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README.md

@@ -1,12 +1,12 @@
----
-title: Arxiv Chatbot
-emoji: 🐢
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Arxiv Chatbot
+emoji: 🐢
+colorFrom: gray
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.34.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

chat/__init__.py

@@ -0,0 +1,9 @@
+
+import chat.arxiv_bot.arxiv_bot_utils as utils
+import os
+from getpass import getpass
+import json
+from .model_manage import get_model
+
+model = get_model()
+

chat/apps.py

@@ -0,0 +1,11 @@
+from django.apps import AppConfig
+import google.generativeai as genai
+
+
+
+
+class MembersConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'chat'
+
+

chat/arxiv_bot/arxiv_bot_utils.py

@@ -0,0 +1,276 @@
+import chromadb
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from transformers import AutoModel
+import json
+from numpy.linalg import norm
+import sqlite3
+import urllib
+from django.conf import settings
+
+
+# this module act as a singleton class
+
+class JinaAIEmbeddingFunction(EmbeddingFunction):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def __call__(self, input: Documents) -> Embeddings:
+        embeddings = self.model.encode(input)
+        return embeddings.tolist()
+
+# instance of embedding_model
+embedding_model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en',
+                                                trust_remote_code=True,
+                                                cache_dir='models')
+
+# instance of JinaAIEmbeddingFunction
+ef = JinaAIEmbeddingFunction(embedding_model)
+
+# list of topics
+topic_descriptions = json.load(open("topic_descriptions.txt")) 
+topics = list(dict.keys(topic_descriptions))
+embeddings = [embedding_model.encode(topic_descriptions[key]) for key in topic_descriptions]
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+
+def choose_topic(summary):
+    embed = embedding_model.encode(summary)
+    topic = ""
+    max_sim = 0.
+    for i,key in enumerate(topics):
+        sim = cos_sim(embed,embeddings[i])
+        if sim > max_sim:
+            topic = key
+            max_sim = sim
+    return topic
+
+def authors_list_to_str(authors):
+   """input a list of authors, return a string represent authors"""
+   text = ""
+   for author in authors:
+      text+=author+", "
+   return text[:-3]
+
+def authors_str_to_list(string):
+    """input a string of authors, return a list of authors"""
+    authors = []
+    list_auth = string.split("and")
+    for author in list_auth:
+        if author != "et al.":
+            authors.append(author.strip())
+    return authors
+
+def chunk_texts(text, max_char=400):
+  """
+  Chunk a long text into several chunks, with each chunk about 300-400 characters long,
+  but make sure no word is cut in half.
+  Args:
+      text: The long text to be chunked.
+      max_char: The maximum number of characters per chunk (default: 400).
+  Returns:
+      A list of chunks.
+  """
+  chunks = []
+  current_chunk = ""
+  words = text.split()
+  for word in words:
+    if len(current_chunk) + len(word) + 1 >= max_char:
+        chunks.append(current_chunk)
+        current_chunk = " "
+    else:
+      current_chunk += " " + word
+  chunks.append(current_chunk.strip())
+  return chunks
+
+def trimming(txt):
+    start = txt.find("{")
+    end = txt.rfind("}")
+    return txt[start:end+1].replace("\n"," ")
+
+# crawl data
+
+def extract_tag(txt,tagname):
+    return txt[txt.find("<"+tagname+">")+len(tagname)+2:txt.find("</"+tagname+">")]
+
+def get_record(extract):
+    id = extract_tag(extract,"id")
+    updated = extract_tag(extract,"updated")
+    published = extract_tag(extract,"published")
+    title = extract_tag(extract,"title").replace("\n ","").strip()
+    summary = extract_tag(extract,"summary").replace("\n","").strip()
+    authors = []
+    while extract.find("<author>")!=-1:
+        author = extract_tag(extract,"name")
+        extract = extract[extract.find("</author>")+9:]
+        authors.append(author)
+    pattern = '<link title="pdf" href="'
+    link_start = extract.find('<link title="pdf" href="')
+    link = extract[link_start+len(pattern):extract.find("rel=",link_start)-2]
+    return [id, updated, published, title, authors, link, summary]
+
+def crawl_exact_paper(title,author,max_results=3):
+    authors = authors_list_to_str(author)
+    records = []
+    url = 'http://export.arxiv.org/api/query?search_query=ti:{title}+AND+au:{author}&max_results={max_results}'.format(title=title,author=authors,max_results=max_results)
+    url = url.replace(" ","%20")
+    try:
+        arxiv_page = urllib.request.urlopen(url,timeout=100).read()
+        xml = str(arxiv_page,encoding="utf-8") 
+        while xml.find("<entry>") != -1:
+            extract = xml[xml.find("<entry>")+7:xml.find("</entry>")]
+            xml = xml[xml.find("</entry>")+8:]
+            extract = get_record(extract)
+            topic = choose_topic(extract[6])
+            records.append([topic,*extract])
+        return records
+    except Exception as e:
+        return "Error: "+str(e)
+
+def crawl_arxiv(keyword_list, max_results=100):
+    baseurl = 'http://export.arxiv.org/api/query?search_query='
+    records = []
+    for i,keyword in enumerate(keyword_list):
+        if i ==0:
+            url = baseurl + 'all:' + keyword
+        else:
+            url = url + '+OR+' + 'all:' + keyword
+    url = url+ '&max_results=' + str(max_results)
+    url = url.replace(' ', '%20')
+    try:
+        arxiv_page = urllib.request.urlopen(url,timeout=100).read()
+        xml = str(arxiv_page,encoding="utf-8") 
+        while xml.find("<entry>") != -1:
+            extract = xml[xml.find("<entry>")+7:xml.find("</entry>")]
+            xml = xml[xml.find("</entry>")+8:]
+            extract = get_record(extract)
+            topic = choose_topic(extract[6])
+            records.append([topic,*extract])
+        return records
+    except Exception as e:
+        return "Error: "+str(e)
+
+class ArxivSQL:
+    def __init__(self, table="arxivsql", name="db.sqlite3"):
+        self.con = sqlite3.connect(name)
+        self.cur = self.con.cursor()
+        self.table = table
+    
+    def query(self, title="", author=[]):
+        if len(title)>0:
+            query_title = 'title like "%{}%"'.format(title)
+        else:
+            query_title = "True"
+        if len(author)>0:
+            query_author = 'authors like '
+            for auth in author:
+                query_author += "'%{}%' or ".format(auth)
+            query_author = query_author[:-4]
+        else:
+            query_author = "True"
+        query = "select * from {} where {} and {}".format(self.table,query_title,query_author)
+        result = self.cur.execute(query)
+        return result.fetchall()
+
+    def query_id(self, ids=[]):
+        try:
+            if len(ids) == 0:
+                return None
+            query = "select * from {} where id in (".format(self.table)
+            for id in ids:
+                query+="'"+id+"',"
+            query = query[:-1] + ")"
+            result = self.cur.execute(query)
+            return result.fetchall()
+        except Exception as e:
+            print(e)
+            print("Error query: ",query)
+    
+    def add(self, crawl_records):
+        """
+        Add crawl_records (list) obtained from arxiv_crawlers
+        A record is a list of 8 columns: 
+        [topic, id, updated, published, title, author, link, summary]
+        Return the final length of the database table
+        """
+        results = ""
+        for record in crawl_records:
+            try:
+                query = """insert into arxivsql values("{}","{}","{}","{}","{}","{}","{}")""".format(
+                    record[1][21:],
+                    record[0],
+                    record[4].replace('"',"'"),
+                    authors_list_to_str(record[5]),
+                    record[2][:10],
+                    record[3][:10],
+                    record[6]
+                )
+                self.cur.execute(query)
+                self.con.commit()
+            except Exception as e:
+                result+=str(e)
+                result+="\n" + query + "\n"
+            finally:
+                return results
+            
+# instance of ArxivSQL
+sqldb = ArxivSQL()
+
+class ArxivChroma:
+    """
+    Create an interface to arxivdb, which only support query and addition.
+    This interface do not support edition and deletion procedures. 
+    """
+    def __init__(self, table="arxiv_records", name="arxivdb/"):
+        self.client = chromadb.PersistentClient(name)
+        self.model = embedding_model
+        self.collection = self.client.get_or_create_collection(table,
+                                                               embedding_function=JinaAIEmbeddingFunction(
+                                                                   model = self.model
+                                                               ))
+    
+    def query_relevant(self, keywords, query_texts, n_results=3):
+        """
+        Perform a query using a list of keywords (str),
+        or using a relavant string
+        """
+        contains = []
+        for keyword in keywords:
+            contains.append({"$contains":keyword.lower()})
+        return self.collection.query(
+            query_texts=query_texts,
+            where_document={
+                "$or":contains
+            },
+            n_results=n_results,
+        )
+    
+    def query_exact(self, id):
+        ids = ["{}_{}".format(id,j) for j in range(0,10)]
+        return self.collection.get(ids=ids)
+
+    def add(self, crawl_records):
+        """
+        Add crawl_records (list) obtained from arxiv_crawlers
+        A record is a list of 8 columns: 
+        [topic, id, updated, published, title, author, link, summary]
+        Return the final length of the database table
+        """
+        for record in crawl_records:
+                embed_text = """
+                Topic: {},
+                Title: {},
+                Summary: {}
+            """.format(record[0],record[4],record[7])
+                chunks = chunk_texts(embed_text)
+                ids = [record[1][21:]+"_"+str(j) for j in range(len(chunks))]
+                paper_ids = [{"paper_id":record[1][21:]} for _ in range(len(chunks))]
+                self.collection.add(
+                    documents = chunks,
+                    metadatas=paper_ids,
+                    ids = ids
+                )
+        return self.collection.count()
+
+# instance of ArxivChroma
+db = ArxivChroma()
+

chat/arxiv_bot/prebuild.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\Program\\Anaconda\\envs\\python_project\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "d:\\Program\\Anaconda\\envs\\python_project\\lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "d:\\Program\\Anaconda\\envs\\python_project\\lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import google.generativeai as genai\n",
+    "import arxiv_bot_utils as utils\n",
+    "import os\n",
+    "from getpass import getpass\n",
+    "import json\n",
+    "#chỉ là import một cách bình thường\n",
+    "#nội dung là "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "models/gemini-1.0-pro\n",
+      "models/gemini-1.0-pro-001\n",
+      "models/gemini-1.0-pro-latest\n",
+      "models/gemini-1.0-pro-vision-latest\n",
+      "models/gemini-1.5-pro-latest\n",
+      "models/gemini-pro\n",
+      "models/gemini-pro-vision\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.environ['GEMINI_API_KEY'] = getpass(\"Input your API key: \")\n",
+    "# gán biến môi trường luôn\n",
+    "gemini_api_key = os.getenv(\"GEMINI_API_KEY\") # string trong môi trường\n",
+    "if not gemini_api_key:\n",
+    "    raise ValueError(\n",
+    "            \"Gemini API Key not provided. Please provide GEMINI_API_KEY as an environment variable\"\n",
+    "        )\n",
+    "genai.configure(api_key=gemini_api_key)\n",
+    "for m in genai.list_models():\n",
+    "  if 'generateContent' in m.supported_generation_methods:\n",
+    "    print(m.name)\n",
+    "    #models nằm trên máy chủ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = genai.GenerationConfig(max_output_tokens=2048,\n",
+    "                                temperature=0.7)\n",
+    "safety_settings = [\n",
+    "    {\n",
+    "        \"category\": \"HARM_CATEGORY_DANGEROUS\",\n",
+    "        \"threshold\": \"BLOCK_NONE\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"category\": \"HARM_CATEGORY_HARASSMENT\",\n",
+    "        \"threshold\": \"BLOCK_NONE\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"category\": \"HARM_CATEGORY_HATE_SPEECH\",\n",
+    "        \"threshold\": \"BLOCK_NONE\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\",\n",
+    "        \"threshold\": \"BLOCK_NONE\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
+    "        \"threshold\": \"BLOCK_NONE\",\n",
+    "    },\n",
+    "]\n",
+    "model = genai.GenerativeModel(\"gemini-pro\",\n",
+    "                              generation_config=config,\n",
+    "                              safety_settings=safety_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_keyword_prompt(query):\n",
+    "    \"\"\"A prompt that return a JSON block as arguments for querying database\"\"\"\n",
+    "\n",
+    "    prompt = (\n",
+    "        \"\"\"[INST] SYSTEM: You are an assistant that choose only one action below based on guest question.\n",
+    "   1. If the guest question is asking for a single specific document or article with explicit title, you need to respond the information in JSON format with 2 keys \"title\", \"author\" if found any above. The authors are separated with the word 'and'. \n",
+    "   2. If the guest question is asking for relevant informations about a topic, you need to respond the information in JSON format with 2 keys \"keywords\", \"description\", include a list of keywords represent the main academic topic, \\\n",
+    "     and a description about the main topic. You may paraphrase the keywords to add more. \\\n",
+    "   3. If the guest is not asking for any informations or documents, you need to respond with a polite answer in JSON format with 1 key \"answer\".\n",
+    "   QUESTION: '{query}'\n",
+    "   [/INST]\n",
+    "   ANSWER: \n",
+    "   \"\"\"\n",
+    "    ).format(query=query)\n",
+    "\n",
+    "    return prompt\n",
+    "\n",
+    "def make_answer_prompt(input, contexts):\n",
+    "    \"\"\"A prompt that return the final answer, based on the queried context\"\"\"\n",
+    "\n",
+    "    prompt = (\n",
+    "        \"\"\"[INST] You are a library assistant that help to search articles and documents based on user's question.\n",
+    "  From guest's question, you have found some records and documents that may help. Now you need to answer the guest with the information found.\n",
+    "  If no information found in the database, you may generate some other recommendation related to user's question using your own knowledge. Each article or paper must have a link to the pdf download page.\n",
+    "  You should answer in a conversational form politely.\n",
+    "  QUESTION: '{input}'\n",
+    "  INFORMATION: '{contexts}'\n",
+    "  [/INST]\n",
+    "  ANSWER:\n",
+    "  \"\"\"\n",
+    "    ).format(input=input, contexts=contexts)\n",
+    "\n",
+    "    return prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def response(args):\n",
+    "    \"\"\"Create response context, based on input arguments\"\"\"\n",
+    "    keys = list(dict.keys(args))\n",
+    "    if \"answer\" in keys:\n",
+    "        return args['answer'], None  # trả lời trực tiếp\n",
+    "     \n",
+    "    if \"keywords\" in keys:\n",
+    "        # perform query\n",
+    "        query_texts = args[\"description\"]\n",
+    "        keywords = args[\"keywords\"]\n",
+    "        results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)\n",
+    "        # print(results)\n",
+    "        ids = results['metadatas'][0]\n",
+    "        if len(ids) == 0:\n",
+    "            # go crawl some\n",
+    "            new_records = utils.crawl_arxiv(keyword_list=keywords, max_results=10)\n",
+    "            print(\"Got new records: \",len(new_records))\n",
+    "            if type(new_records) == str:\n",
+    "                return \"Error occured, information not found\", new_records\n",
+    "            utils.db.add(new_records)\n",
+    "            utils.sqldb.add(new_records)\n",
+    "            results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)\n",
+    "            ids = results['metadatas'][0]\n",
+    "            print(\"Re-queried on chromadb, results: \",ids)\n",
+    "        paper_id = [id['paper_id'] for id in ids]\n",
+    "        paper_info = utils.sqldb.query_id(paper_id)\n",
+    "        print(paper_info)\n",
+    "        records = [] # get title (2), author (3), link (6)\n",
+    "        result_string = \"\"\n",
+    "        if paper_info:\n",
+    "            for i in range(len(paper_info)):\n",
+    "                result_string += \"Title: {}, Author: {}, Link: {}\".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])\n",
+    "                records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])\n",
+    "                return result_string, records\n",
+    "        else:\n",
+    "            return \"Information not found\", \"Information not found\"\n",
+    "        # invoke llm and return result\n",
+    "\n",
+    "    if \"title\" in keys:\n",
+    "        title = args['title']\n",
+    "        authors = utils.authors_str_to_list(args['author'])\n",
+    "        paper_info = utils.sqldb.query(title = title,author = authors)\n",
+    "        # if query not found then go crawl brh\n",
+    "        # print(paper_info)\n",
+    "\n",
+    "        if len(paper_info) == 0:\n",
+    "            new_records = utils.crawl_exact_paper(title=title,author=authors)\n",
+    "            print(\"Got new records: \",len(new_records))\n",
+    "            if type(new_records) == str:\n",
+    "                # print(new_records)\n",
+    "                return \"Error occured, information not found\", \"Information not found\"\n",
+    "            utils.db.add(new_records)\n",
+    "            utils.sqldb.add(new_records)\n",
+    "            paper_info = utils.sqldb.query(title = title,author = authors)\n",
+    "            print(\"Re-queried on chromadb, results: \",paper_info)\n",
+    "        # -------------------------------------\n",
+    "        records = [] # get title (2), author (3), link (6)\n",
+    "        result_string = \"\"\n",
+    "        for i in range(len(paper_info)):\n",
+    "            result_string += \"Title: {}, Author: {}, Link: {}\".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])\n",
+    "            records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])\n",
+    "        # process results:\n",
+    "        if len(result_string) == 0:\n",
+    "            return \"Information not found\", \"Information not found\"\n",
+    "        return result_string, records\n",
+    "        # invoke llm and return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def full_chain_single_question(input_prompt):\n",
+    "    try:\n",
+    "        first_prompt = extract_keyword_prompt(input_prompt)\n",
+    "        temp_answer = model.generate_content(first_prompt).text\n",
+    "\n",
+    "        args = json.loads(utils.trimming(temp_answer))\n",
+    "        contexts, results = response(args)\n",
+    "        if not results:\n",
+    "            print(contexts)\n",
+    "        else:\n",
+    "            output_prompt = make_answer_prompt(input_prompt,contexts)\n",
+    "            answer = model.generate_content(output_prompt).text\n",
+    "        return temp_answer, answer\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        return temp_answer, \"Error occured: \" + str(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('1903.04824v1', 'computer science', 'Proceedings of the Fifth International Conference on Cloud and Robotics (ICCR2018)', ' Huaxi,  Zhang, Jacques Malenfan', '2019-03-12', '2019-03-12', 'http://arxiv.org/pdf/1903.04824v1'), ('1709.07597v1', 'economics', 'Inverse Reinforcement Learning with Conditional Choice Probabilities', 'Mohit Sharma, Kris M. Kitani, Joachim Groege', '2017-09-22', '2017-09-22', 'http://arxiv.org/pdf/1709.07597v1')]\n",
+      "Sure, here are some key papers on model predictive control for nonlinear systems:\n",
+      "\n",
+      "* **Nonlinear Model Predictive Control: A Survey** by Garcia, P.D., Prett, D.M., and Morari, M. (1989)\n",
+      "* **Model Predictive Control for Nonlinear Systems** by Camacho, E.F. and Bordons, C. (1999)\n",
+      "* **Nonlinear Model Predictive Control** by Rawlings, J.B. and Mayne, D.Q. (2009)\n",
+      "\n",
+      "As for recent reviews on the application of control theory to robotics, here are a few:\n",
+      "\n",
+      "* **Control of Robot Manipulators** by Spong, M.W., Hutchinson, S., and Vidyasagar, M. (2006)\n",
+      "* **Robotics: Modelling, Planning and Control** by Siciliano, B., Sciavicco, L., Villani, L., and Oriolo, G. (2010)\n",
+      "* **Control of Robot Arms** by Featherstone, R. (2014)\n",
+      "\n",
+      "I hope this information is helpful. Please let me know if you have any other questions.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test response, second step\n",
+    "input_prompt = \"Can you suggest some key papers on model predictive control for nonlinear systems, and are there any recent reviews on the application of control theory to robotics?\"\n",
+    "args = \"{\\n \\\"keywords\\\": [\\\"Model predictive control\\\", \\\"Nonlinear systems\\\", \\\"Robotics\\\", \\\"Control theory\\\"],\\n \\\"description\\\": \\\"Model predictive control (MPC) is a control algorithm that uses a model of the system to predict future behavior and optimize the control inputs. MPC is particularly well-suited for nonlinear systems, as it can handle the complex dynamics of these systems. In recent years, MPC has been increasingly applied to robotics, as it can improve the performance and safety of robotic systems. Control theory is a branch of mathematics that deals with the analysis and design of control systems. Control theory has been applied to a wide range of problems in robotics, including motion planning, trajectory tracking, and force control.\\\"\\n}\"\n",
+    "args = json.loads(args)\n",
+    "contexts, results = response(args)\n",
+    "if not results:\n",
+    "  # direct answer\n",
+    "  print(contexts)\n",
+    "else:\n",
+    "  output_prompt = make_answer_prompt(input_prompt,contexts)\n",
+    "  answer = model.generate_content(output_prompt).text\n",
+    "  print(answer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'desired': 'Natural Language Processing (Computer Science)', 'question': 'What are some recent papers on deep learning architectures for text classification, and can you recommend any surveys or reviews on the topic?'}\n",
+      "0\n",
+      "[('1808.08121v1', 'computer science', 'An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)', 'Mojtaba Heidarysafa, Kamran Kowsari, Donald E. Brown, Kiana Jafari Meimandi, Laura E. Barne', '2018-08-23', '2018-08-23', 'http://arxiv.org/pdf/1808.08121v1'), ('1904.08067v5', 'computer science', 'Text Classification Algorithms: A Survey', 'Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brow', '2020-05-20', '2019-04-17', 'http://arxiv.org/pdf/1904.08067v5'), ('2202.09144v1', 'computer science', 'Modelling the semantics of text in complex document layouts using graph transformer networks', 'Thomas Roland Barillot, Jacob Saks, Polena Lilyanova, Edward Torgas, Yachen Hu, Yuanqing Liu, Varun Balupuri, Paul Gaskel', '2022-02-18', '2022-02-18', 'http://arxiv.org/pdf/2202.09144v1')]\n",
+      "1\n",
+      "[('1601.04187v1', 'computer science', 'Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware', 'Peter U. Diehl, Guido Zarrella, Andrew Cassidy, Bruno U. Pedroni, Emre Neftc', '2016-01-16', '2016-01-16', 'http://arxiv.org/pdf/1601.04187v1'), ('1801.01093v3', 'economics', 'Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration', 'Angelica Gianfreda, Francesco Ravazzolo, Luca Rossin', '2019-11-12', '2018-01-03', 'http://arxiv.org/pdf/1801.01093v3'), ('2302.11093v1', 'electrical engineering and system science', 'Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification', 'Mehmet Parla', '2023-02-22', '2023-02-22', 'http://arxiv.org/pdf/2302.11093v1')]\n",
+      "2\n",
+      "[('1505.07907v4', 'economics', 'Linking Economic Complexity, Institutions and Income Inequality', 'D. Hartmann, M. R. Guevara, C. Jara-Figueroa, M. Aristaran, C. A. Hidalg', '2017-01-04', '2015-05-29', 'http://arxiv.org/pdf/1505.07907v4'), ('2107.06855v2', 'economics', 'Comparing Intellectual property policy in the Global North and South -- A one-size-fits-all policy for economic prosperity?', 'S Sidhartha Narayan, Malavika Ranjan, Madhumitha Raghurama', '2021-08-10', '2021-07-14', 'http://arxiv.org/pdf/2107.06855v2'), ('1910.11780v1', 'economics', 'Inequality in Turkey: Looking Beyond Growth', 'Bayram Cakir, Ipek Ergu', '2019-10-25', '2019-10-25', 'http://arxiv.org/pdf/1910.11780v1')]\n",
+      "3\n",
+      "[('1607.06583v2', 'computer science', \"Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks\", 'Saman Sarraf, Ghassem Tofigh', '2017-05-19', '2016-07-22', 'http://arxiv.org/pdf/1607.06583v2'), ('2101.10265v1', 'computer science', 'Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks', 'Gokhan Altan, Yakup Kutl', '2021-01-21', '2021-01-21', 'http://arxiv.org/pdf/2101.10265v1'), ('2208.03143v1', 'computer science', 'Deep Learning and Health Informatics for Smart Monitoring and Diagnosis', 'Amin Gasm', '2022-08-05', '2022-08-05', 'http://arxiv.org/pdf/2208.03143v1')]\n",
+      "4\n",
+      "[('2302.06584v3', 'computer science', 'Thermodynamic AI and the fluctuation frontier', 'Patrick J. Coles, Collin Szczepanski, Denis Melanson, Kaelan Donatella, Antonio J. Martinez, Faris Sbah', '2023-06-13', '2023-02-09', 'http://arxiv.org/pdf/2302.06584v3'), ('2307.12298v1', 'computer science', 'Stabilization and Dissipative Information Transfer of a Superconducting Kerr-Cat Qubit', 'Ufuk Korkmaz, Deniz Türkpenç', '2023-07-23', '2023-07-23', 'http://arxiv.org/pdf/2307.12298v1'), ('2106.10421v1', 'computer science', 'QFCNN: Quantum Fourier Convolutional Neural Network', 'Feihong Shen, Jun Li', '2021-06-19', '2021-06-19', 'http://arxiv.org/pdf/2106.10421v1')]\n",
+      "5\n",
+      "[('2308.16539v2', 'computer science', 'On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions', 'Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Torsten Bertra', '2023-10-16', '2023-08-31', 'http://arxiv.org/pdf/2308.16539v2'), ('2404.12474v1', 'computer science', 'Learning a Stable, Safe, Distributed Feedback Controller for a Heterogeneous Platoon of Vehicles', 'Michael H. Shaham, Taskin Padi', '2024-04-18', '2024-04-18', 'http://arxiv.org/pdf/2404.12474v1'), ('2008.13221v1', 'computer science', 'Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems', 'Vinicius G. Goeck', '2020-08-30', '2020-08-30', 'http://arxiv.org/pdf/2008.13221v1')]\n",
+      "6\n",
+      "[('1911.06206v3', 'economics', 'Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy', 'Niko Hauzenberger, Michael Pfarrhofe', '2020-09-10', '2019-11-14', 'http://arxiv.org/pdf/1911.06206v3'), ('2302.14114v1', 'economics', 'Econometric assessment of the monetary policy shocks in Morocco: Evidence from a Bayesian Factor-Augmented VAR', 'Marouane Daou', '2023-02-27', '2023-02-27', 'http://arxiv.org/pdf/2302.14114v1'), ('2311.11858v1', 'economics', 'Theory coherent shrinkage of Time-Varying Parameters in VARs', 'Andrea Renzett', '2023-11-20', '2023-11-20', 'http://arxiv.org/pdf/2311.11858v1')]\n",
+      "7\n",
+      "[('2310.03365v2', 'computer science', 'Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet', 'Hossein Jafari, Karim Faez, Hamidreza Amindava', '2023-10-14', '2023-10-05', 'http://arxiv.org/pdf/2310.03365v2'), ('1808.08531v1', 'computer science', 'DeepTracker: Visualizing the Training Process of Convolutional Neural Networks', 'Dongyu Liu, Weiwei Cui, Kai Jin, Yuxiao Guo, Huamin Q', '2018-08-26', '2018-08-26', 'http://arxiv.org/pdf/1808.08531v1'), ('2105.10448v1', 'computer science', 'Distinguishing artefacts: evaluating the saturation point of convolutional neural networks', 'Ric Real, James Gopsill, David Jones, Chris Snider, Ben Hick', '2021-05-21', '2021-05-21', 'http://arxiv.org/pdf/2105.10448v1')]\n",
+      "8\n",
+      "Got new records:  10\n",
+      "Re-queried on chromadb, results:  []\n",
+      "None\n",
+      "9\n",
+      "[('2403.07017v1', 'computer science', 'Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence', 'Long Wang, Feng Fu, Xingru Che', '2024-03-09', '2024-03-09', 'http://arxiv.org/pdf/2403.07017v1'), ('2210.02205v1', 'computer science', 'Game Theoretic Rating in N-player general-sum games with Equilibria', 'Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome Connor, Karl Tuyls, Thore Graepe', '2022-10-05', '2022-10-05', 'http://arxiv.org/pdf/2210.02205v1'), ('2212.05357v3', 'economics', 'On Blockchain We Cooperate: An Evolutionary Game Perspective', 'Luyao Zhang, Xinyu Tia', '2023-01-19', '2022-12-10', 'http://arxiv.org/pdf/2212.05357v3')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"test_questions.txt\",\"r\") as infile:\n",
+    "    data = json.load(infile)\n",
+    "print(data[0])\n",
+    "\n",
+    "test_log = []\n",
+    "for i,t in enumerate(data):\n",
+    "    print(i)\n",
+    "    temp_answer, answer = full_chain_single_question(t['question'])\n",
+    "    test_log.append({'desired topic':t['desired'],\n",
+    "           'question':t['question'],\n",
+    "           'first answer':temp_answer,\n",
+    "           'final answer':answer})\n",
+    "with open(\"test_results.json\",\"w\") as outfile:\n",
+    "    json.dump(test_log,outfile)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

chat/consumers.py

@@ -0,0 +1,18 @@
+import json
+from . import model_manage as md
+from channels.generic.websocket import WebsocketConsumer
+
+
+class ChatConsumer(WebsocketConsumer):
+    def connect(self):
+        self.accept()
+    def disconnect(self, close_code):
+        pass
+    def receive(self, text_data):
+        text_data_json = json.loads(text_data)
+        message = text_data_json["message"]
+        print(message)
+        record, messagee = md.full_chain_single_question(message)
+        print("First answer: ",record)
+        self.send(text_data=json.dumps({"message": messagee}))
+        

chat/migrations/0001_initial.py

@@ -0,0 +1,22 @@
+# Generated by Django 4.1 on 2024-05-04 14:53
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Member',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('first_name', models.CharField(max_length=24)),
+                ('last_name', models.CharField(max_length=24)),
+            ],
+        ),
+    ]

chat/model_manage.py

@@ -0,0 +1,169 @@
+# my_app/model_manager.py
+import google.generativeai as genai
+import chat.arxiv_bot.arxiv_bot_utils as utils
+import json
+
+model = None
+
+def create_model():
+    with open("apikey.txt","r") as apikey:
+        key = apikey.readline()
+        genai.configure(api_key=key)
+    for m in genai.list_models():
+        if 'generateContent' in m.supported_generation_methods:
+            print(m.name)
+    print("He was there")
+    config = genai.GenerationConfig(max_output_tokens=2048,
+                                temperature=0.7)
+    safety_settings = [
+        {
+            "category": "HARM_CATEGORY_DANGEROUS",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_HARASSMENT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_HATE_SPEECH",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+            "threshold": "BLOCK_NONE",
+        },
+    ]
+    global model
+    model = genai.GenerativeModel("gemini-pro",
+                                generation_config=config,
+                                safety_settings=safety_settings)
+    return model
+
+def get_model():
+    global model
+    if model is None:
+        # Khởi tạo model ở đây
+        model = create_model()  # Giả sử create_model là hàm tạo model của bạn
+    return model
+
+def extract_keyword_prompt(query):
+    """A prompt that return a JSON block as arguments for querying database"""
+
+    prompt = (
+        """[INST] SYSTEM: You are an assistant that choose only one action below based on guest question.
+   1. If the guest question is asking for a single specific document or article with explicit title, you need to respond the information in JSON format with 2 keys "title", "author" if found any above. The authors are separated with the word 'and'. 
+   2. If the guest question is asking for relevant informations about a topic, you need to respond the information in JSON format with 2 keys "keywords", "description", include a list of keywords represent the main academic topic, \
+     and a description about the main topic. You may paraphrase the keywords to add more. \
+   3. If the guest is not asking for any informations or documents, you need to respond with a polite answer in JSON format with 1 key "answer".
+   QUESTION: '{query}'
+   [/INST]
+   ANSWER: 
+   """
+    ).format(query=query)
+
+    return prompt
+
+def make_answer_prompt(input, contexts):
+    """A prompt that return the final answer, based on the queried context"""
+
+    prompt = (
+        """[INST] You are a library assistant that help to search articles and documents based on user's question.
+  From guest's question, you have found some records and documents that may help. Now you need to answer the guest with the information found.
+  If no information found in the database, you may generate some other recommendation related to user's question using your own knowledge. Each article or paper must have a link to the pdf download page.
+  You should answer in a conversational form politely.
+  QUESTION: '{input}'
+  INFORMATION: '{contexts}'
+  [/INST]
+  ANSWER:
+  """
+    ).format(input=input, contexts=contexts)
+
+    return prompt
+
+def response(args):
+    """Create response context, based on input arguments"""
+    keys = list(dict.keys(args))
+    if "answer" in keys:
+        return args['answer'], None  # trả lời trực tiếp
+     
+    if "keywords" in keys:
+        # perform query
+        query_texts = args["description"]
+        keywords = args["keywords"]
+        results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)
+        # print(results)
+        ids = results['metadatas'][0]
+        if len(ids) == 0:
+            # go crawl some
+            new_records = utils.crawl_arxiv(keyword_list=keywords, max_results=10)
+            print("Got new records: ",len(new_records))
+            if type(new_records) == str:
+                return "Error occured, information not found", new_records
+            utils.db.add(new_records)
+            utils.sqldb.add(new_records)
+            results = utils.db.query_relevant(keywords=keywords, query_texts=query_texts)
+            ids = results['metadatas'][0]
+            print("Re-queried on chromadb, results: ",ids)
+        paper_id = [id['paper_id'] for id in ids]
+        paper_info = utils.sqldb.query_id(paper_id)
+        print(paper_info)
+        records = [] # get title (2), author (3), link (6)
+        result_string = ""
+        if paper_info:
+            for i in range(len(paper_info)):
+                result_string += "Title: {}, Author: {}, Link: {}".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])
+                records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])
+                return result_string, records
+        else:
+            return "Information not found", "Information not found"
+        # invoke llm and return result
+
+    if "title" in keys:
+        title = args['title']
+        authors = utils.authors_str_to_list(args['author'])
+        paper_info = utils.sqldb.query(title = title,author = authors)
+        # if query not found then go crawl brh
+        # print(paper_info)
+
+        if len(paper_info) == 0:
+            new_records = utils.crawl_exact_paper(title=title,author=authors)
+            print("Got new records: ",len(new_records))
+            if type(new_records) == str:
+                # print(new_records)
+                return "Error occured, information not found", "Information not found"
+            utils.db.add(new_records)
+            utils.sqldb.add(new_records)
+            paper_info = utils.sqldb.query(title = title,author = authors)
+            print("Re-queried on chromadb, results: ",paper_info)
+        # -------------------------------------
+        records = [] # get title (2), author (3), link (6)
+        result_string = ""
+        for i in range(len(paper_info)):
+            result_string += "Title: {}, Author: {}, Link: {}".format(paper_info[i][2],paper_info[i][3],paper_info[i][6])
+            records.append([paper_info[i][2],paper_info[i][3],paper_info[i][6]])
+        # process results:
+        if len(result_string) == 0:
+            return "Information not found", "Information not found"
+        return result_string, records
+        # invoke llm and return result
+def full_chain_single_question(input_prompt):
+    try:
+        first_prompt = extract_keyword_prompt(input_prompt)
+        temp_answer = model.generate_content(first_prompt).text
+
+        args = json.loads(utils.trimming(temp_answer))
+        contexts, results = response(args)
+        if not results:
+            # print(contexts)
+            return "Random question, direct return", contexts
+        else:
+            output_prompt = make_answer_prompt(input_prompt,contexts)
+            answer = model.generate_content(output_prompt).text
+            return temp_answer, answer
+    except Exception as e:
+        # print(e)
+        return temp_answer, "Error occured: " + str(e)

chat/routing.py

@@ -0,0 +1,9 @@
+# chat/routing.py
+from django.urls import re_path
+
+from . import consumers
+
+websocket_urlpatterns = [
+    re_path(r"ws/chat/(?P<room_name>\w+)/$", consumers.ChatConsumer.as_asgi()),
+    re_path("ws/chat",consumers.ChatConsumer.as_asgi()),
+]

chat/templates/index.html

@@ -0,0 +1,229 @@
+<!doctype html>
+<html>
+
+<head>
+    <title>Python Project</title>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="https://cdn.tailwindcss.com"></script>
+</head>
+<script>
+    function textAreaAdjust(element) {
+        element.style.height = "1px";
+        element.style.height = (25+element.scrollHeight)+"px";
+    }
+</script>
+<body class="bg-[#2A2A2A] p-0 m-0 text-white">
+    <div id="app" class="md:container flex flex-col md:mx-auto mx-0 bg-[#2E2E2E] w-full md:w-[728px] h-screen">
+
+        <div class="flex flex-col h-full">
+            <!-- head -->
+            <div class="flex flex-row justify-between items-center p-4 select-none">
+                <h1 class="text-2xl font-medium text-center">
+                    TinyChat {{
+                    showConfig ? "Config" : ""
+                    }}
+                </h1>
+                <div>
+                    <svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"
+                        class="cursor-pointer" :class="{'text-white':!showConfig,'text-[#919191]':showConfig}"
+                        @click="showConfig=!showConfig">
+                        <path
+                            d="M11.6439 3C10.9352 3 10.2794 3.37508 9.92001 3.98596L9.49643 4.70605C8.96183 5.61487 7.98937 6.17632 6.935 6.18489L6.09966 6.19168C5.39095 6.19744 4.73822 6.57783 4.38385 7.19161L4.02775 7.80841C3.67338 8.42219 3.67031 9.17767 4.01968 9.7943L4.4315 10.5212C4.95126 11.4386 4.95126 12.5615 4.4315 13.4788L4.01968 14.2057C3.67031 14.8224 3.67338 15.5778 4.02775 16.1916L4.38385 16.8084C4.73822 17.4222 5.39095 17.8026 6.09965 17.8083L6.93501 17.8151C7.98938 17.8237 8.96184 18.3851 9.49644 19.294L9.92001 20.014C10.2794 20.6249 10.9352 21 11.6439 21H12.3561C13.0648 21 13.7206 20.6249 14.08 20.014L14.5035 19.294C15.0381 18.3851 16.0106 17.8237 17.065 17.8151L17.9004 17.8083C18.6091 17.8026 19.2618 17.4222 19.6162 16.8084L19.9723 16.1916C20.3267 15.5778 20.3298 14.8224 19.9804 14.2057L19.5686 13.4788C19.0488 12.5615 19.0488 11.4386 19.5686 10.5212L19.9804 9.7943C20.3298 9.17767 20.3267 8.42219 19.9723 7.80841L19.6162 7.19161C19.2618 6.57783 18.6091 6.19744 17.9004 6.19168L17.065 6.18489C16.0106 6.17632 15.0382 5.61487 14.5036 4.70605L14.08 3.98596C13.7206 3.37508 13.0648 3 12.3561 3H11.6439Z"
+                            stroke="white" stroke-width="2" stroke-linejoin="round" />
+                        <path
+                            d="M12 14.5C13.3807 14.5 14.5 13.3807 14.5 12C14.5 10.6193 13.3807 9.5 12 9.5C10.6193 9.5 9.5 10.6193 9.5 12C9.5 13.3807 10.6193 14.5 12 14.5Z"
+                            stroke="white" stroke-width="2" />
+                    </svg>
+
+                </div>
+            </div>
+            <!-- config -->
+            <div v-show="showConfig" class="flex flex-col gap-4 p-4 select-none">
+                <!-- render configFields -->
+                <div v-for="field in configFields" :key="field.name" class="flex flex-col gap-2">
+                    <label class="text-sm text-gray-400">
+                        {{ field.label }}
+                    </label>
+                    <input v-if="field.type=='text'" type="text"
+                        class="bg-transparent border border-[#919191] rounded-lg p-2" v-model="config[field.name]" />
+                    <input v-if="field.type=='number'" type="number"
+                        class="bg-transparent border border-[#919191] rounded-lg p-2" v-model="config[field.name]" />
+                </div>
+            </div>
+            <!-- messages -->
+            <div class="flex flex-col overflow-y-auto scroll-smooth" id="messages">
+                <div v-for="(message,index) in messages" :key="message.id"
+                    class="flex flex-col odd:bg-[#F7F7F7]/10 group">
+                    <div class="p-4 flex flex-col justify-between gap-4">
+                        <div class="flex flex-row gap-4 capitalize">
+                            <div class="text-gray-400 text-sm" >
+                                <textarea
+                                    class="text-gray-400 text-sm size-full outline-0 bg-transparent border-none text-white rounded-lg resize-none"
+                                    :rows="message.role" v-model="message.role"></textarea>
+                            </div>
+                            <div class="flex flex-col w-full">
+                                <textarea
+                                onkeyup="textAreaAdjust(this)" style="overflow:hidden"
+                                    class="size-full outline-0 bg-transparent border-none text-white rounded-lg resize-y"
+                                    :rows="message.content.split('\n').length" v-model="message.content"></textarea>
+                                <div class="mt-4" v-if="index==messages.length-1 && message.role=='assistant'">
+                                    <svg stroke="currentColor" fill="none" stroke-width="1.5" viewBox="0 0 24 24"
+                                        stroke-linecap="round" stroke-linejoin="round" height="1em" width="1em"
+                                        xmlns="http://www.w3.org/2000/svg" v-if="index==messages.length-1"
+                                        class="group-hover:block cursor-pointer h-3 w-3" @click="refresh"
+                                        :class="{'animate-spin':isLoading}">
+                                        >
+                                        >
+                                        >
+                                        <polyline points="1 4 1 10 7 10"></polyline>
+                                        <polyline points="23 20 23 14 17 14"></polyline>
+                                        <path d="M20.49 9A9 9 0 0 0 5.64 5.64L1 10m22 4l-4.64 4.36A9 9 0 0 1 3.51 15">
+                                        </path>
+
+                                    </svg>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- send message -->
+        <div class="flex flex-row justify-between items-center p-4 sticky bottom-0 bg-[#2E2E2E]" v-if="!showConfig">
+            <div class="w-full flex flex-row border border-1 border-[#919191] rounded-xl px-4 py-6 items-center">
+                <textarea
+                    class="w-full h-full outline-0 bg-transparent border-none text-white resize-none text-md max-h-[100px]"
+                    v-on:keyup.enter="sendMessage" placeholder="Type a message" :rows="message.split('\n').length"
+                    v-model="message"></textarea>
+                <div class="flex flex-col justify-end gap-4 text-[#2E2E2E] rounded-md p-1"
+                    :class="{'bg-white':message!='','bg-white/10':message==''}">
+                    <button @click="sendMessage">
+                        <svg class="w-6 h-6" fill="none" stroke="currentColor" viewBox="0 0 24 24"
+                            xmlns="http://www.w3.org/2000/svg">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2"
+                                d="M12 19l9 2-9-18-9 18 9-2zm0 0v-8" />
+                        </svg>
+                    </button>
+                </div>
+            </div>
+        </div>
+
+    </div>
+    <script type="module">
+        import { createApp } from 'https://unpkg.com/vue@3/dist/vue.esm-browser.js'
+        import { SSE } from "https://cdn.jsdelivr.net/npm/sse.js@2.1.0/lib/sse.min.js"
+
+        createApp({
+            data() {
+                return {
+                    showConfig: false,
+                    configFields: [
+                        {
+                            name: "endPoint",
+                            type: "text",
+                            label: "End Point"
+                        },
+                        {
+                            name: "apiKey",
+                            type: "text",
+                            label: "API Key"
+                        },
+                        {
+                            name: "model",
+                            type: "text",
+                            label: "Model"
+                        },
+                        {
+                            name: "temperature",
+                            type: "number",
+                            label: "Temperature"
+                        },
+                        {
+                            name: "presence_penalty",
+                            type: "number",
+                            label: "Presence Penalty"
+                        },
+                        {
+                            name: "top_p",
+                            type: "number",
+                            label: "Top P"
+                        },
+                        {
+                            name: "frequency_penalty",
+                            type: "number",
+                            label: "Frequency Penalty"
+                        },
+                    ],
+                    isLoading: false,
+                    message: '',
+                    messages: [],
+                    chatSocket : new WebSocket(
+                    'ws://'
+                    + window.location.host
+                    + '/ws/chat'
+                    ),
+                    config: {
+                        endPoint: "",
+                        apiKey: "",
+                        model: "gpt-3.5-turbo",
+                        temperature: 1,
+                        presence_penalty: 0,
+                        top_p: 1,
+                        frequency_penalty: 0,
+                        stream: true
+                    }
+                }
+
+            },
+            created() {
+                this.connect();
+            },
+            methods: {
+                refresh() {
+                    this.send(true);
+                },
+                sendMessage() {
+                    this.messages.push({
+                        role: "user",
+                        content: this.message
+                    });
+                    this.chatSocket.send(JSON.stringify({
+                        'message':this.message,
+                        'messages': this.messages,
+                    }));
+                    console.log(JSON.stringify({
+                        'messages': this.messages
+                    }))
+                    this.message = "";
+                },
+                connect() {
+                    this.chatSocket.onopen = () => {
+                        this.connectionStatus = 'Connected';
+                        console.log('WebSocket connected');
+                    };
+
+                    this.chatSocket.onmessage = (event) => {
+                        var mes = JSON.parse(event.data)
+                        this.messages.push({
+                            role: "Chat bot",
+                            content: mes.message
+                        });
+                        console.log('Message received: ', event.data);
+                    };
+
+                    this.chatSocket.onclose = () => {
+                        this.connectionStatus = 'Disconnected';
+                        console.log('WebSocket disconnected');
+                    };
+
+                    this.chatSocket.onerror = (error) => {
+                        console.error('WebSocket Error: ', error);
+                    };
+                }
+            },
+        }).mount('#app')
+    </script>
+</body>
+
+</html>

chat/templates/myfirst.html

@@ -0,0 +1,9 @@
+<!DOCTYPE html>
+<html>
+<body>
+
+<h1>Hello World!</h1>
+<p>Welcome {{my_name}} to my first Django project!</p>
+
+</body>
+</html>

chat/urls.py

@@ -0,0 +1,7 @@
+from django.urls import path
+from . import views
+
+urlpatterns = [
+    path('', views.index, name='index'),
+    path("<str:room_name>/", views.room, name="room"),
+]

chat/views.py

@@ -0,0 +1,10 @@
+# chat/views.py
+from django.shortcuts import render
+
+
+def index(request):
+    return render(request, "index.html")
+
+
+def room(request, room_name):
+    return render(request, "chat/room.html", {"room_name": room_name})

chatbot_django/asgi.py

@@ -0,0 +1,28 @@
+"""
+ASGI config for chatbot_django project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.0/howto/deployment/asgi/
+"""
+
+import os
+
+from channels.auth import AuthMiddlewareStack
+from channels.routing import ProtocolTypeRouter, URLRouter
+from channels.security.websocket import AllowedHostsOriginValidator
+from django.core.asgi import get_asgi_application
+from chat.routing import websocket_urlpatterns
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatbot_django.settings')
+
+django_asgi_app  = get_asgi_application()
+
+application = ProtocolTypeRouter({
+    "http": django_asgi_app,
+    # Just HTTP for now. (We can add other protocols later.)
+    "websocket": AllowedHostsOriginValidator(
+            AuthMiddlewareStack(URLRouter(websocket_urlpatterns))
+        ),
+})

chatbot_django/settings.py

@@ -0,0 +1,126 @@
+"""
+Django settings for chatbot_django project.
+
+Generated by 'django-admin startproject' using Django 5.0.4.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/5.0/ref/settings/
+"""
+
+from pathlib import Path
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/5.0/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = 'django-insecure-l39x6jgx075p9bw8^px-c=pys%&$bzdo_sz_oabf#5bv@^isj9'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = []
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'daphne',
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'chat'
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'chatbot_django.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'chatbot_django.wsgi.application'
+ASGI_APPLICATION = "chatbot_django.asgi.application"
+
+
+# Database
+# https://docs.djangoproject.com/en/5.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': BASE_DIR / 'db.sqlite3',
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/5.0/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/5.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/5.0/howto/static-files/
+
+STATIC_URL = 'static/'
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/5.0/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

chatbot_django/urls.py

@@ -0,0 +1,23 @@
+"""
+URL configuration for chatbot_django project.
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/5.0/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path,include
+
+urlpatterns = [
+    path('chat/',include('chat.urls')),
+    path('admin/', admin.site.urls),
+]

chatbot_django/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for chatbot_django project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatbot_django.settings')
+
+application = get_wsgi_application()

manage.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatbot_django.settings')
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == '__main__':
+    main()

oldindex.html

@@ -0,0 +1,259 @@
+<!doctype html>
+<html>
+
+<head>
+    <title>Python Project</title>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="https://cdn.tailwindcss.com"></script>
+</head>
+
+<body class="bg-[#2A2A2A] p-0 m-0 text-white">
+    <div id="app" class="md:container flex flex-col md:mx-auto mx-0 bg-[#2E2E2E] w-full md:w-[728px] h-screen">
+
+        <div class="flex flex-col h-full">
+            <!-- head -->
+            <div class="flex flex-row justify-between items-center p-4 select-none">
+                <h1 class="text-2xl font-medium text-center">
+                    TinyChat {{
+                    showConfig ? "Config" : ""
+                    }}
+                </h1>
+                <div>
+                    <svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"
+                        class="cursor-pointer" :class="{'text-white':!showConfig,'text-[#919191]':showConfig}"
+                        @click="showConfig=!showConfig">
+                        <path
+                            d="M11.6439 3C10.9352 3 10.2794 3.37508 9.92001 3.98596L9.49643 4.70605C8.96183 5.61487 7.98937 6.17632 6.935 6.18489L6.09966 6.19168C5.39095 6.19744 4.73822 6.57783 4.38385 7.19161L4.02775 7.80841C3.67338 8.42219 3.67031 9.17767 4.01968 9.7943L4.4315 10.5212C4.95126 11.4386 4.95126 12.5615 4.4315 13.4788L4.01968 14.2057C3.67031 14.8224 3.67338 15.5778 4.02775 16.1916L4.38385 16.8084C4.73822 17.4222 5.39095 17.8026 6.09965 17.8083L6.93501 17.8151C7.98938 17.8237 8.96184 18.3851 9.49644 19.294L9.92001 20.014C10.2794 20.6249 10.9352 21 11.6439 21H12.3561C13.0648 21 13.7206 20.6249 14.08 20.014L14.5035 19.294C15.0381 18.3851 16.0106 17.8237 17.065 17.8151L17.9004 17.8083C18.6091 17.8026 19.2618 17.4222 19.6162 16.8084L19.9723 16.1916C20.3267 15.5778 20.3298 14.8224 19.9804 14.2057L19.5686 13.4788C19.0488 12.5615 19.0488 11.4386 19.5686 10.5212L19.9804 9.7943C20.3298 9.17767 20.3267 8.42219 19.9723 7.80841L19.6162 7.19161C19.2618 6.57783 18.6091 6.19744 17.9004 6.19168L17.065 6.18489C16.0106 6.17632 15.0382 5.61487 14.5036 4.70605L14.08 3.98596C13.7206 3.37508 13.0648 3 12.3561 3H11.6439Z"
+                            stroke="white" stroke-width="2" stroke-linejoin="round" />
+                        <path
+                            d="M12 14.5C13.3807 14.5 14.5 13.3807 14.5 12C14.5 10.6193 13.3807 9.5 12 9.5C10.6193 9.5 9.5 10.6193 9.5 12C9.5 13.3807 10.6193 14.5 12 14.5Z"
+                            stroke="white" stroke-width="2" />
+                    </svg>
+
+                </div>
+            </div>
+            <!-- config -->
+            <div v-show="showConfig" class="flex flex-col gap-4 p-4 select-none">
+                <!-- render configFields -->
+                <div v-for="field in configFields" :key="field.name" class="flex flex-col gap-2">
+                    <label class="text-sm text-gray-400">
+                        {{ field.label }}
+                    </label>
+                    <input v-if="field.type=='text'" type="text"
+                        class="bg-transparent border border-[#919191] rounded-lg p-2" v-model="config[field.name]" />
+                    <input v-if="field.type=='number'" type="number"
+                        class="bg-transparent border border-[#919191] rounded-lg p-2" v-model="config[field.name]" />
+                </div>
+            </div>
+            <!-- messages -->
+            <div class="flex flex-col overflow-y-auto scroll-smooth" id="messages">
+                <div v-for="(message,index) in messages" :key="message.id"
+                    class="flex flex-col odd:bg-[#F7F7F7]/10 group">
+                    <div class="p-4 flex flex-col justify-between gap-4">
+                        <div class="flex flex-row gap-4 capitalize">
+                            <div class="text-gray-400 text-sm" >
+                                <textarea
+                                    class="text-gray-400 text-sm size-full outline-0 bg-transparent border-none text-white rounded-lg resize-none"
+                                    :rows="message.role" v-model="message.role"></textarea>
+                            </div>
+                            <div class="flex flex-col w-full">
+                                <textarea
+                                    class="size-full outline-0 bg-transparent border-none text-white rounded-lg resize-none"
+                                    :rows="message.content.split('\n').length" v-model="message.content"></textarea>
+                                <div class="mt-4" v-if="index==messages.length-1 && message.role=='assistant'">
+                                    <svg stroke="currentColor" fill="none" stroke-width="1.5" viewBox="0 0 24 24"
+                                        stroke-linecap="round" stroke-linejoin="round" height="1em" width="1em"
+                                        xmlns="http://www.w3.org/2000/svg" v-if="index==messages.length-1"
+                                        class="group-hover:block cursor-pointer h-3 w-3" @click="refresh"
+                                        :class="{'animate-spin':isLoading}">
+                                        >
+                                        >
+                                        >
+                                        <polyline points="1 4 1 10 7 10"></polyline>
+                                        <polyline points="23 20 23 14 17 14"></polyline>
+                                        <path d="M20.49 9A9 9 0 0 0 5.64 5.64L1 10m22 4l-4.64 4.36A9 9 0 0 1 3.51 15">
+                                        </path>
+
+                                    </svg>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <!-- send message -->
+        <div class="flex flex-row justify-between items-center p-4 sticky bottom-0 bg-[#2E2E2E]" v-if="!showConfig">
+            <div class="w-full flex flex-row border border-1 border-[#919191] rounded-xl px-4 py-6 items-center">
+                <textarea
+                    class="w-full h-full outline-0 bg-transparent border-none text-white resize-none text-md max-h-[100px]"
+                    v-on:keyup.enter="sendMessage" placeholder="Type a message" :rows="message.split('\n').length"
+                    v-model="message"></textarea>
+                <div class="flex flex-col justify-end gap-4 text-[#2E2E2E] rounded-md p-1"
+                    :class="{'bg-white':message!='','bg-white/10':message==''}">
+                    <button @click="sendMessage">
+                        <svg class="w-6 h-6" fill="none" stroke="currentColor" viewBox="0 0 24 24"
+                            xmlns="http://www.w3.org/2000/svg">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2"
+                                d="M12 19l9 2-9-18-9 18 9-2zm0 0v-8" />
+                        </svg>
+                    </button>
+                </div>
+            </div>
+        </div>
+
+    </div>
+    <script type="module">
+        import { createApp } from 'https://unpkg.com/vue@3/dist/vue.esm-browser.js'
+        import { SSE } from "https://cdn.jsdelivr.net/npm/sse.js@2.1.0/lib/sse.min.js"
+
+        createApp({
+            data() {
+                return {
+                    showConfig: false,
+                    configFields: [
+                        {
+                            name: "endPoint",
+                            type: "text",
+                            label: "End Point"
+                        },
+                        {
+                            name: "apiKey",
+                            type: "text",
+                            label: "API Key"
+                        },
+                        {
+                            name: "model",
+                            type: "text",
+                            label: "Model"
+                        },
+                        {
+                            name: "temperature",
+                            type: "number",
+                            label: "Temperature"
+                        },
+                        {
+                            name: "presence_penalty",
+                            type: "number",
+                            label: "Presence Penalty"
+                        },
+                        {
+                            name: "top_p",
+                            type: "number",
+                            label: "Top P"
+                        },
+                        {
+                            name: "frequency_penalty",
+                            type: "number",
+                            label: "Frequency Penalty"
+                        },
+                    ],
+                    isLoading: false,
+                    message: '',
+                    messages: [],
+                    chatSocket : new WebSocket(
+                    'ws://'
+                    + window.location.host
+                    + '/chat'
+                ),
+                    config: {
+                        endPoint: "",
+                        apiKey: "",
+                        model: "gpt-3.5-turbo",
+                        temperature: 1,
+                        presence_penalty: 0,
+                        top_p: 1,
+                        frequency_penalty: 0,
+                        stream: true
+                    }
+                }
+
+            },
+            methods: {
+                refresh() {
+                    this.send(true);
+                },
+                sendMessage() {
+                    this.messages.push({
+                        role: "user",
+                        content: this.message
+                    });
+                    // this.send();
+                    this.chatSocket.send(JSON.stringify({
+                        'message': this.message
+                    }));
+                    this.message = "";
+                },
+                send(refresh = false) {
+                    this.isLoading = true;
+                    // let data = {
+                    //     "messages": this.messages,
+                    //     ...this.config
+                    // }
+
+                    // if (refresh) {
+                    //     this.messages.pop();
+                    //     this.messages.push({
+                    //         role: "assistant",
+                    //         content: ""
+                    //     });
+                    // }
+                    // let addedMsg = false;
+                    // var source = new SSE(this.config.endPoint, {
+                    //     headers: {
+                    //         "accept": "*/*",
+                    //         'content-type': 'application/json',
+                    //         authorization: 'Bearer ' + this.config.apiKey,
+                    //     },
+                    //     payload: JSON.stringify(data),
+                    //     method: 'POST'
+                    // });
+                    // source.addEventListener('message', (e) => {
+                    //     if (!addedMsg && !refresh) {
+                    //         this.messages.push({
+                    //             role: "assistant",
+                    //             content: ""
+                    //         });
+                    //         addedMsg = true;
+                    //     }
+                    //     try {
+                    //         var payload = JSON.parse(e.data);
+                    //         if (payload.choices[0].finish_reason == "stop") {
+                    //             this.isLoading = false;
+                    //             return;
+                    //         }
+                    //         const message = payload.choices[0].delta.content;
+                    //         if (message) {
+                    //             this.messages[this.messages.length - 1].content += message;
+                    //         }
+                    //     } catch (e) {
+                    //     }
+                    // });
+                    // source.addEventListener('error', (error) => {
+                    //     console.error('Error:', error);
+                    //     source.close();
+                    //     this.isLoading = false;
+                    // });
+                }
+            },
+            // watch: {
+            //     config: {
+            //         deep: true,
+            //         handler(newConfig) {
+            //             if (newConfig) {
+            //                 localStorage.setItem("config", JSON.stringify(newConfig));
+            //             }
+            //         }
+            //     }
+            // },
+            // mounted() {
+            //     if (localStorage.getItem("config")) {
+            //         this.config = JSON.parse(localStorage.getItem("config"));
+            //     }
+            // },
+        }).mount('#app')
+    </script>
+</body>
+
+</html>

topic_descriptions.txt

@@ -0,0 +1,6 @@
+{
+    "computer science":"Computer Science: The Language of Machines. Focus: This field deals with the theory, design, development, and application of software and hardware systems. It delves into the creation of algorithms, data structures, and programming languages that enable computers to process information and solve problems. Core Concepts: Algorithms: Step-by-step instructions for computers to solve problems. Data Structures: Organized ways to store and access data efficiently. Programming Languages: Tools to communicate instructions to computers. Artificial Intelligence: Machines mimicking human-like intelligence. Machine Learning: Algorithms that learn from data without explicit programming. Applications: From designing websites and mobile apps to developing complex operating systems and artificial intelligence, computer science underpins the digital world we interact with daily.",
+    "economics":"Economics: The Science of Choice and Exchange. Focus: Economics examines how individuals, societies, and governments make choices in a world with scarce resources. It analyzes production, distribution, and consumption of goods and services, studying factors like market behavior, prices, and money. Core Concepts: Scarcity: Limited resources versus unlimited wants and needs. Supply and Demand: Forces that determine market prices. Rational Choice Theory: Individuals making choices based on their preferences and constraints. Macroeconomics: Economy-wide phenomena like inflation, unemployment, and economic growth. Microeconomics: Individual and firm behavior in markets. Applications: Economic principles are used to understand market trends, formulate effective government policies, design financial systems, and make informed investment decisions.",
+    "electrical engineering and system science":"Electrical Engineering & System Science: Bridging Power and Complexity. Focus: This interdisciplinary field combines electrical engineering, which deals with electricity, electronics, and electromagnetism, with system science, which focuses on analyzing and designing complex systems. It involves designing and developing electrical circuits, devices, and systems for power generation, transmission, control, and communication. Core Concepts: Circuits: Networks of electrical components like resistors, capacitors, and transistors. Electromagnetism: Interaction of electric and magnetic fields. Power Systems: Networks for generating, transmitting, and distributing electricity. Control Systems: Systems that regulate the behavior of other systems. Signals and Systems: Analysis of how information is represented and processed in electrical systems. Applications: Electrical engineers and system scientists create a vast array of technologies, from power grids and communication systems to medical devices and embedded systems in everyday gadgets.",
+    "mathematics":"Mathematics: The Universal Language of Science. Focus: Mathematics is the abstract study of numbers, quantity, relationships, structures, and patterns. It provides a foundation for many other disciplines by offering a language and tools to express ideas, model problems, and derive solutions. Core Concepts: Calculus: Branch of mathematics dealing with rates of change and accumulation. Algebra: Studying relationships between variables using symbols and operations. Geometry: Properties of shapes and spaces. Probability and Statistics: Analyzing randomness and drawing meaningful conclusions from data. Logic: Reasoning and making valid deductions from given statements. Applications: Mathematics is an indispensable tool in all scientific fields, from physics and engineering to economics and computer science. It is also used in finance, cryptography, and game theory."
+}