Chatbot using Flutter & Dart

TensorFlow General Discussion
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Hi,
I am an android and iOS developer and I want to make a chatbot using tensor flow with the help of Flutter & Dart. The chatbot is able to answer of the user input questions. Basically I want a FAQ Chatbot. I want to train my bot with some constant faq question answers by which it can detect and able to answer. I found nothing over internet about my mentioned query.

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You can check:

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We have developed a similar chatbot couple of years back. I used SciPy and BERT mostly along with TensorFlow. Also since the bot just needs to answer FAQs it is quite easy. I remember the client that worked for required NLP and sentiment analysis incorporated into the bot, which made it more difficult to create. We had to implement SCIKT and NLTK as well for the development, which complicated the process. But you can find more in-depth details of our Removed by moderator page and assistance to develop a FAQ bot also can be provided since such bots are simple to create and easier to understand.

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Hey, i am working on the same project and after alot of research this is the only thing i found, Can you please help me with this or provide some guidelines for FAQ bot.

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Creating a FAQ chatbot using TensorFlow, Flutter, and Dart is definitely achievable! Here’s a general roadmap to get you started:

  1. Data Collection: Gather a dataset of FAQ questions and answers that you want your chatbot to recognize and respond to. You can compile this dataset manually or look for existing FAQ datasets online.

  2. Preprocessing: Preprocess the dataset to prepare it for training. This may involve tokenization, lowercasing, removing punctuation, and other text normalization techniques.

  3. Model Selection: Choose a suitable TensorFlow model architecture for your chatbot. This could be a sequence-to-sequence model, a transformer model like BERT, or a simpler architecture depending on the complexity of your FAQ dataset.

  4. Training: Train your TensorFlow model using the preprocessed FAQ dataset. Fine-tune the model’s parameters to improve performance.

  5. Integration with Flutter: Once you have a trained model, integrate it into your Flutter app using TensorFlow Lite or TensorFlow.js for inference on mobile devices. This allows your chatbot to run directly on Android and iOS platforms.

  6. User Interface: Design a user interface for your chatbot within your Flutter app. This could include text input fields for users to ask questions and a chat interface to display the bot’s responses.

  7. Testing and Evaluation: Test your chatbot extensively to ensure it accurately recognizes and responds to FAQ questions. Gather feedback from users and iterate on your model and user interface design as needed.