn and not flag:
 #     for word in response.split():
 #         yield word + " "
 #         time.sleep(0.05)
-from util import generate_assistant_response
 
 st.title("Nile")
 
@@ -52,6 +51,7 @@ for message in st.session_state.messages:
 
 # Accept user input
 if prompt := st.chat_input("What's your question ?"):
+    from util import generate_assistant_response
     # Add user message to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})
     # Display user message in chat message container

util.py

@@ -65,12 +65,6 @@ with open("Code.txt", "w", encoding='utf-8') as output:
                     code = file.read()
                     output.write(f"Filepath: {filepath}:\n\n")
                     output.write(code + "\n\n")
-            elif filename.endswith((".txt")):
-                filepath = os.path.join(directory_path, filename)
-                with open(filepath, "r", encoding="utf-8") as file:
-                    code = file.read()
-                    output.write(f"Documentation list:\n\n")
-                    output.write(code + "\n\n")
 
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import TextLoader
@@ -83,9 +77,8 @@ pages = loader.load_and_split()
 
 # Split data into chunks
 text_splitter = RecursiveCharacterTextSplitter(
-chunk_size = 4000,
+chunk_size = 2000,
 chunk_overlap  = 20,
-length_function = len,
 add_start_index = True,
 )
 chunks = text_splitter.split_documents(pages)
@@ -103,7 +96,7 @@ retriever = vectordb.as_retriever(search_kwargs = {"k": 3})
 # Function to generate assistant's response using ask function
 def generate_assistant_response(question):
     context = retriever.get_relevant_documents(question)
-    qna_prompt_template= f"""### [INST] Instruction: You will be provided with questions and context. Your task is to find the answers to the questions using the given data. If the data doesn't contain the answer to the question, then you must return 'Not enough information.'
+    qna_prompt_template= f"""### [INST] Instruction: You will be provided with questions and context. Your task is to find the answers to the questions using the given data.'
     Context: ```
         {context}
     ```