Update README.md

README.md

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 ---
 pipeline_tag: image-classification
----
+---
+## Location Classification of Indian Cities
+
+This Streamlit app is designed to detect the location of an Indian city in an uploaded image. It uses a deep learning model trained on 10,500 images classified into 5 classes of cities including Ahmedabad, Delhi, Kerala, Kolkata, and Mumbai. The model was trained in association with Parul University and currently has a test accuracy of 66.3%.
+
+## How to Use the App
+1. Clone the GitHub repository:
+   ```
+   git clone https://github.com/shahdivax/Location-Classification-of-Indian-Cities.git --branch master
+   ```
+2. Install the required libraries:
+   ```
+   pip install -r requirements.txt
+   ```
+3. Run the app:
+   ```
+   streamlit run app.py
+   ```
+   ![image](https://github.com/shahdivax/Location-Classification-of-Indian-Cities/blob/master/github_data/streamlitimg.png)
+   For Flask app:<br>
+   change Directory
+   ```
+   cd Flask
+   ```
+   run app:
+   ```
+   flask run
+   ```
+   #### Flask Demo:
+   
+
+https://github.com/shahdivax/Location-Classification-of-Indian-Cities/assets/61962983/d29652ab-2e07-4b81-bd6d-c4e53c5f3891
+
+
+   <br>
+4. Upload an image in JPG or JPEG format.<br>
+5. The app will display the uploaded image and predict the location of the city in the image.<br>
+6. The predicted location and accuracy percentage will be displayed.
+
+Please note that the app may not work accurately for images that are not clear or do not have a distinct view of the city's landmarks.
+
+## Live Demo
+A live demo of the app is available [here](https://location-classification-of-indian-cities.streamlit.app/) hosted with Streamlit.
+
+## Code
+The code for this app was written in Python. It uses the following libraries:
+* Streamlit: To build the app user interface
+* TensorFlow and Keras: To load the pre-trained model and process images
+* Numpy and Random: For data processing and random color selection
+
+The application flow follows the steps below:
+1. Load the trained deep learning model.
+2. Define the class labels for the 5 Indian cities.
+3. Set a minimum accuracy threshold for predictions.
+4. Create a function to process uploaded images.
+5. Create a Streamlit app interface with a file uploader.
+6. Process uploaded images and display the predicted location and accuracy.
+
+## Future Work
+This app can be improved by increasing the size of the training dataset and fine-tuning the pre-trained model to increase its accuracy. Additionally, the app can be trained to recognize city landmarks to improve its performance.