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{
"cells": [
{
"cell_type": "markdown",
"id": "c7d95a7f-205a-4262-a1af-4579489025ff",
"metadata": {},
"source": [
"# Hello everyone."
]
},
{
"cell_type": "markdown",
"id": "bc815dbc-acf7-45f9-a043-5767184c44c6",
"metadata": {},
"source": [
"I completed the day 1, first LLM Experiment moments ago and found it really awesome. After the challenge was done, I wanted to chip in my two cents by making a PDF summarizer, basing myself on the code for the Website Summarizer. I want to share it in this contribution!\n",
"### To consider:\n",
"* To extract the contents of PDF files, I used the PyPDF2 library, which doesn't come with the default configuration of the virtual environment. To remedy the situation, you need to follow the steps:\n",
" 1. Shut down Anaconda. Running `CTRL-C` in the Anaconda terminal should achieve this.\n",
" 2. Run the following command, `pip install PyPDF2 --user`\n",
" 3. Restart Jupyter lab with `jupyter lab`\n",
"* To find PDF files online, you can add `filetype:url` on your browser query, i.e. searching the following can give you PDF files to add as input: `AI Engineering prompts filetype:pdf`!\n",
"\n",
"Without further ado, here's the PDF Summarizer!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "14e98361-f302-423d-87a5-f2f5d570f4ac",
"metadata": {},
"outputs": [],
"source": [
"### Prerequisite - Install below package\n",
"#--> !pip install PyPDF2 --user"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "06b63787-c6c8-4868-8a71-eb56b7618626",
"metadata": {},
"outputs": [],
"source": [
"# Import statements\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"from io import BytesIO\n",
"from PyPDF2 import PdfReader\n",
"import logging"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "284ca770-5da4-495c-b1cf-637727a8609f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key found and looks good so far!\n"
]
}
],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv()\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" 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",
"elif api_key.strip() != api_key:\n",
" 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",
"else:\n",
" print(\"API key found and looks good so far!\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d4c316d7-d9c9-4400-b03e-1dd629c6b2ad",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()\n",
"\n",
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
"# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, or try the below line instead:\n",
"# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b6f2967e-4779-458f-aea8-c47147528f77",
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Defince calss for article pdf document\n",
"\n",
"class Article:\n",
" def __init__(self, url):\n",
" # Configure logging\n",
" logging.basicConfig(level=logging.ERROR)\n",
"\n",
" # Comprehensive headers to mimic browser\n",
" headers = {\n",
" 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',\n",
" 'Accept': 'application/pdf,text/html,application/xhtml+xml,application/xml',\n",
" 'Accept-Language': 'en-US,en;q=0.5',\n",
" 'Referer': self._extract_base_url(url)\n",
" }\n",
"\n",
" try:\n",
" # Enhanced download attempt with multiple strategies\n",
" response = self._download_pdf(url, headers)\n",
" \n",
" # Process PDF\n",
" pdf_bytes = BytesIO(response.content)\n",
" reader = PdfReader(pdf_bytes)\n",
" \n",
" # Efficient text extraction\n",
" self.text = \" \".join(page.extract_text() for page in reader.pages)\n",
" \n",
" # Safe metadata extraction\n",
" self.title = (reader.metadata or {}).get(\"/Title\", \"No title found\") or \"No title found\"\n",
"\n",
" except Exception as error:\n",
" logging.error(f\"PDF Processing Error: {error}\")\n",
" self.text = \"No text found\"\n",
" self.title = \"No title found\"\n",
"\n",
" def _download_pdf(self, url, headers):\n",
" \"\"\"\n",
" Multiple download strategies to handle different access scenarios\n",
" \"\"\"\n",
" try:\n",
" # Primary download attempt\n",
" response = requests.get(url, headers=headers, timeout=10)\n",
" response.raise_for_status()\n",
" return response\n",
"\n",
" except requests.RequestException as primary_error:\n",
" logging.warning(f\"Primary download failed: {primary_error}\")\n",
" \n",
" try:\n",
" # Fallback strategy: Disable SSL verification\n",
" response = requests.get(url, headers=headers, verify=False, timeout=10)\n",
" response.raise_for_status()\n",
" return response\n",
" \n",
" except requests.RequestException as fallback_error:\n",
" logging.error(f\"All download attempts failed: {fallback_error}\")\n",
" raise\n",
"\n",
" def _extract_base_url(self, url):\n",
" \"\"\"\n",
" Extract base URL for Referer header\n",
" \"\"\"\n",
" from urllib.parse import urlparse\n",
" parsed_url = urlparse(url)\n",
" return f\"{parsed_url.scheme}://{parsed_url.netloc}\""
]
},
{
"cell_type": "markdown",
"id": "95933aab-54df-4afc-a19c-46033f51378f",
"metadata": {},
"source": [
"#####user_prompt_input = input(\"Explain how you would like me to summarise the document? \\n\")"
]
},
{
"cell_type": "markdown",
"id": "1510e4df-b15f-4411-924c-feca1c72419a",
"metadata": {},
"source": [
"# General Research Analyst\n",
"#### Step 1: Create your prompts\n",
"\n",
"def craft_user_prompt(article):\n",
" user_prompt = f\"You are looking at a document titled {article.title}\\n Based on the body of the document, provide an impactful summary. The body of the article is as follows.\"\n",
" user_prompt += article.text\n",
" return user_prompt\n",
"\n",
"### Step 2: Make the messages list\n",
"def craft_messages(article):\n",
" system_prompt = '''You are a research assistant specialising in processing documents. You have been tasked with analysing the contents of the articel and call out the important points as bullets and highlights. \\\n",
" Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.'''\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f71a47af-5f16-4b8f-880d-7fc41727564b",
"metadata": {},
"outputs": [],
"source": [
"# Financial Report Analyst\n",
"#### Step 1: Create your prompts\n",
"\n",
"def craft_user_prompt(article):\n",
" user_prompt = f\"You are looking at a document titled {article.title}\\n Based on the body of the document, provide an impactful summary. Call out important points as bullets and highlughts . The body of the article is as follows.\"\n",
" user_prompt += article.text\n",
" return user_prompt\n",
"\n",
"#### Step 2: Make the messages list\n",
"def craft_messages(article):\n",
" system_prompt = '''You are a financial report research assistant that specialises in analysing the contents of a financial report and provide summary for investors to \\ \n",
" help adjust investment strategies. Call out important points as bullets and highlights. \\\n",
" Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.'''\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "81ab896e-1ba9-4964-a477-2a0608b7036c",
"metadata": {},
"outputs": [],
"source": [
"# Step 3: Call OpenAI\n",
"def summarize(url):\n",
" article = Article(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4o-mini\",\n",
" messages = craft_messages(article)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "markdown",
"id": "ccdfdd33-652f-4ecf-9a0d-355197aba9d5",
"metadata": {},
"source": [
"# Results below:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "008c03fd-9cc5-4449-a054-7bbef1863b01",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Please input the pdf URL: \n",
" https://www.spandanasphoorty.com/images_gallary/1714392044-256403-20242904050444-0195731001714392044.pdf\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/msachdeva/anaconda3/envs/llms/lib/python3.11/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'www.spandanasphoorty.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/markdown": [
"## Summary of Spandana Sphoorty Financial Limited's Financial Results for FY24\n",
"\n",
"### What are the key financial highlights for FY24?\n",
"- **Profit After Tax (PAT):** Achieved an all-time high of **₹501 Cr**, significantly up from **₹12 Cr** in FY23.\n",
"- **Assets Under Management (AUM):** Increased by **41%**, reaching **₹11,973 Cr**, up from **₹8,511 Cr** in FY23.\n",
"- **New Customer Acquisition:** Grew by **59%**, adding **13.9 lac** customers in FY24 compared to **8.8 lac** in FY23.\n",
"- **Disbursement:** Total of **₹10,688 Cr**, up **32%** from **₹8,125 Cr** in FY23.\n",
"- **Asset Quality:** \n",
" - **Gross Non-Performing Assets (GNPA):** Improved to **1.50%** from **2.07%** in FY23.\n",
" - **Net Non-Performing Assets (NNPA):** Reduced to **0.30%** from **0.64%** in FY23.\n",
"- **Total Income:** Rose **72%** to **₹2,534 Cr** from **₹1,477 Cr** in FY23.\n",
"- **Net Interest Income (NII):** Increased by **59%** to **₹1,289 Cr** from **₹810 Cr** in FY23.\n",
"\n",
"### How did performance change in Q4FY24?\n",
"- **AUM:** Increased **15% QoQ** and **41% YoY**.\n",
"- **New Customer Acquisition:** Added **4.4 lac** customers in Q4, a **30%** increase QoQ.\n",
"- **Disbursement:** **₹3,970 Cr** in Q4FY24, a **56% QoQ** and **30% YoY** growth.\n",
"- **GNPA & NNPA:** Further improved to **1.50%** and **0.30%** respectively.\n",
"\n",
"### What are the operational efficiencies noted in the report?\n",
"- **Collection Efficiency:** \n",
" - Gross collection efficiency stood at **99.3%**.\n",
" - Net collection efficiency at **96.5%**.\n",
"- **Funding & Borrowings:** \n",
" - Total borrowings increased by **81%** to **₹10,441 Cr** compared to **₹5,775 Cr** in FY23.\n",
"\n",
"### What does the management say about the future?\n",
"- Mr. Shalabh Saxena, CEO, indicates continued focus on sustaining growth and improving efficiencies for FY25. The strategy incorporates managing multiple organizational priorities, including branch expansion and portfolio quality improvement.\n",
"\n",
"### What are the implications for investors?\n",
"- **Strong Growth Indicators:** The noteworthy increase in revenue, profits, and customer acquisition positions Spandana as a promising investment within the microfinance sector.\n",
"- **Reduced Risk Profile:** Improved asset quality metrics (GNPA and NNPA) suggest better risk management and opportunity for long-term stability.\n",
"- **Focus on Operational Efficiency:** The commitment to enhancing distribution productivity may enhance profitability, further benefiting shareholders.\n",
"\n",
"Investors might consider these highlights to evaluate or adjust their investment strategies regarding microfinance and rural-focused financial services in India."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Step 4: Print the result of an example pdf\n",
"article_url=input('Please input the pdf URL: \\n')\n",
"summary = summarize(article_url)\n",
"display(Markdown(summary))"
]
},
{
"cell_type": "markdown",
"id": "70cbbffd-bf70-47d1-b5d6-5f929bdd89eb",
"metadata": {},
"source": [
"# Medical Report Analyst\n",
"#### Step 1: Create your prompts\n",
"\n",
"def craft_user_prompt(article):\n",
" user_prompt = f\"You are looking at a research article titled {article.title}\\n Based on the body of the article, Summarise the research article while highliting, Introduction, the problem, possible solution and conclusion. The body of the article is as follows.\"\n",
" user_prompt += article.text\n",
" return user_prompt\n",
"\n",
"#### Step 2: Make the messages list\n",
"def craft_messages(article):\n",
" system_prompt = \"You are a medical research assistant that analyses the contents of a research article and provides a summary while calling out important points as bullets and quotes. \\\n",
" Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.\"\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n",
" ]"
]
},
{
"cell_type": "markdown",
"id": "c0bb9579-686a-4277-a139-7dff2c64cc9f",
"metadata": {},
"source": [
"# QA Format\n",
"#### Step 1: Create your prompts\n",
"\n",
"def craft_user_prompt(article):\n",
" user_prompt = f\"You are looking at a research article titled {article.title}\\n Based on the body of the article, how are micro RNAs produced in the cell? State the function of the proteins \\\n",
" involved. The body of the article is as follows.\"\n",
" user_prompt += article.text\n",
" return user_prompt\n",
"\n",
"#### Step 2: Make the messages list\n",
"def craft_messages(article):\n",
" system_prompt = \"You are an assistant that analyses the contents of a research article and provide answers to the question asked by the user in 250 words or less. \\\n",
" Ignore text that doesn't belong to the article, like headers or navigation related text. Respond in markdown. Structure your text in the form of question/answer.\"\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": craft_user_prompt(article)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "680b45ae-6f3d-43b5-b69a-4e7f4fcca268",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "988864cc-08a2-4cfb-a09b-524504ee3803",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}