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import asyncio
import streamlit as st
from pydantic_ai import Agent
from pydantic_ai.models.groq import GroqModel
import nest_asyncio
import pdfplumber
import os
api_key = os.getenv("API_KEY")
data = []
#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
model = GroqModel('llama-3.1-70b-versatile', api_key = api_key)
def split_long_string(long_string, chunk_size=6000):
return [long_string[i:i+chunk_size] for i in range(0, len(long_string), chunk_size)]
async def ppt_content(data):
agent = Agent(model,system_prompt=(
"You are an expert in making power-point perssentation",
"Title Slide: Include the document's title, subtitle, author, and date.",
"Methodology Slide: Summarize the methodology in detail",
"Results Slide: Present key findings in detail in simple text and bullet points.",
"Discussion Slide: Summarize the implications and limitations.",
"Conclusion Slide: State the overall conclusion.",
"Reference Slide: Include all citations used.",
"Create 6 sliders",
))
listOfString = split_long_string("".join(data))
print(len(listOfString))
result_1 = agent.run_sync(user_prompt = listOfString[0])
result_2 = agent.run_sync(user_prompt = listOfString[1],message_history=result_1.new_messages())
result_3 = agent.run_sync(user_prompt = listOfString[1],message_history=result_2.new_messages())
print(result_3.data)
def ai_ppt(data):
asyncio.run(ppt_content(data=data))
def extract_data(feed):
with pdfplumber.open(feed) as pdf:
pages = pdf.pages
for p in pages:
data.append(p.extract_text())
return None
# if data is not None:
# st.caption(data)
# ai_ppt(data=data)
def main():
uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
if uploaded_file is not None:
extract_data(uploaded_file)
if st.button("Search"):
ai_ppt(data)
if __name__ == '__main__':
import asyncio
nest_asyncio.apply()
main()
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