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# !pip install -q -U google-generativeai
import time
import json
import pandas as pd
from google.colab import userdata
import google.generativeai as genai
def configure_genai():
# Used to securely store your API key
GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')
genai.configure(api_key=GOOGLE_API_KEY)
def generate_art_questions(data_json, model):
art_texts = []
i = 0
for data_entry in data_json:
# Prompt for generating questions based on art description and metadata
prompt = f"Following is the description and meta data of an art. Based on this generate 4 questions and corresponding answer. Denote each question as **Question and answer as **Answer. {data_entry}"
# Generate content using the model
response = model.generate_content(prompt)
art_texts.append(response.text)
i += 1
if i == 60:
print("60 completed")
i = 0
time.sleep(10)
return art_texts
def extract_qa_pairs(text):
qa_pairs = {}
current_q = 1
current_a = 1
for line in text.splitlines():
if line.startswith("**Question"):
qa_pairs[f"Question{current_q}"] = line.strip().split(":", 1)[1].strip("**") # Extract question
current_q += 1
elif line.startswith(("**Answer", "Answer")):
qa_pairs[f"Answer{current_a}"] = line.strip().split(":", 1)[1].strip("**") # Extract answer
current_a += 1
return qa_pairs
def create_qa_dataframe(art_texts):
df = pd.DataFrame(columns=["Question1", "Answer1", "Question2", "Answer2", "Question3", "Answer3", "Question4", "Answer4"])
for art_text in art_texts:
qa_dict = extract_qa_pairs(art_text)
if len(list(qa_dict.values())) == 8:
# Fill the DataFrame with extracted data
df.loc[len(df)] = list(qa_dict.values()) # Efficiently fill with all values
else:
df.loc[len(df)] = [None]*8
return df
def main():
# Configure GenAI
configure_genai()
# Initialize Generative Model
model = genai.GenerativeModel('gemini-pro')
# Read the data from CSV
data = pd.read_csv('/content/Data_for_questioning.csv')
# Convert DataFrame to JSON
data.to_json('final_data_json.json', orient='records', indent=4)
# Open the JSON file and load its contents
with open('/content/final_data_json.json', 'r') as f:
data_json = json.load(f)
# Generate questions for art descriptions
art_texts = generate_art_questions(data_json, model)
# Create DataFrame with questions and answers
df = create_qa_dataframe(art_texts)
# Save DataFrame to CSV
df.to_csv('QnA.csv', index=False)
if __name__ == "__main__":
main()
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