Spaces:
Sleeping
Sleeping
File size: 8,691 Bytes
32a98d6 f6f33c3 b1a0926 21c729c f6f33c3 a289e51 f6f33c3 740fc36 f6f33c3 f932e1e eda36d5 f6f33c3 f932e1e f6f33c3 32a98d6 0809a99 9a894ab c9d1679 fce100b 9a894ab 15fb029 9a894ab e7dc910 9a894ab e7dc910 9a894ab 0809a99 5040d0d 9a894ab 5040d0d 9a894ab 5040d0d 9a894ab e7dc910 15fb029 4f4ce1a 15fb029 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import streamlit as st
import pandas as pd
from openai import OpenAI
from PyPDF2 import PdfReader
# 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) Create CSVs",
"2) Merge CSVs",
"3) Upload Datasets",
"4) Create Chatbot"
]
# Sidebar dropdown for selecting the application
selected_app = st.sidebar.selectbox("Select Step (1-4)", 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 selected_app == "1) Create CSVs":
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 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="Download CSV",
data=st.session_state.success,
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)
# 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.error = "OpenAI API key is invalid"
st.session_state.success = None
st.session_state.submit = False
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)
csv = df.to_csv(index=False).encode('utf-8')
st.session_state.error = ""
st.session_state.success = csv
st.session_state.submit = False
st.rerun()
else:
st.session_state.error = "Please upload at least 1 PDF"
st.session_state.success = None
st.session_state.submit = False
st.rerun()
if selected_app == "2) Merge CSVs":
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:
a = 1
else:
st.session_state.error = "Please upload at least 2 CSVs to merge"
st.session_state.success = None
st.session_state.submit = False
st.rerun()
|