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app.py
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@@ -1,12 +1,12 @@
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import asyncio
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from pydantic_ai.result import ResultData, RunResult
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import streamlit as st
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from pydantic_ai import Agent
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from pydantic_ai.models.groq import GroqModel
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import nest_asyncio
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import pdfplumber
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from transformers import pipeline
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import torch
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import os
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import presentation as customClass
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from streamlit_pdf_viewer import pdf_viewer
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@@ -23,11 +23,12 @@ model = GroqModel('llama-3.1-70b-versatile', api_key = api_key)
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# to summarize
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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#summarizer = pipeline('text2text-generation', model='describeai/gemini')
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#nlpaueb/legal-bert-base-uncased
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def split_long_string(long_string, chunk_size=3500):
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string_data = "".join(long_string)
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words = string_data.split()
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@@ -35,10 +36,16 @@ def split_long_string(long_string, chunk_size=3500):
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return chunks
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async def ppt_content(data):
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agent = Agent(model,
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system_prompt=(
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"You are an expert in making power-point perssentation",
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"Create 6 sliders",
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result_1 = agent.run_sync(user_prompt = ""
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st.text(result_1.data)
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print(result_1.data)
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def ai_ppt(data):
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#call summerizer to summerize pdf
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summary = summarizer("".join(data), max_length=400, min_length=100, truncation=True,do_sample=False)
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summary_texts = [item['summary_text'] for item in summary]
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#summary_texts = [item['generated_text'] for item in summary]
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asyncio.run(ppt_content(data=
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def extract_data(feed):
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import asyncio
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from pydantic_ai.result import ResultData, RunResult
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import streamlit as st
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from pydantic_ai import Agent,RunContext, Tool
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from pydantic_ai.models.groq import GroqModel
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import nest_asyncio
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import pdfplumber
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#from transformers import pipeline
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#import torch
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import os
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import presentation as customClass
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from streamlit_pdf_viewer import pdf_viewer
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# to summarize
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#summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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#summarizer = pipeline('text2text-generation', model='describeai/gemini')
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#nlpaueb/legal-bert-base-uncased
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def split_long_string(long_string, chunk_size=3500):
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string_data = "".join(long_string)
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words = string_data.split()
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return chunks
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def return_data() -> str:
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return "".join(data)
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async def ppt_content(data):
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agent = Agent(model,
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result_type=customClass.PPT,
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tools=[
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Tool(return_data,takes_ctx=False)
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],
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system_prompt=(
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"You are an expert in making power-point perssentation",
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"Create 6 sliders",
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result_1 = agent.run_sync(user_prompt = "Create a power point presentation with 6 slides")
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st.text(result_1.data)
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print(result_1.data)
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def ai_ppt(data):
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#call summerizer to summerize pdf
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# summary = summarizer("".join(data), max_length=400, min_length=100, truncation=True,do_sample=False)
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# summary_texts = [item['summary_text'] for item in summary]
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#summary_texts = [item['generated_text'] for item in summary]
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asyncio.run(ppt_content(data=data))
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def extract_data(feed):
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