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Merge pull request #2686 from Shivansh-Jain-github/cont1
Open AI chatbot using python
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‎ChatBot using OpenAI API/README.md

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```markdown
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# OpenAI Chatbot Example
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This repository contains examples of using the OpenAI GPT-3 model to create a chatbot that can hold conversations with users.
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## Prerequisites
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- OpenAI Python Package (`openai`)
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- `requests` library (for Method 2)
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You can install the required libraries using the following commands:
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```bash
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pip install openai requests
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```
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## Method 1 - Using OpenAI Python Package
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This method demonstrates how to use the OpenAI Python package to create a chatbot using the `gpt-3.5-turbo` model.
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The code provided in the repository (`method_1_openai_package.py`) includes:
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- Setting up the OpenAI API key.
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- Initiating a conversation with a system message.
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- Taking user inputs and generating replies using the chatbot.
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- Displaying the chat history.
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## Method 2 - Using API Endpoint
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This method demonstrates how to interact with the OpenAI API directly using HTTP requests.
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The code provided in the repository (`method_2_api_endpoint.py`) includes:
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- Defining the API endpoint URL.
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- Creating a payload with user inputs and other parameters.
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- Sending a POST request to the API endpoint.
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- Extracting and displaying the generated response.
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## Usage
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1. Set up your OpenAI API key by replacing `'sk-'` in the code with your actual API key.
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2. Choose the method you want to use (Method 1 or Method 2).
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3. Run the selected script using a Python interpreter:
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```bash
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python method_1_openai_package.py
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```
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or
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```bash
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python method_2_api_endpoint.py
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```
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4. Observe the interaction with the chatbot and the generated responses.
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## Notes
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- Make sure to review the OpenAI API documentation for more information on available models, parameters, and best practices.
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‎ChatBot using OpenAI API/script1.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"!pip install -q openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"import openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"openai.api_key = 'sk-'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a kind helpful assistant.\"},\n",
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"]"
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]
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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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"outputs": [],
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"source": [
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"while True:\n",
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" message = input(\"User : \")\n",
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" if message:\n",
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" messages.append(\n",
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" {\"role\": \"user\", \"content\": message},\n",
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" )\n",
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" chat = openai.ChatCompletion.create(\n",
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" model=\"gpt-3.5-turbo\", messages=messages\n",
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" )\n",
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" \n",
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" reply = chat.choices[0].message.content\n",
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" print(f\"ChatGPT: {reply}\")\n",
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" messages.append({\"role\": \"assistant\", \"content\": reply})\n",
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" "
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎ChatBot using OpenAI API/script2.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'openai' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[1], line 18\u001b[0m\n\u001b[0;32m 3\u001b[0m URL \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhttps://api.openai.com/v1/chat/completions\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 5\u001b[0m payload \u001b[39m=\u001b[39m {\n\u001b[0;32m 6\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mgpt-3.5-turbo\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 7\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmessages\u001b[39m\u001b[39m\"\u001b[39m: [{\u001b[39m\"\u001b[39m\u001b[39mrole\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39muser\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcontent\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mWhat is the first computer in the world?\u001b[39m\u001b[39m\"\u001b[39m}],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mfrequency_penalty\u001b[39m\u001b[39m\"\u001b[39m:\u001b[39m0\u001b[39m,\n\u001b[0;32m 14\u001b[0m }\n\u001b[0;32m 16\u001b[0m headers \u001b[39m=\u001b[39m {\n\u001b[0;32m 17\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mContent-Type\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mapplication/json\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m---> 18\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mAuthorization\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mBearer \u001b[39m\u001b[39m{\u001b[39;00mopenai\u001b[39m.\u001b[39mapi_key\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m 19\u001b[0m }\n\u001b[0;32m 21\u001b[0m response \u001b[39m=\u001b[39m requests\u001b[39m.\u001b[39mpost(URL, headers\u001b[39m=\u001b[39mheaders, json\u001b[39m=\u001b[39mpayload, stream\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
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"\u001b[1;31mNameError\u001b[0m: name 'openai' is not defined"
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]
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}
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],
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"source": [
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"import requests\n",
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"\n",
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"URL = \"https://api.openai.com/v1/chat/completions\"\n",
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"\n",
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"payload = {\n",
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"\"model\": \"gpt-3.5-turbo\",\n",
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"\"messages\": [{\"role\": \"user\", \"content\": f\"What is the first computer in the world?\"}],\n",
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"\"temperature\" : 1.0,\n",
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"\"top_p\":1.0,\n",
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"\"n\" : 1,\n",
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"\"stream\": False,\n",
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"\"presence_penalty\":0,\n",
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"\"frequency_penalty\":0,\n",
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"}\n",
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"\n",
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"headers = {\n",
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"\"Content-Type\": \"application/json\",\n",
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"\"Authorization\": f\"Bearer {openai.api_key}\"\n",
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"}\n",
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"\n",
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"response = requests.post(URL, headers=headers, json=payload, stream=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'response' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m response\u001b[39m.\u001b[39mcontent\n",
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"\u001b[1;31mNameError\u001b[0m: name 'response' is not defined"
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]
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}
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],
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"source": [
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"\n",
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"response.content"
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]
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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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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Web Scrapping using Beautiful Soup/README.md

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# Web Scraping with Beautiful Soup
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This script performs web scraping on a CodeChef problem statement webpage using the Beautiful Soup library in Python.
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## Description
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The Python script utilizes the `requests` and `BeautifulSoup` libraries to extract information from a CodeChef problem statement webpage. It demonstrates the following actions:
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- Printing the title of the webpage.
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- Finding and printing all links on the page.
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- Extracting text from paragraphs.
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- Extracting image URLs.
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- Counting and categorizing HTML tags.
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- Filtering and printing valid links.
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- Saving extracted data to a text file.
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## Prerequisites
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Ensure you have the following libraries installed:
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- `requests`
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- `beautifulsoup4`
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You can install them using the following commands:
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```bash
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pip install requests beautifulsoup4

‎Web Scrapping using Beautiful Soup/code.py

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import requests
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from bs4 import BeautifulSoup
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import re
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url = 'https://www.codechef.com/problems/TWORANGES?tab=statement'
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response = requests.get(url)
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soup = BeautifulSoup(response.content, 'html.parser')
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# Print the title of the webpage
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print(f"Title: {soup.title.text}\n")
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# Find and print all links on the page
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print("Links on the page:")
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for link in soup.find_all('a'):
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print(link.get('href'))
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# Extract text from paragraphs
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print("\nText from paragraphs:")
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for paragraph in soup.find_all('p'):
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print(paragraph.text)
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# Extract image URLs
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print("\nImage URLs:")
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for img in soup.find_all('img'):
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img_url = img.get('src')
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if img_url:
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print(img_url)
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# Count and categorize tags
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print("\nTag counts:")
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tag_counts = {}
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for tag in soup.find_all():
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tag_name = tag.name
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if tag_name:
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tag_counts[tag_name] = tag_counts.get(tag_name, 0) + 1
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for tag, count in tag_counts.items():
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print(f"{tag}: {count}")
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# Filter and print valid links
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print("\nValid links:")
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for link in soup.find_all('a'):
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href = link.get('href')
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if href and re.match(r'^https?://', href):
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print(href)
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# Save data to a file
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with open('webpage_data.txt', 'w') as file:
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file.write(f"Title: {soup.title.text}\n\n")
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file.write("Links on the page:\n")
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for link in soup.find_all('a'):
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file.write(f"{link.get('href')}\n")
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file.write("\nText from paragraphs:\n")
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for paragraph in soup.find_all('p'):
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file.write(f"{paragraph.text}\n")
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print("\nData saved to 'webpage_data.txt'")

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