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import streamlit as st
import PyPDF2
import pandas as pd
import os
# Function to extract text from the uploaded PDF
def extract_pdf_content(file):
reader = PyPDF2.PdfReader(file)
content = ""
for page_num in range(len(reader.pages)):
content += reader.pages[page_num].extract_text()
return content
# Function to generate questions and answers using dummy data
def generate_qa(content, topic, num_questions, answer_type, custom_conditions):
questions = []
answers = []
for i in range(1, num_questions + 1):
questions.append(f"Sample question {i} about {topic}")
answers.append(f"Sample {answer_type.lower()} answer for question {i}")
return questions, answers
# Function to convert questions and answers into a CSV format and save to server
def save_to_csv(questions, answers):
df = pd.DataFrame({'Questions': questions, 'Answers': answers})
# Specify a directory to save the CSV file
directory = "saved_files"
os.makedirs(directory, exist_ok=True) # Create directory if it doesn't exist
file_path = os.path.join(directory, 'questions_answers.csv') # Full path to save
df.to_csv(file_path, index=False)
return file_path
# Streamlit app structure
def app():
# Logo and Title beside each other with slight upward adjustment
col1, col2 = st.columns([1, 3]) # Adjust column width ratio
with col1:
st.image("C://Users//hafee//Downloads//Logo.jpeg", width=150) # Update with your local logo path
with col2:
st.markdown(
"<h1 style='text-align: left; margin-top: -10px;'>Synthetic Data Generator</h1>",
unsafe_allow_html=True
)
# Slogan below the title
st.markdown("<h2 style='text-align: center;'>Your Reliable Synthetic Dataset Generation</h2>", unsafe_allow_html=True)
# File Upload for PDF
file = st.file_uploader("Drag your Content or Document (.pdf only)", type=['pdf'])
# Topic input
topic = st.text_input("Topic Name", placeholder="Enter the topic name")
# Number of questions input
num_questions = st.number_input("Number of Questions", min_value=1, max_value=100, value=5, step=1)
# Answer type selection (horizontal)
answer_type = st.radio("Answer Type", options=["One-word", "Short", "Long"], index=1, horizontal=True)
# Custom conditions input
custom_conditions = st.text_area("Custom Conditions", placeholder="Enter any custom rules for the LLM...")
# Generate button
generate_button = st.button("Generate")
if generate_button and file and topic:
# Extract content from the uploaded PDF
content = extract_pdf_content(file)
# Generate questions and answers
questions, answers = generate_qa(content, topic, num_questions, answer_type, custom_conditions)
# Display the generated questions and answers
st.subheader("Generated Questions and Answers")
for i, (q, a) in enumerate(zip(questions, answers), start=1):
st.write(f"*Q{i}:* {q}")
st.write(f"*A{i}:* {a}")
st.write("---")
# Save to CSV and provide a download link
csv_file_path = save_to_csv(questions, answers)
# Provide a message to indicate where the file is saved
st.success(f"The CSV file has been saved to the server at: {csv_file_path}")
# Provide a download link for the CSV file
with open(csv_file_path, 'rb') as f:
st.download_button(
label="Download as CSV",
data=f,
file_name="questions_answers.csv",
mime="text/csv"
)
# Add a message below the download button
st.write("Click the button above to download your CSV file.")
# Run the Streamlit app
if _name_ == "_main_":
app() |