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| Publish by | Anu selvam |
| Project Name | Online examination system |
| Upload Date | May 6, 2022 |
| Platform | Python |
| Programming Language | python, flask |
| Database | any database related to python |
| Front end | |
| Back end | |
| Project Type | web Application |
| View | 5057 |
The online examination system must have all the features like MCQ-based questions, subjective question and answering boxes, a timer for each section, finishing of the exam after the stipulated time ends and the answer must be saved automatically after the exam end. The access must be in two types. superuser access where the professor can edit & view the responses of the student and the second type of access is by the students where students can take the exam by using email, roll no, and name. Each student can take only one attempt and the use of the same email and roll no won't give access to the exam. after the exam finishes, the student can view their scores automatically.
Query any SQLite database using plain English with no SQL knowledge required. The agent automatically discovers the schema, translates plain English questions into SQL queries, executes them and returns human readable answers. Includes auto repair for broken queries. Features include automatic schema discovery, natural language to SQL conversion ... [ Download Source Code ]
A document summarization pipeline capable of handling texts of any length using map-reduce strategy. Long documents are split into chunks, each chunk is summarized independently then all summaries are combined into a final output. Works on books, reports and transcripts. Features include three summary modes Brief, Detailed and Bullet Points, map ... [ Download Source Code ]
An autonomous AI agent that searches the live web in real time to answer any question. Uses the ReAct framework where the agent thinks about what to search, performs the search, observes the results and repeats until confident answer is found. Features include live web search via Tavily API, multi-step ReAct reasoning loop, transparent step by s ... [ Download Source Code ]
Upload any PDF and ask natural language questions about it using Retrieval-Augmented Generation. The program splits the PDF into chunks, embeds them into a FAISS vector store, and retrieves the most relevant sections to answer each question accurately. Key features include PDF ingestion and smart chunking, semantic search via FAISS vector store, ... [ Download Source Code ]
A conversational chatbot built with LangChain and GPT-4o that remembers the full session history. Users type any question and receive intelligent context-aware replies. The bot uses ConversationBufferMemory to retain all previous messages. Features include persistent conversation memory, GPT-4o integration, custom system prompt, clean CLI interf ... [ Download Source Code ]