File size: 3,532 Bytes
b7709ec |
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 |
import streamlit as st
import base64
import pytesseract
from PIL import Image
from PyPDF2 import PdfReader
from openai import OpenAI
import os
# Retrieve the OpenAI API Key from the secrets
openai_api_key = st.secrets["OPENAI_API_KEY"]
client = OpenAI(api_key=openai_api_key)
# File upload
st.title("Functional Specification Document Processor")
uploaded_file = st.file_uploader("Upload a Functional Spec Document (PDF, TXT, JPG)", type=["pdf", "txt", "jpg", "jpeg"])
# Extract text from different file types
def extract_text(file):
text = ""
if file.type == "application/pdf":
reader = PdfReader(file)
for page in reader.pages:
if page and page.extract_text():
text += page.extract_text()
elif file.type == "text/plain":
text = str(file.read(), "utf-8")
elif file.type in ["image/jpeg", "image/jpg"]:
image = Image.open(file)
text = pytesseract.image_to_string(image)
return text
# Base64 encoding
def encode_base64(text):
return base64.b64encode(text.encode("utf-8")).decode("utf-8")
# Step 1: Identify epics, features, and user stories in a breadcrumb structure
def identify_structure(processed_text):
prompt = f"""
You are a product owner working from a functional specification document.
Analyze the document and identify the high-level epics, features, and detailed user stories.
Structure them in a clear breadcrumb format (Epic > Feature > User Stories). Include titles and short descriptions.
Document:
{processed_text}
"""
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
],
max_tokens=2000
)
return response.choices[0].message.content.strip()
# Step 2: Generate detailed outcomes for a specific user story
def generate_user_story_details(user_story):
prompt = f"""
You are a product owner. Based on the following user story, create a detailed functional breakdown.
Include:
- User story description
- Acceptance criteria
- Detailed steps and requirements
User Story:
{user_story}
"""
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
],
max_tokens=1500
)
return response.choices[0].message.content.strip()
if uploaded_file:
with st.spinner("Processing document..."):
extracted_text = extract_text(uploaded_file)
if extracted_text:
st.subheader("Identified Epics, Features, and User Stories")
structure = identify_structure(extracted_text)
st.text_area("Structure", structure, height=300)
user_story = st.text_input("Copy and paste a user story from the structure above to generate details:")
if user_story:
details = generate_user_story_details(user_story)
st.subheader("Detailed User Story Breakdown")
st.text_area("Details", details, height=400)
if st.button("Download as TXT"):
st.download_button(label="Download", data=details, file_name="user_story_details.txt", mime="text/plain")
else:
st.error("Failed to extract text from the document. Please try again with a clearer document.") |