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added day 1 assignments.

pull/39/head
shreshthkapai 5 months ago
parent
commit
8b67498c58
  1. 297
      week1/community-contributions/day-1-research-paper-summarizer-using -openai-api.ipynb
  2. 206
      week1/community-contributions/day-1-to-do-list using-ollama.ipynb

297
week1/community-contributions/day-1-research-paper-summarizer-using -openai-api.ipynb

@ -0,0 +1,297 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "52dc600c-4c45-4803-81cb-f06347f4b2c3",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4082f16f-d843-41c7-9137-cdfec093b2d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key found and looks good so far\n"
]
}
],
"source": [
"load_dotenv()\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if not api_key:\n",
" print('No API key was found')\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"API key is found but is not in the proper format\")\n",
"else:\n",
" print(\"API key found and looks good so far\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "16c295ce-c57d-429e-8c03-f6610a8ddd42",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9a548a52-0f7e-4fdf-ad68-0138b2445935",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"You are a research summarizer. That summarizes the content of the research paper in no more than 1000 words. The research summary that you provide should include the following:\n",
"1) Title and Authors - Identify the study and contributors.\n",
"2) Objective/Problem - State the research goal or question.\n",
"3) Background - Briefly explain the context and significance.\n",
"4) Methods - Summarize the approach or methodology.\n",
"5) Key Findings - Highlight the main results or insights.\n",
"6) Conclusion - Provide the implications or contributions of the study.\n",
"7) Future Directions - Suggest areas for further research or exploration.\n",
"8) Limitations - Highlight constraints or challenges in the study.\n",
"9) Potential Applications - Discuss how the findings can be applied in real-world scenarios.\n",
"Keep all points concise, clear, and focused and generate output in markdown.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "66b4411f-172e-46be-b6cd-a9e5b857fb28",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: ipywidgets in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (8.1.5)\n",
"Requirement already satisfied: pdfplumber in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (0.11.4)\n",
"Requirement already satisfied: comm>=0.1.3 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (0.2.2)\n",
"Requirement already satisfied: ipython>=6.1.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (8.30.0)\n",
"Requirement already satisfied: traitlets>=4.3.1 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (5.14.3)\n",
"Requirement already satisfied: widgetsnbextension~=4.0.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (4.0.13)\n",
"Requirement already satisfied: jupyterlab_widgets~=3.0.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (3.0.13)\n",
"Requirement already satisfied: pdfminer.six==20231228 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (20231228)\n",
"Requirement already satisfied: Pillow>=9.1 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (11.0.0)\n",
"Requirement already satisfied: pypdfium2>=4.18.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (4.30.0)\n",
"Requirement already satisfied: charset-normalizer>=2.0.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfminer.six==20231228->pdfplumber) (3.4.0)\n",
"Requirement already satisfied: cryptography>=36.0.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfminer.six==20231228->pdfplumber) (44.0.0)\n",
"Requirement already satisfied: colorama in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.4.6)\n",
"Requirement already satisfied: decorator in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n",
"Requirement already satisfied: jedi>=0.16 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.19.2)\n",
"Requirement already satisfied: matplotlib-inline in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n",
"Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (3.0.48)\n",
"Requirement already satisfied: pygments>=2.4.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n",
"Requirement already satisfied: stack_data in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n",
"Requirement already satisfied: typing_extensions>=4.6 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (4.12.2)\n",
"Requirement already satisfied: cffi>=1.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from cryptography>=36.0.0->pdfminer.six==20231228->pdfplumber) (1.17.1)\n",
"Requirement already satisfied: parso<0.9.0,>=0.8.4 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n",
"Requirement already satisfied: wcwidth in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n",
"Requirement already satisfied: executing>=1.2.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (2.1.0)\n",
"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (3.0.0)\n",
"Requirement already satisfied: pure_eval in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (0.2.3)\n",
"Requirement already satisfied: pycparser in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20231228->pdfplumber) (2.22)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install ipywidgets pdfplumber"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8cd8556-ad86-4949-9f15-09de2b8c712b",
"metadata": {},
"outputs": [],
"source": [
"import pdfplumber\n",
"from ipywidgets import widgets\n",
"from io import BytesIO"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0eba3cee-d85c-4d75-9b27-70c8cd7587b1",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display, Markdown"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "53e270e1-c2e6-4bcc-9ada-90c059cd5a51",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(user_prompt):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2f1807ec-c10b-4d26-9bee-89bd7a4bbb95",
"metadata": {},
"outputs": [],
"source": [
"def summarize(user_prompt):\n",
" # Generate messages using the user_prompt\n",
" messages = messages_for(user_prompt)\n",
" try:\n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\", # Correct model name\n",
" messages=messages,\n",
" max_tokens = 1000 # Pass the generated messages\n",
" )\n",
" # Return the content from the API response correctly\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" # Instead of printing, return an error message that can be displayed\n",
" return f\"Error in OpenAI API call: {e}\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0dee8345-4eec-4a9c-ac4e-ad70e13cea44",
"metadata": {},
"outputs": [],
"source": [
"upload_widget = widgets.FileUpload(\n",
" accept='.pdf', \n",
" multiple=False,\n",
" description='Upload PDF',\n",
" layout=widgets.Layout(width='300px',height = '100px', border='2px dashed #cccccc', padding='10px')\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1ff9c7b9-1a3a-4128-a33f-0e5bb2a93d33",
"metadata": {},
"outputs": [],
"source": [
"def extract_text_and_generate_summary(change):\n",
" print(\"extracting text\")\n",
" if upload_widget.value:\n",
" # Extract the first uploaded file\n",
" uploaded_file = list(upload_widget.value)[0]\n",
" pdf_file = uploaded_file['content']\n",
"\n",
" # Extract text from the PDF\n",
" try:\n",
" with pdfplumber.open(BytesIO(pdf_file)) as pdf:\n",
" extracted_text = \"\\n\".join(page.extract_text() for page in pdf.pages)\n",
"\n",
" # Generate the user prompt\n",
" user_prompt = (\n",
" f\"You are looking at the text from a research paper. Summarize it in no more than 1000 words. \"\n",
" f\"The output should be in markdown.\\n\\n{extracted_text}\"\n",
" )\n",
"\n",
" # Get the summarized response\n",
" response = summarize(user_prompt)\n",
" \n",
" if response:\n",
" # Use IPython's display method to show markdown below the cell\n",
" display(Markdown(response))\n",
" \n",
" except Exception as e:\n",
" # If there's an error, display it using Markdown\n",
" display(Markdown(f\"**Error:** {str(e)}\"))\n",
"\n",
" # Reset the upload widget\n",
" upload_widget.value = ()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0c16fe3f-704e-4a87-acd9-42c4e6b0d2fa",
"metadata": {},
"outputs": [],
"source": [
"upload_widget.observe(extract_text_and_generate_summary, names='value')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c2c2d2b2-1264-42d9-9271-c4700b4df80a",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7304350377d845e78a9a758235e5eba1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FileUpload(value=(), accept='.pdf', description='Upload PDF', layout=Layout(border_bottom='2px dashed #cccccc'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(upload_widget)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70c76b90-e626-44b3-8d1f-6e995e8a938d",
"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
}

206
week1/community-contributions/day-1-to-do-list using-ollama.ipynb

@ -0,0 +1,206 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 208,
"id": "f61139a1-40e1-4273-b9a6-5a0a9d63a9bd",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"from reportlab.lib.pagesizes import letter\n",
"from reportlab.pdfgen import canvas\n",
"from IPython.display import display, FileLink\n",
"from IPython.display import display, HTML, FileLink\n",
"from reportlab.lib.pagesizes import A4"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "e0858b96-fd41-4911-a333-814e4ed23279",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting reportlab\n",
" Downloading reportlab-4.2.5-py3-none-any.whl.metadata (1.5 kB)\n",
"Requirement already satisfied: pillow>=9.0.0 in c:\\users\\legion\\anaconda3\\envs\\to_do_list\\lib\\site-packages (from reportlab) (11.0.0)\n",
"Collecting chardet (from reportlab)\n",
" Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)\n",
"Downloading reportlab-4.2.5-py3-none-any.whl (1.9 MB)\n",
" ---------------------------------------- 0.0/1.9 MB ? eta -:--:--\n",
" ---------------- ----------------------- 0.8/1.9 MB 6.7 MB/s eta 0:00:01\n",
" ---------------------------------------- 1.9/1.9 MB 11.9 MB/s eta 0:00:00\n",
"Downloading chardet-5.2.0-py3-none-any.whl (199 kB)\n",
"Installing collected packages: chardet, reportlab\n",
"Successfully installed chardet-5.2.0 reportlab-4.2.5\n"
]
}
],
"source": [
"!pip install reportlab"
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "62cc9d37-c801-4e8a-ad2c-7b1450725a10",
"metadata": {},
"outputs": [],
"source": [
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\":\"application/json\"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": 249,
"id": "525a81e7-30f8-4db7-bc8d-29948195bd4f",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"You are a to-do list generator. Based on the user's input, you will create a clear and descriptive to-do\n",
"list using bullet points. Only generate the to-do list as bullet points with some explaination and time fraame only if asked for and nothing else. \n",
"Be a little descriptive.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 315,
"id": "7fca3303-3add-468a-a6bd-be7a4d72c811",
"metadata": {},
"outputs": [],
"source": [
"def generate_to_do_list(task_description):\n",
" payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": task_description}\n",
" ],\n",
" \"stream\": False\n",
" }\n",
"\n",
" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"\n",
" if response.status_code == 200:\n",
" try:\n",
" json_response = response.json()\n",
" to_do_list = json_response.get(\"message\", {}).get(\"content\", \"No to-do list found.\")\n",
" \n",
" formatted_output = \"Your To-Do List:\\n\\n\" + to_do_list\n",
" file_name = \"to_do_list.txt\"\n",
" \n",
" with open(file_name, \"w\", encoding=\"utf-8\") as file:\n",
" file.write(formatted_output)\n",
"\n",
" return file_name\n",
" \n",
" except Exception as e:\n",
" return f\"Error parsing JSON: {e}\"\n",
" else:\n",
" return f\"Error: {response.status_code} - {response.text}\""
]
},
{
"cell_type": "code",
"execution_count": 316,
"id": "d45d6c7e-0e89-413e-8f30-e4975ea6d043",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Enter the task description of the to-do list: Give me a 4-week to-do list plan for a wedding reception party.\n"
]
}
],
"source": [
"task_description = input(\"Enter the task description of the to-do list:\")"
]
},
{
"cell_type": "code",
"execution_count": 317,
"id": "5493da44-e254-4d06-b973-a8069c2fc625",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"result = generate_to_do_list(task_description)"
]
},
{
"cell_type": "code",
"execution_count": 318,
"id": "5e95c722-ce1a-4630-b21a-1e00e7ba6ab9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<p>You can download your to-do list by clicking the link below:</p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<a href='to_do_list.txt' target='_blank'>to_do_list.txt</a><br>"
],
"text/plain": [
"C:\\Users\\Legion\\to-do list using ollama\\to_do_list.txt"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(HTML(\"<p>You can download your to-do list by clicking the link below:</p>\"))\n",
"display(FileLink(result))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3d0a44e-bca4-4944-8593-1761c2f73a70",
"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
}
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