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The bot can respond to user queries and facilitate content searches using the information stored in specific directories.\n\n---\n\n## Steps and Guidelines to Make It Work\n\n### Server Setup\n\n1. **Install Python**:\n   - Ensure Python 3.7, 3.8, 3.9, or 3.10 is installed. Recommended version: 3.9.13.\n   - Download Python from the [official site](https://www.python.org/downloads/).\n\n2. **Install RASA Open Source**:\n   - Run the following command to install RASA using pip:\n     ```bash\n     pip install rasa\n     ```\n   - Verify the installation:\n     ```bash\n     rasa --version\n     ```\n\n3. **Create a New RASA Project**:\n   - Run the following command to initialize a new RASA project:\n     ```bash\n     rasa init --no-prompt\n     ```\n   - Navigate to your RASA project directory.\n\n4. **Train the RASA Model**:\n   - To train your RASA model, use:\n     ```bash\n     rasa train\n     ```\n\n5. **Test and Run the RASA Assistant**:\n   - Run the bot in the terminal using:\n     ```bash\n     rasa shell\n     ```\n   - Or, run the server with API support using:\n     ```bash\n     rasa run --enable-api\n     ```\n\n6. **Training Data**:\n   - The model can be trained using files located within your project directory such as `nlu.md`, `domain.yml`, `stories.md`, etc.\n\n7. **Install spaCy for NLU**:\n   - Install the `spaCy` library using pip:\n     ```bash\n     pip install spacy\n     ```\n\n8. **Install Other Required Packages**:\n   - Install any additional dependencies using pip as required by your project.\n\n---\n\n### Client Setup (Android App)\n\n1. **Install the Application**:\n   - Download and install the Android application on the device.\n\n2. **Grant Permissions**:\n   - Ensure the app has the required permissions to access the device's storage.\n\n3. **Network Security Configuration**:\n   - The server system must be configured to allow cleartext traffic using the server's IP address. This is crucial for enabling communication between the Android app and the server over HTTP.\n\n---\n\n### Guidelines\n\n- Both the client (Android app) and server (RASA bot) should be able to communicate over the network.\n- The bot can provide services regardless of how much it has been trained.\n- All files needed for content search must be placed in a folder named `Rasabot` in your device's internal storage.\n\n---\n\n### Working Process\n\n1. **User Interaction**:\n   - The user communicates with the AI bot (RASA) through the Android app for queries.\n\n2. **Content Search**:\n   - The bot provides instructions for content search, and the user's request is sent via an HTTP call to the server.\n   - On the server side, the bot processes the request and sends the appropriate response back through the same communication path.\n\n---\n\n## Tools Used\n\n### Client-Side\n\n- **Android Studio**\n- **Java**\n- **ITextpdf Library** (for text extraction)\n\n### Server-Side\n\n- **Python**\n- **RASA Framework**\n- **spaCy Library** (for NLU)\n\n---\n\n## Contact\n\nFor further information, feel free to contact.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs-gani%2Fadvanced_dms_bot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fs-gani%2Fadvanced_dms_bot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs-gani%2Fadvanced_dms_bot/lists"}