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|
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{ |
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"nbformat": 4, |
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"nbformat_minor": 0, |
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"metadata": { |
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"colab": { |
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"provenance": [] |
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}, |
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"kernelspec": { |
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"name": "python3", |
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"display_name": "Python 3" |
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}, |
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"language_info": { |
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"name": "python" |
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} |
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}, |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"source": [ |
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"# Getting MOM from call transcripts" |
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], |
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"metadata": { |
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"id": "99Z21wE7xpKS" |
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} |
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}, |
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{ |
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"cell_type": "markdown", |
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"source": [ |
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"Import necessary libraries" |
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], |
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"metadata": { |
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"id": "YZMeexE8M_Pp" |
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} |
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}, |
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{ |
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"cell_type": "code", |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import requests\n", |
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"\n", |
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"from bs4 import BeautifulSoup\n", |
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"from IPython.display import Markdown, display\n", |
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"from openai import OpenAI\n" |
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], |
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"metadata": { |
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"id": "u5DCVg0Mxj5T" |
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}, |
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"execution_count": null, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": { |
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"id": "i0V11JQ2az-C" |
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}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"\n", |
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"#The below code can be uncommented in using .env file\n", |
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"\n", |
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"#from dotenv import load_dotenv\n", |
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"#load_dotenv(override=True)\n", |
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"#api_key = os.getenv('OPENAI_API_KEY')\n", |
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"\n", |
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"#I am using google colab to import api_key\n", |
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"from google.colab import userdata\n", |
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"api_key=userdata.get('gemini_api')\n", |
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"\n", |
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"# Check the key\n", |
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"if not api_key:\n", |
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
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"elif not api_key.startswith(\"sk-proj-\"):\n", |
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", |
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"elif api_key.strip() != api_key:\n", |
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
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"else:\n", |
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" print(\"API key found and looks good so far!\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"source": [ |
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"# A class to represet Transcript\n", |
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"from pathlib import Path\n", |
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"class Transcript:\n", |
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" def __init__(self, file_path):\n", |
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" self.file_path=file_path\n", |
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" self.content=Path(file_path).read_text(encoding='utf-8')\n" |
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], |
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"metadata": { |
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"id": "j6UTsnTEyWZ-" |
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}, |
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"execution_count": null, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"source": [ |
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"# Source of the text file -\"https://raw.githubusercontent.com/GeminiLn/EarningsCall_Dataset/refs/heads/master/3M%20Company_20170425/Text.txt\"\n", |
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"path = '/content/Text.txt' # Specify the path of file you want to use - format should be .txt\n", |
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"t=Transcript(path)\n" |
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], |
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"metadata": { |
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"id": "hquePU_mzZ7s" |
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}, |
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"execution_count": null, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"source": [ |
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"\n", |
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"system_prompt = \"You are expert at taking Meeting Notes & given the below transcript , create an MOM (Minutes of meeting)\"" |
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], |
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"metadata": { |
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"id": "ex5DB7M8L7KT" |
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}, |
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"execution_count": null, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"source": [ |
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"from google import genai\n", |
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"from google.genai import types\n", |
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"\n", |
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"client = genai.Client(api_key=api_key)\n", |
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"\n", |
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"response = client.models.generate_content(\n", |
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" model=\"gemini-2.0-flash\",\n", |
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" config=types.GenerateContentConfig(\n", |
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" system_instruction=system_prompt,\n", |
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" max_output_tokens=500,\n", |
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" temperature=0.1\n", |
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" ),\n", |
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" contents=t.content,\n", |
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")\n", |
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"\n", |
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"print(response.text)" |
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], |
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"metadata": { |
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"id": "wcpJ34qfMKmV" |
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}, |
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"execution_count": null, |
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"outputs": [] |
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} |
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] |
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} |
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Reference in new issue