AI Chatbot Platform

A minimal Chatbot Platform built with FastAPI (backend) and HTML/CSS/JS (frontend).
It supports authentication, project/agent creation, document uploads, and context-aware conversations with an LLM using the OpenAI Responses API.

---

Live Demo

You can try the hosted version here:
https://chatbot-4-jvnz.onrender.com/

---

Features

Authentication

• Register and log in with email and password
• JWT-based authentication
• Secure password hashing with bcrypt

Projects / Agents

• Each user can create multiple projects
• Projects are private to each user
• Projects store uploaded files and chat history

Document Upload

• Supports .pdf, .docx, and .txt files
• Extracts text and uses it as context during chats
• Optional support for uploading to the OpenAI Files API

Chat

• Chat sessions are scoped to a project
• Uploaded documents are used as context when generating answers
• Integrated with the OpenAI Responses API (extensible to other LLM services)

Frontend

• Simple login/registration form (email + password)
• Project creation and selection
• File upload and chat box with conversation history

---

Project Structure

WEB_CHATBOT/
 ├── backend/
 │    ├── main.py          # FastAPI application
 │    ├── index.html       # Frontend UI
 │    ├── requirements.txt # Dependencies
 │    ├── .env.example     # Example environment variables
 │    └── uploads/         # Temporary file storage (ignored in git)
 ├── README.md             # Project documentation
 └── .gitignore            # Ignore venv, .env, cache files, etc.

---

Setup and Installation

Prerequisites

• Python 3.9+
• An OpenAI API key (or compatible provider)
• Git

Installation Steps

1. Clone the repository:

   git clone https://github.com/YOUR-USERNAME/web-chatbot.git
   cd web-chatbot/backend

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Copy .env.example to .env and set your secrets:

   OPENAI_API_KEY=your_openai_api_key
   OPENAI_MODEL=gpt-4o-mini
   SECRET_KEY=your_secret_key
   

5. Run the application:

   uvicorn main:app --reload

6. Open the app in your browser:

   http://127.0.0.1:8000/
   

---

Deployment

This project is deployed on Render.
To deploy your own version:

1. Push your code to GitHub
2. Create a new Web Service on Render
3. Connect your GitHub repo
4. Set build command:

   pip install -r backend/requirements.txt

5. Set start command:

   uvicorn backend.main:app --host 0.0.0.0 --port 10000

6. Add environment variables in the Render dashboard (OPENAI_API_KEY, OPENAI_MODEL, SECRET_KEY)

Your app will be live at a Render-provided URL.

---