app.py

from setup_code import *  # This imports everything from setup_code.py

class Query_Agent:
    def __init__(self, pinecone_index, pinecone_index_python, openai_client) -> None:
        # TODO: Initialize the Query_Agent agent
        self.pinecone_index = pinecone_index
        self.pinecone_index_python = pinecone_index_python
        self.openai_client = openai_client
        self.query_embedding = None
        self.codbert_tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
        self.codebert_model = AutoModel.from_pretrained("microsoft/codebert-base")

    def get_codebert_embedding(self, code: str):
        inputs = self.codbert_tokenizer(code, return_tensors="pt", max_length=512, truncation=True)
        outputs = self.codebert_model(**inputs)
        cb_embedding = outputs.last_hidden_state.mean(dim=1)  # A simple way to pool the embeddings
        cb_embedding = cb_embedding.detach().numpy()
        cb_embedding = cb_embedding.tolist()
        cb_embedding = cb_embedding[0]
        return cb_embedding

    def get_openai_embedding(self, text, model="text-embedding-ada-002"):
        text = text.replace("\n", " ")
        return self.openai_client.embeddings.create(input=[text], model=model).data[0].embedding

    def query_vector_store(self, query, query_topic: str, index=None, k=5) -> str:
        if index == None:
            index = self.pinecone_index

        if query_topic == 'ml':
            self.query_embedding = self.get_openai_embedding(query)
        elif query_topic == 'python':
            index = self.pinecone_index_python
            self.query_embedding = self.get_codebert_embedding(query)

        def get_namespace(index):
            stat = index.describe_index_stats()
            stat_dict_key = stat['namespaces'].keys()

            stat_dict_key_list = list(stat_dict_key)
            first_key = stat_dict_key_list[0]

            return first_key

        ns = get_namespace(index)

        if query_topic == 'ml':
          matches_text = get_top_k_text(index.query(
            namespace=ns,
            top_k=k,
            vector=self.query_embedding,
            include_values=True,
            include_metadata=True
            )
          )
        elif query_topic == 'python':
          matches_text = get_top_filename(index.query(
            namespace=ns,
            top_k=k,
            vector=self.query_embedding,
            include_values=True,
            include_metadata=True
            )
          )

        return matches_text

    def process_query_response(self, head_agent, user_query, query_topic):

        # Retrieve the history related to the query_topic
        conversation = []
        index = head_agent.pinecone_index

        if query_topic == "ml":
            conversation = Head_Agent.get_history_about('ml')
        elif query_topic == 'python':
            conversation = Head_Agent.get_history_about('python')
            index = head_agent.pinecone_index_python

        # get matches from Query_Agent, which uses Pinecone
        user_query_plus_conversation = f"The current query is: {user_query}"
        if len(conversation) > 0:
            conversation_text = "\n".join(conversation)
            user_query_plus_conversation += f'The current conversation is: {conversation_text}'

        ## self.query_embedding is set here
        matches_text = self.query_vector_store(user_query_plus_conversation, query_topic, index)

        if head_agent.relevant_documents_agent.is_relevant(matches_text, user_query_plus_conversation) or contains_py_filename(matches_text):
            if query_topic == 'python':
                user_query = user_query + "  Please use Python Code in your response."
            response = head_agent.answering_agent.generate_response(user_query, matches_text, conversation, head_agent.selected_mode)
        else:
            prompt_for_gpt = f"Return a response to this query: {user_query} in the context of this conversation: {conversation}. Please use language appropriate for a {head_agent.selected_mode}."
            response = get_completion(head_agent.openai_client, prompt_for_gpt)
            response = "[EXTERNAL] " + response

        return response

class Answering_Agent:
    def __init__(self, openai_client) -> None:
        self.client = openai_client

    def generate_response(self, query, docs, conv_history, selected_mode):
        prompt_for_gpt = f"Based on this text in angle brackets: <{docs}>, please summarize a response to this query: {query} in the context of this conversation: {conv_history}. Please use language appropriate for a {selected_mode}. Do not use the phrase `in the context of conversation` in your response"
        return get_completion(self.client, prompt_for_gpt)

    def generate_response_topic(self, topic_desc, topic_text, conv_history, selected_mode):
      prompt_for_gpt = f"Please return a summary response on this topic: {topic_desc} using this text as best as possible {topic_text} in the context of this {conv_history}. Please use language appropriate for a {selected_mode}."
      return get_completion(self.client, prompt_for_gpt)

    def generate_image(self, text):
        if DEBUG:
            return None, ""

        dall_e_prompt_from_gpt = f"Based on this text, repeated here in double square brackets for your reference: [[{text}]], please generate a simple caption that I can use with dall-e to generate an instructional image."
        dall_e_text = get_completion(self.client, dall_e_prompt_from_gpt)

        # Write open_ai text
        with open("dall_e_prompts.txt", "a") as f:
          f.write(f"{dall_e_text}\n\n")

        # get image from dall-e
        image = Head_Agent.text_to_image(self.client, dall_e_text)

        # once u have get a caption from GPT
        image_caption_prompt = f"This text in double square brackets is used to prompt dall-e: [[{dall_e_text}]]. Please generate a simple caption that I can use to display with the image dall-e will create. Only return that caption."
        image_caption = get_completion(self.client, image_caption_prompt)
        #st.write(f"image_caption_prompt): {image_caption_prompt}")
        return (image, image_caption)

class Concepts_Agent:
    def __init__(self):
      self._df = pd.read_csv("concepts_final.csv")
      #self.topic_matrix = [[0] * 5 for _ in range(12)]

    def increase_cell(self, i, j):
      st.session_state.topic_matrix[i][j] += + 1

    def display_topic_matrix(self):
      headers = [f"Topic {i}" for i in range(1, 6)]
      row_indices = [f"{self._df['concept'][i-1]}" for i in range(1, 13)]

      topic_df = pd.DataFrame(st.session_state.topic_matrix, row_indices, headers)
      st.table(topic_df)

      st.write(f"Total Topics covered: {sum(sum(row) for row in st.session_state.topic_matrix)}")

    def display_topic_matrix(self):
      headers = [f"Topic {i}" for i in range(1, 6)]
      row_indices = [f"{self._df['concept'][i-1]}" for i in range(1, 13)]

      topic_df = pd.DataFrame(st.session_state.topic_matrix, row_indices, headers)
      st.table(topic_df)

      st.write(f"Total Topics covered: {sum(sum(row) for row in st.session_state.topic_matrix)}")

    def display_topic_matrix_star(self):
        headers = [f"Topic {i}" for i in range(1, 6)]
        row_indices = [f"{self._df['concept'][i-1]}" for i in range(1, 13)]

        # Replace 1 with the Unicode star symbol
        topic_matrix_star = [[chr(9733) if val == 1 else val for val in row] for row in st.session_state.topic_matrix]

        topic_df = pd.DataFrame(topic_matrix_star, row_indices, headers)
        st.table(topic_df)

        st.write(f"Total Topics covered: {sum(sum(row) for row in st.session_state.topic_matrix)}")

    def display_topic_matrix_as_image(self):
      headers = [f"Topic {i}" for i in range(1, 6)]
      row_indices = [f"{self._df['concept'][i-1]}" for i in range(1, 13)]
      topic_df = pd.DataFrame(st.session_state.topic_matrix, row_indices, headers)

      df_html = topic_df.to_html(index=False)

      # Create an image of the HTML table
      image = Image.new("RGB", (800, 600), color="white")  # Define image size
      draw = ImageDraw.Draw(image)
      draw.text((10, 10), df_html, fill="black")  # Position of the table in the image

      # Save the image to a byte stream
      image_byte_array = io.BytesIO()
      image.save(image_byte_array, format="PNG")
      image_byte_array.seek(0)

      # Now you can use the image_byte_array in Streamlit as an image
      st.image(image_byte_array, caption="DataFrame as Image")
      return image_byte_array

    # for each query_embedding, we will look through the df of concepts
    # we'll do a cosine_similarity of that query_embedding with each of the embeddings for each concept
    def find_top_concept_index(self, query_embedding):
      top_sim = 0
      top_concept_index = 0

      for index, row in self._df.iterrows():

        float_array = np.array(ast.literal_eval(row['embedding'])).reshape(1, -1)
        qe_array = np.array(query_embedding).reshape(1, -1)

        sim = cosine_similarity(float_array, qe_array)

        if sim[0][0] > top_sim:
          top_sim = sim[0][0]
          top_concept_index = index

      return top_concept_index

    def get_top_k_text_list(self, matches, k):
      text_list = []
      for i in range(0, k):
          text_list.append(matches.get('matches')[i]['metadata']['text'])
      return text_list

    def write_to_file(self, filename):
        self._df.to_csv(filename, index=False)  # Setting index=False to avoid writing row indices

class Head_Agent:
    def __init__(self, openai_key, pinecone_key) -> None:
        # TODO: Initialize the Head_Agent
        self.openai_key = openai_key
        self.pinecone_key = pinecone_key
        self.selected_mode = ""

        self.openai_client = OpenAI(api_key=self.openai_key)
        self.pc = Pinecone(api_key=self.pinecone_key)
        self.pinecone_index = self.pc.Index("index-600")
        self.pinecone_index_python = self.pc.Index("index-python-files")

        self.query_embedding_local = None
        self.setup_sub_agents()

    def setup_sub_agents(self):
        self.classify_agent = Classify_Agent(self.openai_client)
        self.query_agent = Query_Agent(self.pinecone_index, self.pinecone_index_python, self.openai_client)  # took away embeddings argument since not used
        self.answering_agent = Answering_Agent(self.openai_client)
        self.relevant_documents_agent = Relevant_Documents_Agent(self.openai_client)
        self.ca = Concepts_Agent()

    @staticmethod
    def get_conversation():
        # ... (code for getting conversation history)
        return Head_Agent.get_history_about()

    @staticmethod
    def get_history_about(topic=None):
        history = []

        for message in st.session_state.messages:
            role = message["role"]
            content = message["content"]

            if topic == None:
                if role == "user":
                    history.append(f"{content} ")
            else:
                if message["topic"] == topic:
                    history.append(f"{content} ")

        # st.write(f"user history in get_conversation is {history}")

        if history != None:
            history = history[-2:]

        return history

    @staticmethod
    def text_to_image(openai_client, text):
        model = "dall-e-3"
        size = "512x512"
        with st.spinner("Generating ..."):
          response = openai_client.images.generate(
              model=model,
              prompt = text,
              n=1,
              size="1024x1024"
          )
        image_url = response.data[0].url
        with urllib.request.urlopen(image_url) as image_url:
            img = Image.open(BytesIO(image_url.read()))

        return img

    def get_default_value(self, variable):
        if variable == "openai_model": return "gpt-3.5-turbo"
        elif variable == "messages": return []
        elif variable == "stage": return 0
        elif variable == "query_embedding": return None
        elif variable == "topic_matrix": return [[0] * 5 for _ in range(12)]
        else:
          st.write(f"Error: get_default_value, variable not defined: {variable}")
          return None

    def initialize_session_state(self):
        session_state_variables = ["openai_model", "messages", "stage", "query_embedding", "topic_matrix"]
        for variable in session_state_variables:
            if variable not in st.session_state:
                st.session_state[variable] = self.get_default_value(variable)

    def display_selection_options(self):
        modes = ['college student', 'middle school student', '1st grade student', 'high school student', 'grad student']
        self.selected_mode = st.selectbox("Select your education level:", modes)

    def display_chat_messages(self):
        # Display existing chat messages
        for message in st.session_state.messages:
            if message["role"] == "assistant":
                with st.chat_message("assistant"):
                    st.write(message["content"])
                    if message['image'] != None:
                        st.image(message['image'])
            else:
                with st.chat_message("user"):
                    st.write(message["content"])

    def main_loop(self):
        st.title("Machine Learning Text Guide Chatbot")

        self.initialize_session_state()
        self.display_selection_options()
        self.display_chat_messages()

        ### Wait for user input ###
        if user_query := st.chat_input("What would you like to chat about?"):
            with st.chat_message("user"): st.write(user_query)

            with st.chat_message("assistant"):
                response = ""; topic = None; image = None; caption = ""; st.session_state.stage = 0

                # Get the current conversation with new user query to check for users' intention
                conversation = self.get_conversation()
                user_query_plus_conversation = f"The current query is: {user_query}. The current conversation is: {conversation}"
                classify_query = self.classify_agent.classify_query(user_query_plus_conversation)

                if classify_query == general_greeting_num:
                    response = "How can I assist you today?"
                elif classify_query == general_question_num:
                    response = "Please ask a question about Machine Learning or Python Code."
                elif classify_query == obnoxious_num:
                    response = "Please dont be obnoxious."
                elif classify_query == progress_num:
                    self.ca.display_topic_matrix_star()
                elif classify_query == default_num:
                    response = "I'm not sure how to respond to that."
                elif classify_query == machine_learning_num:
                    response = self.query_agent.process_query_response(self, user_query, 'ml')
                    st.session_state.query_embedding = self.query_agent.get_openai_embedding(user_query)
                    image, caption = self.answering_agent.generate_image(response)
                    topic = "ml"
                    st.session_state.stage = 1
                elif classify_query == python_code_num:
                    response = self.query_agent.process_query_response(self, user_query, 'python')
                    image, caption = self.answering_agent.generate_image(response)
                    image = None
                    topic = "python"
                    st.session_state.stage = 0
                else:
                    response = "I'm not sure how to respond to that."

                # ... (get AI response and display it)
                st.write(response)
                if image and caption != "": st.image(image, caption)

                st.session_state.messages.append({"role": "user", "content": user_query, "topic": topic, "image": None})
                st.session_state.messages.append({"role": "assistant", "content": response, "topic": topic, "image": image})

        if st.session_state.stage == 1: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###

            # it looks like after we hit st.button, we go back to the top of the st.session_state.stage == 1 loop, and we lose the query_embedding_local

            # we use st.session_state.query_embedding to get the concept index
            top_concept_index = self.ca.find_top_concept_index(st.session_state.query_embedding)
            concept_name = self.ca._df['concept'][top_concept_index]

            st.write(f"Your question is associated to the Fundamental Concept in Machine Learning: {concept_name}.\n\n")
            st.write(f"Here are some topics you can explore to help you learn about {concept_name}, pick one.")

            response = ""; image = None; topic = ""
            topic0_desc = self.ca._df['topic_0_desc'][top_concept_index]
            topic1_desc = self.ca._df['topic_1_desc'][top_concept_index]
            topic2_desc = self.ca._df['topic_2_desc'][top_concept_index]
            topic3_desc = self.ca._df['topic_3_desc'][top_concept_index]
            topic4_desc = self.ca._df['topic_4_desc'][top_concept_index]

            matrix_row = st.session_state.topic_matrix[top_concept_index]

            if (matrix_row[0] == 0 and st.session_state.stage):
                if st.button(topic0_desc): process_button_click(self, 0, topic0_desc, top_concept_index)
            if (matrix_row[1] == 0 and st.session_state.stage):
                if st.button(topic1_desc): process_button_click(self, 1, topic1_desc, top_concept_index)
            if (matrix_row[2] == 0 and st.session_state.stage):
                if st.button(topic2_desc): process_button_click(self, 2, topic2_desc, top_concept_index)
            if (matrix_row[3] == 0 and st.session_state.stage):
                if st.button(topic3_desc): process_button_click(self, 3, topic3_desc, top_concept_index)
            if (matrix_row[4] == 0 and st.session_state.stage):
                if st.button(topic4_desc): process_button_click(self, 4, topic4_desc, top_concept_index)

def process_button_click(head, button_index, topic_desc, top_concept_index):
    with st.chat_message("user"): st.write(topic_desc)

    # we then assign to st.session_state.query_embedding the embedding for the topic_desc
    st.session_state.query_embedding = head.query_agent.get_openai_embedding(topic_desc)

    topic_text_index = 'topic_' + str(button_index)
    topic_text = head.ca._df[topic_text_index][top_concept_index]

    with st.chat_message("assistant"):
        response = head.answering_agent.generate_response_topic(topic_desc, topic_text, head.get_conversation(), head.selected_mode)
        image, caption = head.answering_agent.generate_image(topic_text)
        topic = topic_desc
    
        st.session_state.topic_matrix[top_concept_index][button_index] += 1
    
        st.write(response)
        if image and caption != "": st.image(image, caption)

    # ... (add response & image to message)
    st.session_state.messages.append({"role": "user", "content": topic_desc, "topic": "ml", "image": None})
    st.session_state.messages.append({"role": "assistant", "content": response, "topic": topic, "image": image})

    st.session_state.stage = 0


if __name__ == "__main__":
  head_agent = Head_Agent(OPENAI_KEY, pc_apikey)
  DEBUG = False

  head_agent.main_loop()
  #main()