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| import streamlit as st | |
| import pandas as pd | |
| from openai import OpenAI | |
| from PyPDF2 import PdfReader | |
| from PIL import Image | |
| # source: eagle0504/document-search-q-series | |
| def read_and_textify_advanced(files, chunk_size): | |
| """ | |
| Reads PDF files and extracts text from each page, breaking the text into specified segments. | |
| This function iterates over a list of uploaded PDF files, extracts text from each page, | |
| and compiles a list of texts and corresponding source information, segmented into smaller parts | |
| of approximately 'chunk_size' words each. | |
| Args: | |
| files (List[st.uploaded_file_manager.UploadedFile]): A list of uploaded PDF files. | |
| chunk_size (int): The number of words per text segment. Default is 50. | |
| Returns: A list of strings, where each string is a segment of text extracted from a PDF page. | |
| """ | |
| text_list = [] # List to store extracted text segments | |
| # Iterate over each file | |
| for file in files: | |
| pdfReader = PdfReader(file) # Create a PDF reader object | |
| # Iterate over each page in the PDF | |
| for i in range(len(pdfReader.pages)): | |
| pageObj = pdfReader.pages[i] # Get the page object | |
| text = pageObj.extract_text() # Extract text from the page | |
| if text: | |
| # Split text into chunks of approximately 'chunk_size' words | |
| words = text.split(".") | |
| for j in range(0, len(words), chunk_size): | |
| # Get the chunk of text from j-chunk_size to j+chunk_size | |
| # start = max(0, j - chunk_size) | |
| # end = min(len(words), j + chunk_size + 1) | |
| chunk = ".".join(words[j:j+chunk_size]) + '.' | |
| chunk = chunk.strip() | |
| text_list.append(chunk) | |
| # Create a source identifier for each chunk and add it to the list | |
| else: | |
| # If no text extracted, still add a placeholder | |
| text_list.append("") | |
| pageObj.clear() # Clear the page object (optional, for memory management) | |
| return text_list | |
| def get_questions(context, instructions) -> str: | |
| """ | |
| Given a text context, generates a list of questions using OpenAI's GPT-3 API. | |
| Args: | |
| - context: A string representing the context for which questions should be generated. | |
| Returns: | |
| - A string containing the question generated by the API. | |
| """ | |
| try: | |
| response = client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"{instructions}\n\nText: {context}\n\nQuestions:\n"} | |
| ] | |
| ) | |
| # Extract question text from the response | |
| question_text = response.choices[0].message.content | |
| return question_text | |
| except: | |
| # Return an empty string if there was an error | |
| return "" | |
| def get_answers(row, instructions) -> str: | |
| """ | |
| Given a dataframe row containing context and questions, generates an answer using OpenAI's GPT-3 API. | |
| Args: | |
| - row: A pandas dataframe row containing 'context' and 'questions' columns. | |
| Returns: | |
| - A string containing the answer generated by the API. | |
| """ | |
| try: | |
| response = client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"{instructions}\n\nText: {row.context}\n\nQuestions:\n{row.questions}\n\nAnswers:\n"} | |
| ] | |
| ) | |
| # Extract answer text from the response | |
| answer_text = response.choices[0].message.content | |
| return answer_text | |
| except Exception as e: | |
| # Print the error message and return an empty string if there was an error | |
| print (e) | |
| return "" | |
| st.set_page_config(page_title="ChatbotGuide", layout="wide") | |
| st.title("Chatbot Guide") | |
| # Define the options in the dropdown menu | |
| app_options = [ | |
| "1) Scrape PDFs", | |
| "2) Create CSVs", | |
| "3) Merge CSVs", | |
| "4) Upload Datasets", | |
| "5) Create Chatbot" | |
| ] | |
| # Sidebar dropdown for selecting the application | |
| selected_app = st.sidebar.selectbox("Select Step (1-5)", app_options) | |
| # Clear session state when switching apps | |
| if 'last_selected_app' in st.session_state: | |
| if st.session_state.last_selected_app != selected_app: | |
| st.session_state.clear() | |
| st.session_state.last_selected_app = selected_app | |
| if 'submit' not in st.session_state: | |
| st.session_state.submit = False | |
| if 'error' not in st.session_state: | |
| st.session_state.error = "" | |
| if 'success' not in st.session_state: | |
| st.session_state.success = None | |
| if selected_app == "1) Scrape PDFs": | |
| st.write("1. Go to your organizations webpage") | |
| st.divider() | |
| st.write("2. Choose an section in the webpage (example below)") | |
| image = Image.open('Example1.png') | |
| st.image(image, use_column_width=True) | |
| st.divider() | |
| st.markdown("3. Open a new google doc") | |
| if selected_app == "2) Create CSVs": | |
| if st.session_state.error != "": | |
| st.error(st.session_state.error) | |
| if st.session_state.success != None: | |
| st.success("Success! Download the Q/A pairs below / Click reset to upload more PDFs") | |
| st.download_button( | |
| label=f"Download CSV: length = {st.session_state.success[1]}", | |
| data=st.session_state.success[0], | |
| file_name='questions_answers.csv', | |
| mime='text/csv', | |
| ) | |
| if st.button('Reset'): | |
| st.session_state.clear() | |
| st.rerun() | |
| else: | |
| uploaded_files = st.file_uploader("Upload PDFs Here", type="pdf", accept_multiple_files=True) | |
| question_protocol = st.text_input("Provide instructions for how questions should be generated", "Write a question based on the text") | |
| answer_protocol = st.text_input("Provide instructions for how answers should be generated", "Write an answer based on the text") | |
| sentence_chunks = st.number_input("Number sentences per Q/A pair", value=2, step=1, min_value=1, max_value=3) | |
| openai_api_key = st.text_input("Enter your OpenAI API key", type="password") | |
| submit = st.button("Submit") | |
| if submit: | |
| st.session_state.submit = True | |
| if st.session_state.submit: | |
| if uploaded_files: | |
| client = OpenAI(api_key=openai_api_key) | |
| with st.spinner("Loading, please be patient with us ... 🙏"): | |
| # test api key | |
| try: | |
| response = client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": "Say this is a test"} | |
| ] | |
| ) | |
| except: | |
| st.session_state.clear() | |
| st.session_state.error = "OpenAI API key is invalid" | |
| st.rerun() | |
| with st.spinner("Loading, please be patient with us ... 🙏"): | |
| textify_output = read_and_textify_advanced(uploaded_files, sentence_chunks) | |
| df = pd.DataFrame(textify_output) | |
| df.columns = ['context'] | |
| if question_protocol == "": | |
| question_protocol = "Write questions based on the text" | |
| df['questions'] = df.apply(lambda row: get_questions(row['context'], question_protocol), axis=1) | |
| if answer_protocol == "": | |
| answer_protocol = "Write answers based on the text" | |
| df['answers'] = df.apply(lambda row: get_answers(row, answer_protocol), axis=1) | |
| df = df.drop('context', axis=1) | |
| length = len(df) | |
| csv = df.to_csv(index=False).encode('utf-8') | |
| st.session_state.clear() | |
| st.session_state.success = (csv, length) | |
| st.rerun() | |
| else: | |
| st.session_state.clear() | |
| st.session_state.error = "Please upload at least 1 PDF" | |
| st.rerun() | |
| if selected_app == "3) Merge CSVs": | |
| if st.session_state.error != "": | |
| st.error(st.session_state.error) | |
| if st.session_state.success != None: | |
| st.success("Success! Download the merged CSV with Q/A pairs below / Reset to merge more CSVs") | |
| st.download_button( | |
| label=f"Download CSV: length = {st.session_state.success[1]}", | |
| data=st.session_state.success[0], | |
| file_name='questions_answers.csv', | |
| mime='text/csv', | |
| ) | |
| if st.button('Reset'): | |
| st.session_state.clear() | |
| st.rerun() | |
| else: | |
| uploaded_files = st.file_uploader("Upload CSV files to merge", accept_multiple_files=True, type="csv") | |
| submit = st.button("Submit") | |
| if submit: | |
| st.session_state.submit = True | |
| if st.session_state.submit: | |
| if len(uploaded_files) > 1: | |
| dfs = [] | |
| for file in uploaded_files: | |
| df = pd.read_csv(file) | |
| if "questions" in df.columns and "answers" in df.columns: | |
| df = df[["questions", "answers"]] | |
| dfs.append(df) | |
| else: | |
| st.session_state.clear() | |
| st.session_state.error = "Please upload CSVs that have been generated from 1) Create CSV" | |
| st.rerun() | |
| df = pd.concat(dfs, ignore_index=True) | |
| length = len(df) | |
| csv = df.to_csv(index=False).encode('utf-8') | |
| st.session_state.clear() | |
| st.session_state.success = (csv, length) | |
| st.rerun() | |
| else: | |
| st.session_state.clear() | |
| st.session_state.error = "Please upload at least 2 CSVs to merge" | |
| st.rerun() | |
| if selected_app == "4) Upload Datasets": | |
| st.markdown("Go to this [google colab link](https://colab.research.google.com/drive/1eCpk9HUoCKZb--tiNyQSHFW2ojoaA35m) to get started") | |
| if selected_app == "5) Create Chatbot": | |
| if st.session_state.error != "": | |
| st.error(st.session_state.error) | |
| if st.session_state.success != None: | |
| st.success("Success! Copy/paste the requirements.txt and app.py files into your HuggingFace Space") | |
| st.write('requirements.txt') | |
| st.code(st.session_state.success[0], language='python') | |
| st.write('app.py') | |
| st.code(st.session_state.success[1], language='python') | |
| if st.button('Reset'): | |
| st.session_state.clear() | |
| st.rerun() | |
| else: | |
| organization_name = st.text_input("What is the name of your organization", "") | |
| num_domains = st.number_input("How many datasets do you have uploaded", value=1, step=1, min_value=1, max_value=10) | |
| st.divider() | |
| domain_info = [] | |
| for i in range(num_domains): | |
| domain_link = st.text_input(f"Please enter link to dataset {i+1} with the format username/dataset_name", "Example: KeshavRa/About_YSA_Database") | |
| domain_name = st.text_input(f"What should domain {i+1} be called in the chatbot itself", "Example: About YSA") | |
| domain_purpose = st.text_area(f"What is the purpose of domain {i+1}, provide example questions (this will be visible to users of the chatbot)", 'Example: On this page, you can learn about what YSA does, how YSA was started, the advisory board, and the programs we offer.\n\nExample Questions\n\n--> What is the purpose of Youth Spirit Artworks?\n\n--> Who created YSA?\n\n--> What is the Advisory Board for Youth Spirit Artworks?\n\n--> What are the three empowerment-focused program areas of YSA?') | |
| domain_instructions = st.text_input(f"What baseline instructions/specifications should be sent to ChatGPT to answer questions in domain {i+1}", "Example: You are an assistant to help the user learn more about Youth Spirit Artworks") | |
| domain = {"link": domain_link, "name": domain_name, "purpose": domain_purpose, "instructions": domain_instructions} | |
| domain_info.append(domain) | |
| st.divider() | |
| submit = st.button("Submit") | |
| if submit: | |
| st.session_state.submit = True | |
| if st.session_state.submit: | |
| if organization_name == "": | |
| st.session_state.clear() | |
| st.session_state.error = "Please enter an organization name" | |
| st.rerun() | |
| missing_info = [] | |
| for i in range(len(domain_info)): | |
| if domain_info[i]['link'] == "": | |
| missing_info.append(f"link to domain {i+1}") | |
| if domain_info[i]['name'] == "": | |
| missing_info.append(f"name for domain {i+1}") | |
| if domain_info[i]['purpose'] == "": | |
| missing_info.append(f"purpose for domain {i+1}") | |
| if domain_info[i]['instructions'] == "": | |
| missing_info.append(f"instructions for domain {i+1}") | |
| if missing_info: | |
| error = "Missing Info: " | |
| for info in missing_info: | |
| error += (info + ', ') | |
| st.session_state.clear() | |
| st.session_state.error = error | |
| st.rerun() | |
| requirements = ''' | |
| openai | |
| scipy | |
| streamlit | |
| chromadb | |
| datasets | |
| ''' | |
| app = f""" | |
| import os | |
| import streamlit as st | |
| from datasets import load_dataset | |
| import chromadb | |
| import string | |
| from openai import OpenAI | |
| import numpy as np | |
| import pandas as pd | |
| from scipy.spatial.distance import cosine | |
| from typing import Dict, List | |
| def merge_dataframes(dataframes): | |
| # Concatenate the list of dataframes | |
| combined_dataframe = pd.concat(dataframes, ignore_index=True) | |
| # Ensure that the resulting dataframe only contains the columns "context", "questions", "answers" | |
| combined_dataframe = combined_dataframe[['context', 'questions', 'answers']] | |
| return combined_dataframe | |
| def call_chatgpt(prompt: str, directions: str) -> str: | |
| ''' | |
| Uses the OpenAI API to generate an AI response to a prompt. | |
| Args: | |
| prompt: A string representing the prompt to send to the OpenAI API. | |
| Returns: | |
| A string representing the AI's generated response. | |
| ''' | |
| # Use the OpenAI API to generate a response based on the input prompt. | |
| client = OpenAI(api_key = os.environ["OPENAI_API_KEY"]) | |
| completion = client.chat.completions.create( | |
| model="gpt-3.5-turbo-0125", | |
| messages=[ | |
| {{"role": "system", "content": directions}}, | |
| {{"role": "user", "content": prompt}} | |
| ] | |
| ) | |
| # Extract the text from the first (and only) choice in the response output. | |
| ans = completion.choices[0].message.content | |
| # Return the generated AI response. | |
| return ans | |
| def openai_text_embedding(prompt: str) -> str: | |
| return openai.Embedding.create(input=prompt, model="text-embedding-ada-002")[ | |
| "data" | |
| ][0]["embedding"] | |
| def calculate_sts_openai_score(sentence1: str, sentence2: str) -> float: | |
| # Compute sentence embeddings | |
| embedding1 = openai_text_embedding(sentence1) # Flatten the embedding array | |
| embedding2 = openai_text_embedding(sentence2) # Flatten the embedding array | |
| # Convert to array | |
| embedding1 = np.asarray(embedding1) | |
| embedding2 = np.asarray(embedding2) | |
| # Calculate cosine similarity between the embeddings | |
| similarity_score = 1 - cosine(embedding1, embedding2) | |
| return similarity_score | |
| def add_dist_score_column( | |
| dataframe: pd.DataFrame, sentence: str, | |
| ) -> pd.DataFrame: | |
| dataframe["stsopenai"] = dataframe["questions"].apply( | |
| lambda x: calculate_sts_openai_score(str(x), sentence) | |
| ) | |
| sorted_dataframe = dataframe.sort_values(by="stsopenai", ascending=False) | |
| return sorted_dataframe.iloc[:5, :] | |
| def convert_to_list_of_dict(df: pd.DataFrame) -> List[Dict[str, str]]: | |
| ''' | |
| Reads in a pandas DataFrame and produces a list of dictionaries with two keys each, 'question' and 'answer.' | |
| Args: | |
| df: A pandas DataFrame with columns named 'questions' and 'answers'. | |
| Returns: | |
| A list of dictionaries, with each dictionary containing a 'question' and 'answer' key-value pair. | |
| ''' | |
| # Initialize an empty list to store the dictionaries | |
| result = [] | |
| # Loop through each row of the DataFrame | |
| for index, row in df.iterrows(): | |
| # Create a dictionary with the current question and answer | |
| qa_dict_quest = {{"role": "user", "content": row["questions"]}} | |
| qa_dict_ans = {{"role": "assistant", "content": row["answers"]}} | |
| # Add the dictionary to the result list | |
| result.append(qa_dict_quest) | |
| result.append(qa_dict_ans) | |
| # Return the list of dictionaries | |
| return result | |
| domain_info = {domain_info} | |
| st.sidebar.markdown('''This is a chatbot to help you learn more about {organization_name}''') | |
| domain = st.sidebar.selectbox("Select a topic", [d["name"] for d in domain_info]) | |
| special_threshold = 0.3 | |
| n_results = 3 | |
| clear_button = st.sidebar.button("Clear Conversation", key="clear") | |
| if clear_button: | |
| st.session_state.messages = [] | |
| st.session_state.curr_domain = "" | |
| for d in domain_info: | |
| if domain == d['name']: | |
| dataset = load_dataset(d['link']) | |
| initial_input = "Tell me about {organization_name}" | |
| # Initialize a new client for ChromeDB. | |
| client = chromadb.Client() | |
| # Generate a random number between 1 billion and 10 billion. | |
| random_number: int = np.random.randint(low=1e9, high=1e10) | |
| # Generate a random string consisting of 10 uppercase letters and digits. | |
| random_string: str = "".join( | |
| np.random.choice(list(string.ascii_uppercase + string.digits), size=10) | |
| ) | |
| # Combine the random number and random string into one identifier. | |
| combined_string: str = f"{{random_number}}{{random_string}}" | |
| # Create a new collection in ChromeDB with the combined string as its name. | |
| collection = client.create_collection(combined_string) | |
| st.title("{organization_name} Chatbot") | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if "curr_domain" not in st.session_state: | |
| st.session_state.curr_domain = "" | |
| init_messages = {{}} | |
| for d in domain_info: | |
| init_messages[d['name']] = d['purpose'] | |
| chatbot_instructions = {{}} | |
| for d in domain_info: | |
| chatbot_instructions[d['name']] = d['instructions'] | |
| # Embed and store the first N supports for this demo | |
| with st.spinner("Loading, please be patient with us ... 🙏"): | |
| L = len(dataset["train"]["questions"]) | |
| collection.add( | |
| ids=[str(i) for i in range(0, L)], # IDs are just strings | |
| documents=dataset["train"]["questions"], # Enter questions here | |
| metadatas=[{{"type": "support"}} for _ in range(0, L)], | |
| ) | |
| if st.session_state.curr_domain != domain: | |
| st.session_state.messages = [] | |
| init_message = init_messages[domain] | |
| st.session_state.messages.append({{"role": "assistant", "content": init_message}}) | |
| st.session_state.curr_domain = domain | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # React to user input | |
| if prompt := st.chat_input("Tell me about {organization_name}"): | |
| # Display user message in chat message container | |
| st.chat_message("user").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({{"role": "user", "content": prompt}}) | |
| question = prompt | |
| results = collection.query(query_texts=question, n_results=n_results) | |
| idx = results["ids"][0] | |
| idx = [int(i) for i in idx] | |
| ref = pd.DataFrame( | |
| {{ | |
| "idx": idx, | |
| "questions": [dataset["train"]["questions"][i] for i in idx], | |
| "answers": [dataset["train"]["answers"][i] for i in idx], | |
| "distances": results["distances"][0], | |
| }} | |
| ) | |
| # special_threshold = st.sidebar.slider('How old are you?', 0, 0.6, 0.1) # 0.3 | |
| # special_threshold = 0.3 | |
| filtered_ref = ref[ref["distances"] < special_threshold] | |
| if filtered_ref.shape[0] > 0: | |
| # st.success("There are highly relevant information in our database.") | |
| ref_from_db_search = filtered_ref["answers"].str.cat(sep=" ") | |
| final_ref = filtered_ref | |
| else: | |
| # st.warning( | |
| # "The database may not have relevant information to help your question so please be aware of hallucinations." | |
| # ) | |
| ref_from_db_search = ref["answers"].str.cat(sep=" ") | |
| final_ref = ref | |
| engineered_prompt = f''' | |
| Based on the context: {{ref_from_db_search}}, | |
| answer the user question: {{question}}. | |
| ''' | |
| directions = chatbot_instructions[domain] | |
| answer = call_chatgpt(engineered_prompt, directions) | |
| response = answer | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| with st.expander("See reference:"): | |
| st.table(final_ref) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({{"role": "assistant", "content": response}}) | |
| """ | |
| st.session_state.clear() | |
| st.session_state.success = (requirements, app) | |
| st.rerun() |