This is a Streamlit application that uses a pre-trained deep learning model to classify objects in images. It provides an interactive experience where users can take a picture, get predictions, fetch related YouTube videos, and answer questions based on the predicted object.
https://drive.google.com/drive/folders/1lAfbLjJ1F8Rib-afytW_AsBhdc4mNl_c?usp=sharing.
Make sure you have the following installed:
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Clone the repository:
git clone https://github.com/your-username/your-repository.git cd your-repository
Install the required dependencies:
bash Copy code pip install -r requirements.txt Usage Running the App Execute the following command to run the Streamlit app:
streamlit run app.pyThis will launch the app in your default web browser. Follow the instructions on the webpage to interact with the application.
Application Flow Take a Picture: Click the "Take a picture" button to capture an image using your device's camera.
Object Prediction: The app will use a pre-trained model to predict the class of the object in the image.
YouTube Video Fetching: Click "Fetch Youtube Video" to find related YouTube videos based on the predicted object.
Answer Questions: Click "Fetch Questions" to answer a set of predefined questions related to the predicted object.
Folder Structure dataset/: Contains training and testing datasets. models/: Store your pre-trained model file here. app.py: Main application script. requirements.txt: List of Python dependencies. Customization Model: You can replace the pre-trained model file (object_classifier.h5) in the models/ directory with your own model.
Dataset: Customize the train_data_dir and valid_data_dir paths in app.py according to your dataset structure.
Questions: Modify the sets of questions (qs1 and qs2) in app.py to suit your needs.
Contributing Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull 4CD9 request.
License This project is licensed under the MIT License.