functions.py

from config import *

@st.cache_data()
def make_request(user_input:str, 
                 short_history:list, 
                 chat_history:list):

    """Makes a request to the Hugging Face API"""

    client = InferenceClient(
        MODEL_PATH,
        token=HUGGING_FACE_API_KEY,
        )
    
    try:
        response = client.chat_completion(
                messages=short_history,
                max_tokens = 5000,
                stream = False,
                )
        
        # get the response
        message = response.choices[0].message['content']

        # analyse the content to see if there is an action to perform
        try:
            perform_actions = look_for_actions(user_input, message)

        except Exception as e:
            st.info(f"An error occurred while looking for actions: {e}")
            perform_actions = (False, None)

        # if there was an action to perform, resubmit the question to the chatbot:
        if perform_actions[0]:
            
            # replace the last message in the short history with the new message
            short_history[-1] = {'role':'user', 'content':perform_actions[1]}

            # replace the first message with the system prompt without url analysis
            short_history[0] = {'role':'system', 'content':SYSTEM_PROMPT_NO_URL}

            # wait a little bit to avoid the API limit
            time.sleep(1)

            # make the request again
            response = client.chat_completion(
                messages=short_history,
                max_tokens = 5000,
                stream = False,
                )
                
        # append to the history
        chat_history.append({'content':user_input, 'role':'user'})
        chat_history.append(response.choices[0].message) # append the response

        return chat_history

    except Exception as e:
        st.error(f"An error occurred: {e}")
        st.stop()

@st.cache_data()
def get_site_content(url:str):

    """Receives a URL and returns the content of the site"""

    # create an user agent
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
    }

    # get the site content
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')

    # remove styles and scripts
    for script in soup(["script", "style"]):
        script.extract()

    # let the meta descriptions of the header and all the content inside the body
    # for the meta tags, get the tag itself and its content
    meta_tags = soup.head.find_all('meta')
    meta_tags_text = ''
    for tag in meta_tags:
        meta_tags_text += f'<{tag.name} {tag.attrs}>\n'

    # get the body text
    body_text = soup.body.get_text()

    # join the meta tags and the body text
    text = f'{meta_tags_text}\n{body_text}'

    # remove empty lines
    text = os.linesep.join([s for s in text.splitlines() if s])

    return text

def look_for_actions(user_input:str, message:str):

    """Reveives a message and look for the pattern ###ACTION###function###URL###"""

    # check if the pattern is in the message. 
    if '###' in message:

        # split the message by the pattern ###ACTION###function###URL### to get the URL and the action
        split_string = message.split('###')

        if 'getSiteContent' in message:

            st.info("I need to visit the site to provide the answer. Please wait...")

            url = split_string[3].strip()

            # remove everything inside ### and ### (including the ###) from the user_input
            user_input = re.sub(r'###.*?###', '', user_input)

            # add the content of the website to the message
            url_content = f'{user_input}. Content of the site {url}:\n{get_site_content(url)}'

            # check if the url_content is too long. If soo, keep trimming the text until it is not too long
            while get_token_amount(url_content) > 5000:
                url_content = url_content[:-100]
            
            return (True, url_content)

    # if there is no action to perform, return None   
    return (False, None)

@st.cache_data(ttl=3600)
def get_token_amount(text,
                     model_name="gpt-4") -> int:
    
    """Uses the tiktoken library to check if a text is too long for a given model.
    Even tough we are using a Llama model, we are using the GPT-4 model ans an approximation.
    
    Args:
        text (str): The text to check.
        model_name (str): The name of the model to check. Defaults to "gpt-4".
        
    Returns:
        int: The number of tokens in the text.
    """

    encoding = tiktoken.encoding_for_model(model_name)
    tokens = encoding.encode(text)
    return len(tokens)